From 9020a934be35b0798c972eb77a22fb62ce654ca5 Mon Sep 17 00:00:00 2001 From: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> Date: Fri, 24 Jan 2025 06:56:26 -0600 Subject: [PATCH 01/12] docs: add Azure RAG example (#675) Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Co-authored-by: Farzad Sunavala --- docs/examples/rag_azuresearch.ipynb | 893 ++++++++++++++++++++++++++++ mkdocs.yml | 10 +- 2 files changed, 899 insertions(+), 4 deletions(-) create mode 100644 docs/examples/rag_azuresearch.ipynb diff --git a/docs/examples/rag_azuresearch.ipynb b/docs/examples/rag_azuresearch.ipynb new file mode 100644 index 00000000..abbe3774 --- /dev/null +++ b/docs/examples/rag_azuresearch.ipynb @@ -0,0 +1,893 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Ag9kcX2B_atc" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RAG with Azure AI Search" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "| Step | Tech | Execution |\n", + "| ------------------ | ------------------ | --------- |\n", + "| Embedding | Azure OpenAI | ๐ŸŒ Remote |\n", + "| Vector Store | Azure AI Search | ๐ŸŒ Remote |\n", + "| Gen AI | Azure OpenAI | ๐ŸŒ Remote |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "This notebook demonstrates how to build a Retrieval-Augmented Generation (RAG) system using:\n", + "- [Docling](https://ds4sd.github.io/docling/) for document parsing and chunking\n", + "- [Azure AI Search](https://azure.microsoft.com/products/ai-services/ai-search/?msockid=0109678bea39665431e37323ebff6723) for vector indexing and retrieval\n", + "- [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service?msockid=0109678bea39665431e37323ebff6723) for embeddings and chat completion\n", + "\n", + "This sample demonstrates how to:\n", + "1. Parse a PDF with Docling.\n", + "2. Chunk the parsed text.\n", + "3. Use Azure OpenAI for embeddings.\n", + "4. Index and search in Azure AI Search.\n", + "5. Run a retrieval-augmented generation (RAG) query with Azure OpenAI GPT-4o.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# If running in a fresh environment (like Google Colab), uncomment and run this single command:\n", + "%pip install \"docling~=2.12\" azure-search-documents==11.5.2 azure-identity openai rich torch python-dotenv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part 0: Prerequisites\n", + " - **Azure AI Search** resource\n", + " - **Azure OpenAI** resource with a deployed embedding & chat completion model (e.g. `text-embedding-3-small` & `gpt-4o`) \n", + " - **Docling 2.12+** (installs `docling_core` automatically) Docling installed (Python 3.8+ environment)\n", + "\n", + "- A **GPU-enabled environment** is preferred for faster parsing. Docling 2.12 automatically detects GPU if present.\n", + " - If you only have CPU, parsing large PDFs can be slower. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from dotenv import load_dotenv\n", + "\n", + "load_dotenv()\n", + "\n", + "\n", + "def _get_env(key, default=None):\n", + " try:\n", + " from google.colab import userdata\n", + "\n", + " try:\n", + " return userdata.get(key)\n", + " except userdata.SecretNotFoundError:\n", + " pass\n", + " except ImportError:\n", + " pass\n", + " return os.getenv(key, default)\n", + "\n", + "\n", + "AZURE_SEARCH_ENDPOINT = _get_env(\"AZURE_SEARCH_ENDPOINT\")\n", + "AZURE_SEARCH_KEY = _get_env(\"AZURE_SEARCH_KEY\") # Ensure this your Admin Key\n", + "AZURE_SEARCH_INDEX_NAME = _get_env(\"AZURE_SEARCH_INDEX_NAME\", \"docling-rag-sample\")\n", + "AZURE_OPENAI_ENDPOINT = _get_env(\"AZURE_OPENAI_ENDPOINT\")\n", + "AZURE_OPENAI_API_KEY = _get_env(\"AZURE_OPENAI_API_KEY\")\n", + "AZURE_OPENAI_API_VERSION = _get_env(\"AZURE_OPENAI_API_VERSION\", \"2024-10-21\")\n", + "AZURE_OPENAI_CHAT_MODEL = _get_env(\n", + " \"AZURE_OPENAI_CHAT_MODEL\"\n", + ") # Using a deployed model named \"gpt-4o\"\n", + "AZURE_OPENAI_EMBEDDINGS = _get_env(\n", + " \"AZURE_OPENAI_EMBEDDINGS\", \"text-embedding-3-small\"\n", + ") # Using a deployed model named \"text-embeddings-3-small\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1: Parse the PDF with Docling\n", + "\n", + "Weโ€™ll parse the **Microsoft GraphRAG Research Paper** (~15 pages). Parsing should be relatively quick, even on CPU, but it will be faster on a GPU or MPS device if available.\n", + "\n", + "*(If you prefer a different document, simply provide a different URL or local file path.)*" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Parsing a ~15-page PDF. The process should be relatively quick, even on CPU...\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;33mParsing a ~\u001b[0m\u001b[1;33m15\u001b[0m\u001b[1;33m-page PDF. The process should be relatively quick, even on CPU\u001b[0m\u001b[1;33m...\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ Docling Markdown Preview โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\n",
+       "โ”‚ ## From Local to Global: A Graph RAG Approach to Query-Focused Summarization                                    โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ Darren Edge 1โ€                                                                                                   โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ Ha Trinh 1โ€                                                                                                      โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ Newman Cheng 2                                                                                                  โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ Joshua Bradley 2                                                                                                โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ Alex Chao 3                                                                                                     โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ Apurva Mody 3                                                                                                   โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ Steven Truitt 2                                                                                                 โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ ## Jonathan Larson 1                                                                                            โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ 1 Microsoft Research 2 Microsoft Strategic Missions and Technologies 3 Microsoft Office of the CTO              โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ { daedge,trinhha,newmancheng,joshbradley,achao,moapurva,steventruitt,jolarso } @microsoft.com                   โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ โ€  These authors contributed equally to this work                                                                โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ ## Abstract                                                                                                     โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ The use of retrieval-augmented gen...                                                                           โ”‚\n",
+       "โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\n",
+       "
\n" + ], + "text/plain": [ + "โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ Docling Markdown Preview โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\n", + "โ”‚ ## From Local to Global: A Graph RAG Approach to Query-Focused Summarization โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ Darren Edge 1โ€  โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ Ha Trinh 1โ€  โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ Newman Cheng 2 โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ Joshua Bradley 2 โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ Alex Chao 3 โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ Apurva Mody 3 โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ Steven Truitt 2 โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ ## Jonathan Larson 1 โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ 1 Microsoft Research 2 Microsoft Strategic Missions and Technologies 3 Microsoft Office of the CTO โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ { daedge,trinhha,newmancheng,joshbradley,achao,moapurva,steventruitt,jolarso } @microsoft.com โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ โ€  These authors contributed equally to this work โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ ## Abstract โ”‚\n", + "โ”‚ โ”‚\n", + "โ”‚ The use of retrieval-augmented gen... โ”‚\n", + "โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from rich.console import Console\n", + "from rich.panel import Panel\n", + "\n", + "from docling.document_converter import DocumentConverter\n", + "\n", + "console = Console()\n", + "\n", + "# This URL points to the Microsoft GraphRAG Research Paper (arXiv: 2404.16130), ~15 pages\n", + "source_url = \"https://arxiv.org/pdf/2404.16130\"\n", + "\n", + "console.print(\n", + " \"[bold yellow]Parsing a ~15-page PDF. The process should be relatively quick, even on CPU...[/bold yellow]\"\n", + ")\n", + "converter = DocumentConverter()\n", + "result = converter.convert(source_url)\n", + "\n", + "# Optional: preview the parsed Markdown\n", + "md_preview = result.document.export_to_markdown()\n", + "console.print(Panel(md_preview[:500] + \"...\", title=\"Docling Markdown Preview\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part 3: Hierarchical Chunking\n", + "We convert the `Document` into smaller chunks for embedding & indexing. The built-in `HierarchicalChunker` preserves structure. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Total chunks from PDF: 106\n",
