🚀 NL2SQL Data Analysis System Powered by LangChain

Make data analysis as easy as chatting

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⚡ Project Overview

This project is an NL2SQL data analysis system built on LangChain 1.0, using FastAPI + React.

You can use natural language to query, aggregate, and visualize data from a database or uploaded CSV/Excel files. No SQL needed — charts and results are generated automatically.

🎯 Key Features

🔍 Data Processing & Ingestion

• Batch upload .csv / .excel files, automatically parse and ingest into a temporary database, support data retrieval, visualization, and detailed analytical report generation based on the file contents.
• Automatically extract headers, estimate row counts, and other basic metadata to support quick exploration.

🤖 Natural Language to SQL (NL2SQL)

• Integrates LangChain SQL Agent to automatically convert natural language into SQL queries.
• Supports connecting to local/cloud databases, selecting tables, and asking questions in natural language.

📈 Data Visualization

• Choose single-table or multi-table automatic analysis; the system generates SQL queries in real time and executes them, then builds visualizations based on the query results.

🎥 Demo

redpandacompress_nl2sql.mp4

📁 Project Structure

├── backend/                 # Backend (FastAPI + LangChain)
│   ├── app/                 # Core app (routes, Agent, visualization)
│   ├── data/                # Data directory (uploads & visualization outputs)
│   ├── requirements.txt     # Python dependencies
│   ├── api_with_db.py       # Launch script (with temporary DB)
│   └── .env                 # Environment variables (recommended here)
├── frontend/                # Frontend (React + Vite)
│   ├── src/                 # Pages & components
│   └── package.json         # Node project configuration
└── README.md

🚀 Quick Start

1. Configure Environment Variables (.env file)

Create and fill backend/.env with:

OPENAI_API_KEY=sk-your-api-key
OPENAI_BASE_URL=https://api.openai.com/v1
DEFAULT_MODEL=gpt-4o-mini

2. Start the Backend Service

# 1. Enter the backend directory
cd backend

# 2. Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate     # Windows

# 3. Install dependencies
pip install -r requirements.txt

# 4. Start the service
python api_with_db.py

3. Start the Frontend Service

# 1. Enter the frontend directory
cd frontend

# 2. Install dependencies
npm install

# 3. Start the dev server
npm run dev

🙈 Contributing

We welcome contributions via PRs and issues on GitHub. Contributions of all kinds are appreciated — feature improvements, bug fixes, and documentation enhancements.

😎 Community

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