+       "
\n" + ], + "text/plain": [ + "Total chunks from PDF: \u001b[1;36m106\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from docling.chunking import HierarchicalChunker\n", + "\n", + "chunker = HierarchicalChunker()\n", + "doc_chunks = list(chunker.chunk(result.document))\n", + "\n", + "all_chunks = []\n", + "for idx, c in enumerate(doc_chunks):\n", + " chunk_text = c.text\n", + " all_chunks.append((f\"chunk_{idx}\", chunk_text))\n", + "\n", + "console.print(f\"Total chunks from PDF: {len(all_chunks)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part 3: Create Azure Search Index and Push Chunk Embeddings\n", + "Weโ€™ll define a vector index in Azure AI Search, then embed each chunk using Azure OpenAI and upload in batches." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Index 'docling-rag-sample-2' created.\n",
+       "
\n" + ], + "text/plain": [ + "Index \u001b[32m'docling-rag-sample-2'\u001b[0m created.\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from azure.core.credentials import AzureKeyCredential\n", + "from azure.search.documents.indexes import SearchIndexClient\n", + "from azure.search.documents.indexes.models import (\n", + " AzureOpenAIVectorizer,\n", + " AzureOpenAIVectorizerParameters,\n", + " HnswAlgorithmConfiguration,\n", + " SearchableField,\n", + " SearchField,\n", + " SearchFieldDataType,\n", + " SearchIndex,\n", + " SimpleField,\n", + " VectorSearch,\n", + " VectorSearchProfile,\n", + ")\n", + "from rich.console import Console\n", + "\n", + "console = Console()\n", + "\n", + "VECTOR_DIM = 1536 # Adjust based on your chosen embeddings model\n", + "\n", + "index_client = SearchIndexClient(\n", + " AZURE_SEARCH_ENDPOINT, AzureKeyCredential(AZURE_SEARCH_KEY)\n", + ")\n", + "\n", + "\n", + "def create_search_index(index_name: str):\n", + " # Define fields\n", + " fields = [\n", + " SimpleField(name=\"chunk_id\", type=SearchFieldDataType.String, key=True),\n", + " SearchableField(name=\"content\", type=SearchFieldDataType.String),\n", + " SearchField(\n", + " name=\"content_vector\",\n", + " type=SearchFieldDataType.Collection(SearchFieldDataType.Single),\n", + " searchable=True,\n", + " filterable=False,\n", + " sortable=False,\n", + " facetable=False,\n", + " vector_search_dimensions=VECTOR_DIM,\n", + " vector_search_profile_name=\"default\",\n", + " ),\n", + " ]\n", + " # Vector search config with an AzureOpenAIVectorizer\n", + " vector_search = VectorSearch(\n", + " algorithms=[HnswAlgorithmConfiguration(name=\"default\")],\n", + " profiles=[\n", + " VectorSearchProfile(\n", + " name=\"default\",\n", + " algorithm_configuration_name=\"default\",\n", + " vectorizer_name=\"default\",\n", + " )\n", + " ],\n", + " vectorizers=[\n", + " AzureOpenAIVectorizer(\n", + " vectorizer_name=\"default\",\n", + " parameters=AzureOpenAIVectorizerParameters(\n", + " resource_url=AZURE_OPENAI_ENDPOINT,\n", + " deployment_name=AZURE_OPENAI_EMBEDDINGS,\n", + " model_name=\"text-embedding-3-small\",\n", + " api_key=AZURE_OPENAI_API_KEY,\n", + " ),\n", + " )\n", + " ],\n", + " )\n", + "\n", + " # Create or update the index\n", + " new_index = SearchIndex(name=index_name, fields=fields, vector_search=vector_search)\n", + " try:\n", + " index_client.delete_index(index_name)\n", + " except:\n", + " pass\n", + "\n", + " index_client.create_or_update_index(new_index)\n", + " console.print(f\"Index '{index_name}' created.\")\n", + "\n", + "\n", + "create_search_index(AZURE_SEARCH_INDEX_NAME)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Embed and Upsert to Azure AI Search\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Uploaded batch 0 -> 50; all_succeeded: True, first_doc_status_code: 201\n",
+       "
\n" + ], + "text/plain": [ + "Uploaded batch \u001b[1;36m0\u001b[0m -> \u001b[1;36m50\u001b[0m; all_succeeded: \u001b[3;92mTrue\u001b[0m, first_doc_status_code: \u001b[1;36m201\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
Uploaded batch 50 -> 100; all_succeeded: True, first_doc_status_code: 201\n",
+       "
\n" + ], + "text/plain": [ + "Uploaded batch \u001b[1;36m50\u001b[0m -> \u001b[1;36m100\u001b[0m; all_succeeded: \u001b[3;92mTrue\u001b[0m, first_doc_status_code: \u001b[1;36m201\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
Uploaded batch 100 -> 106; all_succeeded: True, first_doc_status_code: 201\n",
+       "
\n" + ], + "text/plain": [ + "Uploaded batch \u001b[1;36m100\u001b[0m -> \u001b[1;36m106\u001b[0m; all_succeeded: \u001b[3;92mTrue\u001b[0m, first_doc_status_code: \u001b[1;36m201\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
All chunks uploaded to Azure Search.\n",
+       "
\n" + ], + "text/plain": [ + "All chunks uploaded to Azure Search.\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from azure.search.documents import SearchClient\n", + "from openai import AzureOpenAI\n", + "\n", + "search_client = SearchClient(\n", + " AZURE_SEARCH_ENDPOINT, AZURE_SEARCH_INDEX_NAME, AzureKeyCredential(AZURE_SEARCH_KEY)\n", + ")\n", + "openai_client = AzureOpenAI(\n", + " api_key=AZURE_OPENAI_API_KEY,\n", + " api_version=AZURE_OPENAI_API_VERSION,\n", + " azure_endpoint=AZURE_OPENAI_ENDPOINT,\n", + ")\n", + "\n", + "\n", + "def embed_text(text: str):\n", + " \"\"\"\n", + " Helper to generate embeddings with Azure OpenAI.\n", + " \"\"\"\n", + " response = openai_client.embeddings.create(\n", + " input=text, model=AZURE_OPENAI_EMBEDDINGS\n", + " )\n", + " return response.data[0].embedding\n", + "\n", + "\n", + "upload_docs = []\n", + "for chunk_id, chunk_text in all_chunks:\n", + " embedding_vector = embed_text(chunk_text)\n", + " upload_docs.append(\n", + " {\n", + " \"chunk_id\": chunk_id,\n", + " \"content\": chunk_text,\n", + " \"content_vector\": embedding_vector,\n", + " }\n", + " )\n", + "\n", + "\n", + "BATCH_SIZE = 50\n", + "for i in range(0, len(upload_docs), BATCH_SIZE):\n", + " subset = upload_docs[i : i + BATCH_SIZE]\n", + " resp = search_client.upload_documents(documents=subset)\n", + "\n", + " all_succeeded = all(r.succeeded for r in resp)\n", + " console.print(\n", + " f\"Uploaded batch {i} -> {i+len(subset)}; all_succeeded: {all_succeeded}, \"\n", + " f\"first_doc_status_code: {resp[0].status_code}\"\n", + " )\n", + "\n", + "console.print(\"All chunks uploaded to Azure Search.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part 4: RAG Query with Azure OpenAI\n", + "Combine retrieval from Azure Search with Chat Completions (aka. grounding your LLM)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ RAG Prompt โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ You are an AI assistant helping answering questions about Microsoft GraphRAG.                                   โ”‚\n",
+       "โ”‚ Use ONLY the text below to answer the user's question.                                                          โ”‚\n",
+       "โ”‚ If the answer isn't in the text, say you don't know.                                                            โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ Context:                                                                                                        โ”‚\n",
+       "โ”‚ Community summaries vs. source texts. When comparing community summaries to source texts using Graph RAG,       โ”‚\n",
+       "โ”‚ community summaries generally provided a small but consistent improvement in answer comprehensiveness and       โ”‚\n",
+       "โ”‚ diversity, except for root-level summaries. Intermediate-level summaries in the Podcast dataset and low-level   โ”‚\n",
+       "โ”‚ community summaries in the News dataset achieved comprehensiveness win rates of 57% and 64%, respectively.      โ”‚\n",
+       "โ”‚ Diversity win rates were 57% for Podcast intermediate-level summaries and 60% for News low-level community      โ”‚\n",
+       "โ”‚ summaries. Table 3 also illustrates the scalability advantages of Graph RAG compared to source text             โ”‚\n",
+       "โ”‚ summarization: for low-level community summaries ( C3 ), Graph RAG required 26-33% fewer context tokens, while  โ”‚\n",
+       "โ”‚ for root-level community summaries ( C0 ), it required over 97% fewer tokens. For a modest drop in performance  โ”‚\n",
+       "โ”‚ compared with other global methods, root-level Graph RAG offers a highly efficient method for the iterative     โ”‚\n",
+       "โ”‚ question answering that characterizes sensemaking activity, while retaining advantages in comprehensiveness     โ”‚\n",
+       "โ”‚ (72% win rate) and diversity (62% win rate) over naยจฤฑve RAG.                                                    โ”‚\n",
+       "โ”‚ ---                                                                                                             โ”‚\n",
+       "โ”‚ We have presented a global approach to Graph RAG, combining knowledge graph generation, retrieval-augmented     โ”‚\n",
+       "โ”‚ generation (RAG), and query-focused summarization (QFS) to support human sensemaking over entire text corpora.  โ”‚\n",
+       "โ”‚ Initial evaluations show substantial improvements over a naยจฤฑve RAG baseline for both the comprehensiveness and โ”‚\n",
+       "โ”‚ diversity of answers, as well as favorable comparisons to a global but graph-free approach using map-reduce     โ”‚\n",
+       "โ”‚ source text summarization. For situations requiring many global queries over the same dataset, summaries of     โ”‚\n",
+       "โ”‚ root-level communities in the entity-based graph index provide a data index that is both superior to naยจฤฑve RAG โ”‚\n",
+       "โ”‚ and achieves competitive performance to other global methods at a fraction of the token cost.                   โ”‚\n",
+       "โ”‚ ---                                                                                                             โ”‚\n",
+       "โ”‚ Trade-offs of building a graph index . We consistently observed Graph RAG achieve the best headto-head results  โ”‚\n",
+       "โ”‚ against other methods, but in many cases the graph-free approach to global summarization of source texts        โ”‚\n",
+       "โ”‚ performed competitively. The real-world decision about whether to invest in building a graph index depends on   โ”‚\n",
+       "โ”‚ multiple factors, including the compute budget, expected number of lifetime queries per dataset, and value      โ”‚\n",
+       "โ”‚ obtained from other aspects of the graph index (including the generic community summaries and the use of other  โ”‚\n",
+       "โ”‚ graph-related RAG approaches).                                                                                  โ”‚\n",
+       "โ”‚ ---                                                                                                             โ”‚\n",
+       "โ”‚ Future work . The graph index, rich text annotations, and hierarchical community structure supporting the       โ”‚\n",
+       "โ”‚ current Graph RAG approach offer many possibilities for refinement and adaptation. This includes RAG approaches โ”‚\n",
+       "โ”‚ that operate in a more local manner, via embedding-based matching of user queries and graph annotations, as     โ”‚\n",
+       "โ”‚ well as the possibility of hybrid RAG schemes that combine embedding-based matching against community reports   โ”‚\n",
+       "โ”‚ before employing our map-reduce summarization mechanisms. This 'roll-up' operation could also be extended       โ”‚\n",
+       "โ”‚ across more levels of the community hierarchy, as well as implemented as a more exploratory 'drill down'        โ”‚\n",
+       "โ”‚ mechanism that follows the information scent contained in higher-level community summaries.                     โ”‚\n",
+       "โ”‚ ---                                                                                                             โ”‚\n",
+       "โ”‚ Advanced RAG systems include pre-retrieval, retrieval, post-retrieval strategies designed to overcome the       โ”‚\n",
+       "โ”‚ drawbacks of Naยจฤฑve RAG, while Modular RAG systems include patterns for iterative and dynamic cycles of         โ”‚\n",
+       "โ”‚ interleaved retrieval and generation (Gao et al., 2023). Our implementation of Graph RAG incorporates multiple  โ”‚\n",
+       "โ”‚ concepts related to other systems. For example, our community summaries are a kind of self-memory (Selfmem,     โ”‚\n",
+       "โ”‚ Cheng et al., 2024) for generation-augmented retrieval (GAR, Mao et al., 2020) that facilitates future          โ”‚\n",
+       "โ”‚ generation cycles, while our parallel generation of community answers from these summaries is a kind of         โ”‚\n",
+       "โ”‚ iterative (Iter-RetGen, Shao et al., 2023) or federated (FeB4RAG, Wang et al., 2024) retrieval-generation       โ”‚\n",
+       "โ”‚ strategy. Other systems have also combined these concepts for multi-document summarization (CAiRE-COVID, Su et  โ”‚\n",
+       "โ”‚ al., 2020) and multi-hop question answering (ITRG, Feng et al., 2023; IR-CoT, Trivedi et al., 2022; DSP,        โ”‚\n",
+       "โ”‚ Khattab et al., 2022). Our use of a hierarchical index and summarization also bears resemblance to further      โ”‚\n",
+       "โ”‚ approaches, such as generating a hierarchical index of text chunks by clustering the vectors of text embeddings โ”‚\n",
+       "โ”‚ (RAPTOR, Sarthi et al., 2024) or generating a 'tree of clarifications' to answer multiple interpretations of    โ”‚\n",
+       "โ”‚ ambiguous questions (Kim et al., 2023). However, none of these iterative or hierarchical approaches use the     โ”‚\n",
+       "โ”‚ kind of self-generated graph index that enables Graph RAG.                                                      โ”‚\n",
+       "โ”‚ ---                                                                                                             โ”‚\n",
+       "โ”‚ The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge     โ”‚\n",
+       "โ”‚ source enables large language models (LLMs) to answer questions over private and/or previously unseen document  โ”‚\n",
+       "โ”‚ collections. However, RAG fails on global questions directed at an entire text corpus, such as 'What are the    โ”‚\n",
+       "โ”‚ main themes in the dataset?', since this is inherently a queryfocused summarization (QFS) task, rather than an  โ”‚\n",
+       "โ”‚ explicit retrieval task. Prior QFS methods, meanwhile, fail to scale to the quantities of text indexed by       โ”‚\n",
+       "โ”‚ typical RAGsystems. To combine the strengths of these contrasting methods, we propose a Graph RAG approach to   โ”‚\n",
+       "โ”‚ question answering over private text corpora that scales with both the generality of user questions and the     โ”‚\n",
+       "โ”‚ quantity of source text to be indexed. Our approach uses an LLM to build a graph-based text index in two        โ”‚\n",
+       "โ”‚ stages: first to derive an entity knowledge graph from the source documents, then to pregenerate community      โ”‚\n",
+       "โ”‚ summaries for all groups of closely-related entities. Given a question, each community summary is used to       โ”‚\n",
+       "โ”‚ generate a partial response, before all partial responses are again summarized in a final response to the user. โ”‚\n",
+       "โ”‚ For a class of global sensemaking questions over datasets in the 1 million token range, we show that Graph RAG  โ”‚\n",
+       "โ”‚ leads to substantial improvements over a naยจฤฑve RAG baseline for both the comprehensiveness and diversity of    โ”‚\n",
+       "โ”‚ generated answers. An open-source, Python-based implementation of both global and local Graph RAG approaches is โ”‚\n",
+       "โ”‚ forthcoming at https://aka . ms/graphrag .                                                                      โ”‚\n",
+       "โ”‚ ---                                                                                                             โ”‚\n",
+       "โ”‚ Given the multi-stage nature of our Graph RAG mechanism, the multiple conditions we wanted to compare, and the  โ”‚\n",
+       "โ”‚ lack of gold standard answers to our activity-based sensemaking questions, we decided to adopt a head-to-head   โ”‚\n",
+       "โ”‚ comparison approach using an LLM evaluator. We selected three target metrics capturing qualities that are       โ”‚\n",
+       "โ”‚ desirable for sensemaking activities, as well as a control metric (directness) used as a indicator of validity. โ”‚\n",
+       "โ”‚ Since directness is effectively in opposition to comprehensiveness and diversity, we would not expect any       โ”‚\n",
+       "โ”‚ method to win across all four metrics.                                                                          โ”‚\n",
+       "โ”‚ ---                                                                                                             โ”‚\n",
+       "โ”‚ Figure 1: Graph RAG pipeline using an LLM-derived graph index of source document text. This index spans nodes   โ”‚\n",
+       "โ”‚ (e.g., entities), edges (e.g., relationships), and covariates (e.g., claims) that have been detected,           โ”‚\n",
+       "โ”‚ extracted, and summarized by LLM prompts tailored to the domain of the dataset. Community detection (e.g.,      โ”‚\n",
+       "โ”‚ Leiden, Traag et al., 2019) is used to partition the graph index into groups of elements (nodes, edges,         โ”‚\n",
+       "โ”‚ covariates) that the LLM can summarize in parallel at both indexing time and query time. The 'global answer' to โ”‚\n",
+       "โ”‚ a given query is produced using a final round of query-focused summarization over all community summaries       โ”‚\n",
+       "โ”‚ reporting relevance to that query.                                                                              โ”‚\n",
+       "โ”‚ ---                                                                                                             โ”‚\n",
+       "โ”‚ Retrieval-augmented generation (RAG, Lewis et al., 2020) is an established approach to answering user questions โ”‚\n",
+       "โ”‚ over entire datasets, but it is designed for situations where these answers are contained locally within        โ”‚\n",
+       "โ”‚ regions of text whose retrieval provides sufficient grounding for the generation task. Instead, a more          โ”‚\n",
+       "โ”‚ appropriate task framing is query-focused summarization (QFS, Dang, 2006), and in particular, query-focused     โ”‚\n",
+       "โ”‚ abstractive summarization that generates natural language summaries and not just concatenated excerpts (Baumel  โ”‚\n",
+       "โ”‚ et al., 2018; Laskar et al., 2020; Yao et al., 2017) . In recent years, however, such distinctions between      โ”‚\n",
+       "โ”‚ summarization tasks that are abstractive versus extractive, generic versus query-focused, and single-document   โ”‚\n",
+       "โ”‚ versus multi-document, have become less relevant. While early applications of the transformer architecture      โ”‚\n",
+       "โ”‚ showed substantial improvements on the state-of-the-art for all such summarization tasks (Goodwin et al., 2020; โ”‚\n",
+       "โ”‚ Laskar et al., 2022; Liu and Lapata, 2019), these tasks are now trivialized by modern LLMs, including the GPT   โ”‚\n",
+       "โ”‚ (Achiam et al., 2023; Brown et al., 2020), Llama (Touvron et al., 2023), and Gemini (Anil et al., 2023) series, โ”‚\n",
+       "โ”‚ all of which can use in-context learning to summarize any content provided in their context window.             โ”‚\n",
+       "โ”‚ ---                                                                                                             โ”‚\n",
+       "โ”‚ community descriptions provide complete coverage of the underlying graph index and the input documents it       โ”‚\n",
+       "โ”‚ represents. Query-focused summarization of an entire corpus is then made possible using a map-reduce approach:  โ”‚\n",
+       "โ”‚ first using each community summary to answer the query independently and in parallel, then summarizing all      โ”‚\n",
+       "โ”‚ relevant partial answers into a final global answer.                                                            โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ Question: What are the main advantages of using the Graph RAG approach for query-focused summarization compared โ”‚\n",
+       "โ”‚ to traditional RAG methods?                                                                                     โ”‚\n",
+       "โ”‚ Answer:                                                                                                         โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;31mโ•ญโ”€\u001b[0m\u001b[1;31mโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\u001b[0m RAG Prompt \u001b[1;31mโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\u001b[0m\u001b[1;31mโ”€โ•ฎ\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mYou are an AI assistant helping answering questions about Microsoft GraphRAG.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mUse ONLY the text below to answer the user's question.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mIf the answer isn't in the text, say you don't know.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mContext:\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mCommunity summaries vs. source texts. When comparing community summaries to source texts using Graph RAG, \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcommunity summaries generally provided a small but consistent improvement in answer comprehensiveness and \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mdiversity, except for root-level summaries. Intermediate-level summaries in the Podcast dataset and low-level \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcommunity summaries in the News dataset achieved comprehensiveness win rates of 57% and 64%, respectively. \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mDiversity win rates were 57% for Podcast intermediate-level summaries and 60% for News low-level community \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msummaries. Table 3 also illustrates the scalability advantages of Graph RAG compared to source text \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msummarization: for low-level community summaries ( C3 ), Graph RAG required 26-33% fewer context tokens, while \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mfor root-level community summaries ( C0 ), it required over 97% fewer tokens. For a modest drop in performance \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcompared with other global methods, root-level Graph RAG offers a highly efficient method for the iterative \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mquestion answering that characterizes sensemaking activity, while retaining advantages in comprehensiveness \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(72% win rate) and diversity (62% win rate) over naยจฤฑve RAG.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mWe have presented a global approach to Graph RAG, combining knowledge graph generation, retrieval-augmented \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mgeneration (RAG), and query-focused summarization (QFS) to support human sensemaking over entire text corpora. \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mInitial evaluations show substantial improvements over a naยจฤฑve RAG baseline for both the comprehensiveness and\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mdiversity of answers, as well as favorable comparisons to a global but graph-free approach using map-reduce \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msource text summarization. For situations requiring many global queries over the same dataset, summaries of \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mroot-level communities in the entity-based graph index provide a data index that is both superior to naยจฤฑve RAG\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mand achieves competitive performance to other global methods at a fraction of the token cost.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mTrade-offs of building a graph index . We consistently observed Graph RAG achieve the best headto-head results \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31magainst other methods, but in many cases the graph-free approach to global summarization of source texts \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mperformed competitively. The real-world decision about whether to invest in building a graph index depends on \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mmultiple factors, including the compute budget, expected number of lifetime queries per dataset, and value \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mobtained from other aspects of the graph index (including the generic community summaries and the use of other \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mgraph-related RAG approaches).\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mFuture work . The graph index, rich text annotations, and hierarchical community structure supporting the \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcurrent Graph RAG approach offer many possibilities for refinement and adaptation. This includes RAG approaches\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mthat operate in a more local manner, via embedding-based matching of user queries and graph annotations, as \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mwell as the possibility of hybrid RAG schemes that combine embedding-based matching against community reports \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mbefore employing our map-reduce summarization mechanisms. This 'roll-up' operation could also be extended \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31macross more levels of the community hierarchy, as well as implemented as a more exploratory 'drill down' \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mmechanism that follows the information scent contained in higher-level community summaries.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mAdvanced RAG systems include pre-retrieval, retrieval, post-retrieval strategies designed to overcome the \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mdrawbacks of Naยจฤฑve RAG, while Modular RAG systems include patterns for iterative and dynamic cycles of \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31minterleaved retrieval and generation (Gao et al., 2023). Our implementation of Graph RAG incorporates multiple \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mconcepts related to other systems. For example, our community summaries are a kind of self-memory (Selfmem, \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mCheng et al., 2024) for generation-augmented retrieval (GAR, Mao et al., 2020) that facilitates future \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mgeneration cycles, while our parallel generation of community answers from these summaries is a kind of \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31miterative (Iter-RetGen, Shao et al., 2023) or federated (FeB4RAG, Wang et al., 2024) retrieval-generation \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mstrategy. Other systems have also combined these concepts for multi-document summarization (CAiRE-COVID, Su et \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mal., 2020) and multi-hop question answering (ITRG, Feng et al., 2023; IR-CoT, Trivedi et al., 2022; DSP, \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mKhattab et al., 2022). Our use of a hierarchical index and summarization also bears resemblance to further \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mapproaches, such as generating a hierarchical index of text chunks by clustering the vectors of text embeddings\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(RAPTOR, Sarthi et al., 2024) or generating a 'tree of clarifications' to answer multiple interpretations of \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mambiguous questions (Kim et al., 2023). However, none of these iterative or hierarchical approaches use the \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mkind of self-generated graph index that enables Graph RAG.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mThe use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msource enables large language models (LLMs) to answer questions over private and/or previously unseen document \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcollections. However, RAG fails on global questions directed at an entire text corpus, such as 'What are the \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mmain themes in the dataset?', since this is inherently a queryfocused summarization (QFS) task, rather than an \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mexplicit retrieval task. Prior QFS methods, meanwhile, fail to scale to the quantities of text indexed by \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mtypical RAGsystems. To combine the strengths of these contrasting methods, we propose a Graph RAG approach to \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mquestion answering over private text corpora that scales with both the generality of user questions and the \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mquantity of source text to be indexed. Our approach uses an LLM to build a graph-based text index in two \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mstages: first to derive an entity knowledge graph from the source documents, then to pregenerate community \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msummaries for all groups of closely-related entities. Given a question, each community summary is used to \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mgenerate a partial response, before all partial responses are again summarized in a final response to the user.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mFor a class of global sensemaking questions over datasets in the 1 million token range, we show that Graph RAG \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mleads to substantial improvements over a naยจฤฑve RAG baseline for both the comprehensiveness and diversity of \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mgenerated answers. An open-source, Python-based implementation of both global and local Graph RAG approaches is\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mforthcoming at https://aka . ms/graphrag .\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mGiven the multi-stage nature of our Graph RAG mechanism, the multiple conditions we wanted to compare, and the \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mlack of gold standard answers to our activity-based sensemaking questions, we decided to adopt a head-to-head \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcomparison approach using an LLM evaluator. We selected three target metrics capturing qualities that are \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mdesirable for sensemaking activities, as well as a control metric (directness) used as a indicator of validity.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mSince directness is effectively in opposition to comprehensiveness and diversity, we would not expect any \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mmethod to win across all four metrics.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mFigure 1: Graph RAG pipeline using an LLM-derived graph index of source document text. This index spans nodes \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(e.g., entities), edges (e.g., relationships), and covariates (e.g., claims) that have been detected, \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mextracted, and summarized by LLM prompts tailored to the domain of the dataset. Community detection (e.g., \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mLeiden, Traag et al., 2019) is used to partition the graph index into groups of elements (nodes, edges, \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcovariates) that the LLM can summarize in parallel at both indexing time and query time. The 'global answer' to\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31ma given query is produced using a final round of query-focused summarization over all community summaries \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mreporting relevance to that query.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mRetrieval-augmented generation (RAG, Lewis et al., 2020) is an established approach to answering user questions\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mover entire datasets, but it is designed for situations where these answers are contained locally within \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mregions of text whose retrieval provides sufficient grounding for the generation task. Instead, a more \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mappropriate task framing is query-focused summarization (QFS, Dang, 2006), and in particular, query-focused \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mabstractive summarization that generates natural language summaries and not just concatenated excerpts (Baumel \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31met al., 2018; Laskar et al., 2020; Yao et al., 2017) . In recent years, however, such distinctions between \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msummarization tasks that are abstractive versus extractive, generic versus query-focused, and single-document \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mversus multi-document, have become less relevant. While early applications of the transformer architecture \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mshowed substantial improvements on the state-of-the-art for all such summarization tasks (Goodwin et al., 2020;\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mLaskar et al., 2022; Liu and Lapata, 2019), these tasks are now trivialized by modern LLMs, including the GPT \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(Achiam et al., 2023; Brown et al., 2020), Llama (Touvron et al., 2023), and Gemini (Anil et al., 2023) series,\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mall of which can use in-context learning to summarize any content provided in their context window.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcommunity descriptions provide complete coverage of the underlying graph index and the input documents it \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mrepresents. Query-focused summarization of an entire corpus is then made possible using a map-reduce approach: \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mfirst using each community summary to answer the query independently and in parallel, then summarizing all \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mrelevant partial answers into a final global answer.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mQuestion: What are the main advantages of using the Graph RAG approach for query-focused summarization compared\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mto traditional RAG methods?\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mAnswer:\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ RAG Response โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\n",
+       "โ”‚ The main advantages of using the Graph RAG approach for query-focused summarization compared to traditional RAG โ”‚\n",
+       "โ”‚ methods include:                                                                                                โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ 1. **Improved Comprehensiveness and Diversity**: Graph RAG shows substantial improvements over a naรฏve RAG      โ”‚\n",
+       "โ”‚ baseline in terms of the comprehensiveness and diversity of answers. This is particularly beneficial for global โ”‚\n",
+       "โ”‚ sensemaking questions over large datasets.                                                                      โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ 2. **Scalability**: Graph RAG provides scalability advantages, achieving efficient summarization with           โ”‚\n",
+       "โ”‚ significantly fewer context tokens required. For instance, it requires 26-33% fewer tokens for low-level        โ”‚\n",
+       "โ”‚ community summaries and over 97% fewer tokens for root-level summaries compared to source text summarization.   โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ 3. **Efficiency in Iterative Question Answering**: Root-level Graph RAG offers a highly efficient method for    โ”‚\n",
+       "โ”‚ iterative question answering, which is crucial for sensemaking activities, with only a modest drop in           โ”‚\n",
+       "โ”‚ performance compared to other global methods.                                                                   โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ 4. **Global Query Handling**: It supports handling global queries effectively, as it combines knowledge graph   โ”‚\n",
+       "โ”‚ generation, retrieval-augmented generation, and query-focused summarization, making it suitable for sensemaking โ”‚\n",
+       "โ”‚ over entire text corpora.                                                                                       โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ 5. **Hierarchical Indexing and Summarization**: The use of a hierarchical index and summarization allows for    โ”‚\n",
+       "โ”‚ efficient processing and summarizing of community summaries into a final global answer, facilitating a          โ”‚\n",
+       "โ”‚ comprehensive coverage of the underlying graph index and input documents.                                       โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ 6. **Reduced Token Cost**: For situations requiring many global queries over the same dataset, Graph RAG        โ”‚\n",
+       "โ”‚ achieves competitive performance to other global methods at a fraction of the token cost.                       โ”‚\n",
+       "โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;32mโ•ญโ”€\u001b[0m\u001b[1;32mโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\u001b[0m RAG Response \u001b[1;32mโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\u001b[0m\u001b[1;32mโ”€โ•ฎ\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mThe main advantages of using the Graph RAG approach for query-focused summarization compared to traditional RAG\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mmethods include:\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m1. **Improved Comprehensiveness and Diversity**: Graph RAG shows substantial improvements over a naรฏve RAG \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mbaseline in terms of the comprehensiveness and diversity of answers. This is particularly beneficial for global\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32msensemaking questions over large datasets.\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m2. **Scalability**: Graph RAG provides scalability advantages, achieving efficient summarization with \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32msignificantly fewer context tokens required. For instance, it requires 26-33% fewer tokens for low-level \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mcommunity summaries and over 97% fewer tokens for root-level summaries compared to source text summarization.\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m3. **Efficiency in Iterative Question Answering**: Root-level Graph RAG offers a highly efficient method for \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32miterative question answering, which is crucial for sensemaking activities, with only a modest drop in \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mperformance compared to other global methods.\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m4. **Global Query Handling**: It supports handling global queries effectively, as it combines knowledge graph \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mgeneration, retrieval-augmented generation, and query-focused summarization, making it suitable for sensemaking\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mover entire text corpora.\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m5. **Hierarchical Indexing and Summarization**: The use of a hierarchical index and summarization allows for \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mefficient processing and summarizing of community summaries into a final global answer, facilitating a \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mcomprehensive coverage of the underlying graph index and input documents.\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m6. **Reduced Token Cost**: For situations requiring many global queries over the same dataset, Graph RAG \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32machieves competitive performance to other global methods at a fraction of the token cost.\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from azure.search.documents.models import VectorizableTextQuery\n", + "\n", + "\n", + "def generate_chat_response(prompt: str, system_message: str = None):\n", + " \"\"\"\n", + " Generates a single-turn chat response using Azure OpenAI Chat.\n", + " If you need multi-turn conversation or follow-up queries, you'll have to\n", + " maintain the messages list externally.\n", + " \"\"\"\n", + " messages = []\n", + " if system_message:\n", + " messages.append({\"role\": \"system\", \"content\": system_message})\n", + " messages.append({\"role\": \"user\", \"content\": prompt})\n", + "\n", + " completion = openai_client.chat.completions.create(\n", + " model=AZURE_OPENAI_CHAT_MODEL, messages=messages, temperature=0.7\n", + " )\n", + " return completion.choices[0].message.content\n", + "\n", + "\n", + "user_query = \"What are the main advantages of using the Graph RAG approach for query-focused summarization compared to traditional RAG methods?\"\n", + "user_embed = embed_text(user_query)\n", + "\n", + "vector_query = VectorizableTextQuery(\n", + " text=user_query, # passing in text for a hybrid search\n", + " k_nearest_neighbors=5,\n", + " fields=\"content_vector\",\n", + ")\n", + "\n", + "search_results = search_client.search(\n", + " search_text=user_query, vector_queries=[vector_query], select=[\"content\"], top=10\n", + ")\n", + "\n", + "retrieved_chunks = []\n", + "for result in search_results:\n", + " snippet = result[\"content\"]\n", + " retrieved_chunks.append(snippet)\n", + "\n", + "context_str = \"\\n---\\n\".join(retrieved_chunks)\n", + "rag_prompt = f\"\"\"\n", + "You are an AI assistant helping answering questions about Microsoft GraphRAG.\n", + "Use ONLY the text below to answer the user's question.\n", + "If the answer isn't in the text, say you don't know.\n", + "\n", + "Context:\n", + "{context_str}\n", + "\n", + "Question: {user_query}\n", + "Answer:\n", + "\"\"\"\n", + "\n", + "final_answer = generate_chat_response(rag_prompt)\n", + "\n", + "console.print(Panel(rag_prompt, title=\"RAG Prompt\", style=\"bold red\"))\n", + "console.print(Panel(final_answer, title=\"RAG Response\", style=\"bold green\"))" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/mkdocs.yml b/mkdocs.yml index 8f8d86d9..0f3e9dd0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -76,15 +76,17 @@ nav: - "Multimodal export": examples/export_multimodal.py - "Force full page OCR": examples/full_page_ocr.py - "Accelerator options": examples/run_with_accelerator.py - - "Simple translation": examples/translate.py + - "Simple translation": examples/translate.py - โœ‚๏ธ Chunking: - - "Hybrid chunking": examples/hybrid_chunking.ipynb - - ๐Ÿ’ฌ RAG / QA: + - examples/hybrid_chunking.ipynb + - ๐Ÿค– RAG with AI dev frameworks: - examples/rag_haystack.ipynb - - examples/rag_llamaindex.ipynb - examples/rag_langchain.ipynb + - examples/rag_llamaindex.ipynb + - ๐Ÿ—‚๏ธ More examples: - examples/rag_weaviate.ipynb - RAG with Granite [โ†—]: https://github.com/ibm-granite-community/granite-snack-cookbook/blob/main/recipes/RAG/Granite_Docling_RAG.ipynb + - examples/rag_azuresearch.ipynb - examples/retrieval_qdrant.ipynb - Integrations: - Integrations: integrations/index.md From c58f75d0f75040e32820cc2915ec00755211c02f Mon Sep 17 00:00:00 2001 From: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> Date: Fri, 24 Jan 2025 09:27:05 -0600 Subject: [PATCH 02/12] docs: fix minor typos (#801) Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> --- docs/examples/rag_azuresearch.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/examples/rag_azuresearch.ipynb b/docs/examples/rag_azuresearch.ipynb index abbe3774..4350ec2b 100644 --- a/docs/examples/rag_azuresearch.ipynb +++ b/docs/examples/rag_azuresearch.ipynb @@ -63,7 +63,7 @@ "source": [ "# Part 0: Prerequisites\n", " - **Azure AI Search** resource\n", - " - **Azure OpenAI** resource with a deployed embedding & chat completion model (e.g. `text-embedding-3-small` & `gpt-4o`) \n", + " - **Azure OpenAI** resource with a deployed embedding and chat completion model (e.g. `text-embedding-3-small` and `gpt-4o`) \n", " - **Docling 2.12+** (installs `docling_core` automatically) Docling installed (Python 3.8+ environment)\n", "\n", "- A **GPU-enabled environment** is preferred for faster parsing. Docling 2.12 automatically detects GPU if present.\n", @@ -97,7 +97,7 @@ "\n", "\n", "AZURE_SEARCH_ENDPOINT = _get_env(\"AZURE_SEARCH_ENDPOINT\")\n", - "AZURE_SEARCH_KEY = _get_env(\"AZURE_SEARCH_KEY\") # Ensure this your Admin Key\n", + "AZURE_SEARCH_KEY = _get_env(\"AZURE_SEARCH_KEY\") # Ensure this is your Admin Key\n", "AZURE_SEARCH_INDEX_NAME = _get_env(\"AZURE_SEARCH_INDEX_NAME\", \"docling-rag-sample\")\n", "AZURE_OPENAI_ENDPOINT = _get_env(\"AZURE_OPENAI_ENDPOINT\")\n", "AZURE_OPENAI_API_KEY = _get_env(\"AZURE_OPENAI_API_KEY\")\n", @@ -235,8 +235,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Part 3: Hierarchical Chunking\n", - "We convert the `Document` into smaller chunks for embedding & indexing. The built-in `HierarchicalChunker` preserves structure. " + "# Part 2: Hierarchical Chunking\n", + "We convert the `Document` into smaller chunks for embedding and indexing. The built-in `HierarchicalChunker` preserves structure. " ] }, { From 3213b247ad6870ff984271f09f7720be68d9479b Mon Sep 17 00:00:00 2001 From: Matteo <43417658+Matteo-Omenetti@users.noreply.github.com> Date: Fri, 24 Jan 2025 16:54:22 +0100 Subject: [PATCH 03/12] feat: Code and equation model for PDF and code blocks in markdown (#752) * propagated changes for new CodeItem class Signed-off-by: Matteo Omenetti * Rebased branch on latest main. changes for CodeItem Signed-off-by: Matteo Omenetti * removed unused files Signed-off-by: Matteo Omenetti * chore: update lockfile Signed-off-by: Christoph Auer * pin latest docling-core Signed-off-by: Michele Dolfi * update docling-core pinning Signed-off-by: Michele Dolfi * pin docling-core Signed-off-by: Michele Dolfi * use new add_code in backends and update typing in MD backend Signed-off-by: Michele Dolfi * added if statement for backend Signed-off-by: Matteo Omenetti * removed unused import Signed-off-by: Matteo Omenetti * removed print statements Signed-off-by: Matteo Omenetti * gt for new pdf Signed-off-by: Matteo Omenetti * Update docling/pipeline/standard_pdf_pipeline.py Co-authored-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> Signed-off-by: Matteo <43417658+Matteo-Omenetti@users.noreply.github.com> * fixed doc comment of __call__ function of code_formula_model Signed-off-by: Matteo Omenetti * fix artifacts_path type Signed-off-by: Michele Dolfi * move imports Signed-off-by: Michele Dolfi * move expansion_factor to base class Signed-off-by: Michele Dolfi --------- Signed-off-by: Matteo Omenetti Signed-off-by: Christoph Auer Signed-off-by: Michele Dolfi Signed-off-by: Matteo <43417658+Matteo-Omenetti@users.noreply.github.com> Co-authored-by: Christoph Auer Co-authored-by: Michele Dolfi Co-authored-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> --- docling/backend/asciidoc_backend.py | 1 - docling/backend/html_backend.py | 2 +- docling/backend/md_backend.py | 71 ++- docling/backend/msexcel_backend.py | 1 - docling/backend/msword_backend.py | 1 - docling/backend/pdf_backend.py | 2 - docling/datamodel/pipeline_options.py | 14 +- docling/models/base_model.py | 19 +- docling/models/code_formula_model.py | 245 ++++++++++ docling/models/layout_model.py | 2 +- docling/models/page_assemble_model.py | 25 - docling/models/tesseract_ocr_cli_model.py | 1 - docling/pipeline/base_pipeline.py | 8 +- docling/pipeline/standard_pdf_pipeline.py | 21 +- docling/utils/glm_utils.py | 5 +- docs/examples/develop_picture_enrichment.py | 2 - poetry.lock | 460 +++++++++--------- pyproject.toml | 4 +- tests/data/code_and_formula.pdf | Bin 0 -> 94301 bytes .../docling_v1/code_and_formula.doctags.txt | 13 + .../docling_v1/code_and_formula.json | 1 + .../docling_v1/code_and_formula.md | 19 + .../docling_v1/code_and_formula.pages.json | 1 + .../docling_v2/code_and_formula.doctags.txt | 14 + .../docling_v2/code_and_formula.json | 1 + .../docling_v2/code_and_formula.md | 25 + .../docling_v2/code_and_formula.pages.json | 1 + tests/test_code_formula.py | 66 +++ 28 files changed, 707 insertions(+), 318 deletions(-) create mode 100644 docling/models/code_formula_model.py create mode 100644 tests/data/code_and_formula.pdf create mode 100644 tests/data/groundtruth/docling_v1/code_and_formula.doctags.txt create mode 100644 tests/data/groundtruth/docling_v1/code_and_formula.json create mode 100644 tests/data/groundtruth/docling_v1/code_and_formula.md create mode 100644 tests/data/groundtruth/docling_v1/code_and_formula.pages.json create mode 100644 tests/data/groundtruth/docling_v2/code_and_formula.doctags.txt create mode 100644 tests/data/groundtruth/docling_v2/code_and_formula.json create mode 100644 tests/data/groundtruth/docling_v2/code_and_formula.md create mode 100644 tests/data/groundtruth/docling_v2/code_and_formula.pages.json create mode 100644 tests/test_code_formula.py diff --git a/docling/backend/asciidoc_backend.py b/docling/backend/asciidoc_backend.py index 829419af..397bfc44 100644 --- a/docling/backend/asciidoc_backend.py +++ b/docling/backend/asciidoc_backend.py @@ -24,7 +24,6 @@ class AsciiDocBackend(DeclarativeDocumentBackend): - def __init__(self, in_doc: InputDocument, path_or_stream: Union[BytesIO, Path]): super().__init__(in_doc, path_or_stream) diff --git a/docling/backend/html_backend.py b/docling/backend/html_backend.py index ae478885..66dd4a2c 100644 --- a/docling/backend/html_backend.py +++ b/docling/backend/html_backend.py @@ -215,7 +215,7 @@ def handle_code(self, element, idx, doc): label = DocItemLabel.CODE if len(text) == 0: return - doc.add_text(parent=self.parents[self.level], label=label, text=text) + doc.add_code(parent=self.parents[self.level], label=label, text=text) def handle_paragraph(self, element, idx, doc): """Handles paragraph tags (p).""" diff --git a/docling/backend/md_backend.py b/docling/backend/md_backend.py index 2bcc6d7d..8171085c 100644 --- a/docling/backend/md_backend.py +++ b/docling/backend/md_backend.py @@ -3,19 +3,22 @@ import warnings from io import BytesIO from pathlib import Path -from typing import Set, Union +from typing import List, Optional, Set, Union import marko import marko.ext import marko.ext.gfm import marko.inline from docling_core.types.doc import ( + DocItem, DocItemLabel, DoclingDocument, DocumentOrigin, GroupLabel, + NodeItem, TableCell, TableData, + TextItem, ) from marko import Markdown @@ -27,8 +30,7 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend): - - def shorten_underscore_sequences(self, markdown_text, max_length=10): + def shorten_underscore_sequences(self, markdown_text: str, max_length: int = 10): # This regex will match any sequence of underscores pattern = r"_+" @@ -90,13 +92,13 @@ def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path] ) from e return - def close_table(self, doc=None): + def close_table(self, doc: DoclingDocument): if self.in_table: _log.debug("=== TABLE START ===") for md_table_row in self.md_table_buffer: _log.debug(md_table_row) _log.debug("=== TABLE END ===") - tcells = [] + tcells: List[TableCell] = [] result_table = [] for n, md_table_row in enumerate(self.md_table_buffer): data = [] @@ -137,15 +139,19 @@ def close_table(self, doc=None): self.in_table = False self.md_table_buffer = [] # clean table markdown buffer # Initialize Docling TableData - data = TableData(num_rows=num_rows, num_cols=num_cols, table_cells=tcells) + table_data = TableData( + num_rows=num_rows, num_cols=num_cols, table_cells=tcells + ) # Populate for tcell in tcells: - data.table_cells.append(tcell) + table_data.table_cells.append(tcell) if len(tcells) > 0: - doc.add_table(data=data) + doc.add_table(data=table_data) return - def process_inline_text(self, parent_element, doc=None): + def process_inline_text( + self, parent_element: Optional[NodeItem], doc: DoclingDocument + ): # self.inline_text_buffer += str(text_in) txt = self.inline_text_buffer.strip() if len(txt) > 0: @@ -156,14 +162,20 @@ def process_inline_text(self, parent_element, doc=None): ) self.inline_text_buffer = "" - def iterate_elements(self, element, depth=0, doc=None, parent_element=None): + def iterate_elements( + self, + element: marko.block.Element, + depth: int, + doc: DoclingDocument, + parent_element: Optional[NodeItem] = None, + ): # Iterates over all elements in the AST # Check for different element types and process relevant details if isinstance(element, marko.block.Heading): self.close_table(doc) self.process_inline_text(parent_element, doc) _log.debug( - f" - Heading level {element.level}, content: {element.children[0].children}" + f" - Heading level {element.level}, content: {element.children[0].children}" # type: ignore ) if element.level == 1: doc_label = DocItemLabel.TITLE @@ -172,10 +184,10 @@ def iterate_elements(self, element, depth=0, doc=None, parent_element=None): # Header could have arbitrary inclusion of bold, italic or emphasis, # hence we need to traverse the tree to get full text of a header - strings = [] + strings: List[str] = [] # Define a recursive function to traverse the tree - def traverse(node): + def traverse(node: marko.block.BlockElement): # Check if the node has a "children" attribute if hasattr(node, "children"): # If "children" is a list, continue traversal @@ -209,9 +221,13 @@ def traverse(node): self.process_inline_text(parent_element, doc) _log.debug(" - List item") - snippet_text = str(element.children[0].children[0].children) + snippet_text = str(element.children[0].children[0].children) # type: ignore is_numbered = False - if parent_element.label == GroupLabel.ORDERED_LIST: + if ( + parent_element is not None + and isinstance(parent_element, DocItem) + and parent_element.label == GroupLabel.ORDERED_LIST + ): is_numbered = True doc.add_list_item( enumerated=is_numbered, parent=parent_element, text=snippet_text @@ -221,7 +237,14 @@ def traverse(node): self.close_table(doc) self.process_inline_text(parent_element, doc) _log.debug(f" - Image with alt: {element.title}, url: {element.dest}") - doc.add_picture(parent=parent_element, caption=element.title) + + fig_caption: Optional[TextItem] = None + if element.title is not None and element.title != "": + fig_caption = doc.add_text( + label=DocItemLabel.CAPTION, text=element.title + ) + + doc.add_picture(parent=parent_element, caption=fig_caption) elif isinstance(element, marko.block.Paragraph): self.process_inline_text(parent_element, doc) @@ -252,27 +275,21 @@ def traverse(node): self.process_inline_text(parent_element, doc) _log.debug(f" - Code Span: {element.children}") snippet_text = str(element.children).strip() - doc.add_text( - label=DocItemLabel.CODE, parent=parent_element, text=snippet_text - ) + doc.add_code(parent=parent_element, text=snippet_text) elif isinstance(element, marko.block.CodeBlock): self.close_table(doc) self.process_inline_text(parent_element, doc) _log.debug(f" - Code Block: {element.children}") - snippet_text = str(element.children[0].children).strip() - doc.add_text( - label=DocItemLabel.CODE, parent=parent_element, text=snippet_text - ) + snippet_text = str(element.children[0].children).strip() # type: ignore + doc.add_code(parent=parent_element, text=snippet_text) elif isinstance(element, marko.block.FencedCode): self.close_table(doc) self.process_inline_text(parent_element, doc) _log.debug(f" - Code Block: {element.children}") - snippet_text = str(element.children[0].children).strip() - doc.add_text( - label=DocItemLabel.CODE, parent=parent_element, text=snippet_text - ) + snippet_text = str(element.children[0].children).strip() # type: ignore + doc.add_code(parent=parent_element, text=snippet_text) elif isinstance(element, marko.inline.LineBreak): self.process_inline_text(parent_element, doc) diff --git a/docling/backend/msexcel_backend.py b/docling/backend/msexcel_backend.py index 508b0e8d..2d200d75 100644 --- a/docling/backend/msexcel_backend.py +++ b/docling/backend/msexcel_backend.py @@ -44,7 +44,6 @@ class ExcelTable(BaseModel): class MsExcelDocumentBackend(DeclarativeDocumentBackend): - def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]): super().__init__(in_doc, path_or_stream) diff --git a/docling/backend/msword_backend.py b/docling/backend/msword_backend.py index f59356e2..f8148d52 100644 --- a/docling/backend/msword_backend.py +++ b/docling/backend/msword_backend.py @@ -26,7 +26,6 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend): - def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]): super().__init__(in_doc, path_or_stream) self.XML_KEY = ( diff --git a/docling/backend/pdf_backend.py b/docling/backend/pdf_backend.py index cd7a0815..35c83b8c 100644 --- a/docling/backend/pdf_backend.py +++ b/docling/backend/pdf_backend.py @@ -12,7 +12,6 @@ class PdfPageBackend(ABC): - @abstractmethod def get_text_in_rect(self, bbox: BoundingBox) -> str: pass @@ -45,7 +44,6 @@ def unload(self): class PdfDocumentBackend(PaginatedDocumentBackend): - def __init__(self, in_doc: InputDocument, path_or_stream: Union[BytesIO, Path]): super().__init__(in_doc, path_or_stream) diff --git a/docling/datamodel/pipeline_options.py b/docling/datamodel/pipeline_options.py index eeec6bab..efdf3b1c 100644 --- a/docling/datamodel/pipeline_options.py +++ b/docling/datamodel/pipeline_options.py @@ -1,17 +1,11 @@ import logging import os -import warnings from enum import Enum from pathlib import Path -from typing import Annotated, Any, Dict, List, Literal, Optional, Tuple, Type, Union +from typing import Any, List, Literal, Optional, Union -from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator -from pydantic_settings import ( - BaseSettings, - PydanticBaseSettingsSource, - SettingsConfigDict, -) -from typing_extensions import deprecated +from pydantic import BaseModel, ConfigDict, Field, model_validator +from pydantic_settings import BaseSettings, SettingsConfigDict _log = logging.getLogger(__name__) @@ -225,6 +219,8 @@ class PdfPipelineOptions(PipelineOptions): artifacts_path: Optional[Union[Path, str]] = None do_table_structure: bool = True # True: perform table structure extraction do_ocr: bool = True # True: perform OCR, replace programmatic PDF text + do_code_enrichment: bool = False # True: perform code OCR + do_formula_enrichment: bool = False # True: perform formula OCR, return Latex code table_structure_options: TableStructureOptions = TableStructureOptions() ocr_options: Union[ diff --git a/docling/models/base_model.py b/docling/models/base_model.py index 5a98382a..08d728cc 100644 --- a/docling/models/base_model.py +++ b/docling/models/base_model.py @@ -1,7 +1,7 @@ from abc import ABC, abstractmethod from typing import Any, Generic, Iterable, Optional -from docling_core.types.doc import DoclingDocument, NodeItem, TextItem +from docling_core.types.doc import BoundingBox, DoclingDocument, NodeItem, TextItem from typing_extensions import TypeVar from docling.datamodel.base_models import ItemAndImageEnrichmentElement, Page @@ -53,6 +53,7 @@ class BaseItemAndImageEnrichmentModel( ): images_scale: float + expansion_factor: float = 0.0 def prepare_element( self, conv_res: ConversionResult, element: NodeItem @@ -62,8 +63,22 @@ def prepare_element( assert isinstance(element, TextItem) element_prov = element.prov[0] + + bbox = element_prov.bbox + width = bbox.r - bbox.l + height = bbox.t - bbox.b + + # TODO: move to a utility in the BoundingBox class + expanded_bbox = BoundingBox( + l=bbox.l - width * self.expansion_factor, + t=bbox.t + height * self.expansion_factor, + r=bbox.r + width * self.expansion_factor, + b=bbox.b - height * self.expansion_factor, + coord_origin=bbox.coord_origin, + ) + page_ix = element_prov.page_no - 1 cropped_image = conv_res.pages[page_ix].get_image( - scale=self.images_scale, cropbox=element_prov.bbox + scale=self.images_scale, cropbox=expanded_bbox ) return ItemAndImageEnrichmentElement(item=element, image=cropped_image) diff --git a/docling/models/code_formula_model.py b/docling/models/code_formula_model.py new file mode 100644 index 00000000..e4d56945 --- /dev/null +++ b/docling/models/code_formula_model.py @@ -0,0 +1,245 @@ +import re +from pathlib import Path +from typing import Iterable, List, Literal, Optional, Tuple, Union + +from docling_core.types.doc import ( + CodeItem, + DocItemLabel, + DoclingDocument, + NodeItem, + TextItem, +) +from docling_core.types.doc.labels import CodeLanguageLabel +from PIL import Image +from pydantic import BaseModel + +from docling.datamodel.base_models import ItemAndImageEnrichmentElement +from docling.datamodel.pipeline_options import AcceleratorOptions +from docling.models.base_model import BaseItemAndImageEnrichmentModel +from docling.utils.accelerator_utils import decide_device + + +class CodeFormulaModelOptions(BaseModel): + """ + Configuration options for the CodeFormulaModel. + + Attributes + ---------- + kind : str + Type of the model. Fixed value "code_formula". + do_code_enrichment : bool + True if code enrichment is enabled, False otherwise. + do_formula_enrichment : bool + True if formula enrichment is enabled, False otherwise. + """ + + kind: Literal["code_formula"] = "code_formula" + do_code_enrichment: bool = True + do_formula_enrichment: bool = True + + +class CodeFormulaModel(BaseItemAndImageEnrichmentModel): + """ + Model for processing and enriching documents with code and formula predictions. + + Attributes + ---------- + enabled : bool + True if the model is enabled, False otherwise. + options : CodeFormulaModelOptions + Configuration options for the CodeFormulaModel. + code_formula_model : CodeFormulaPredictor + The predictor model for code and formula processing. + + Methods + ------- + __init__(self, enabled, artifacts_path, accelerator_options, code_formula_options) + Initializes the CodeFormulaModel with the given configuration options. + is_processable(self, doc, element) + Determines if a given element in a document can be processed by the model. + __call__(self, doc, element_batch) + Processes the given batch of elements and enriches them with predictions. + """ + + images_scale = 1.66 # = 120 dpi, aligned with training data resolution + expansion_factor = 0.03 + + def __init__( + self, + enabled: bool, + artifacts_path: Optional[Union[Path, str]], + options: CodeFormulaModelOptions, + accelerator_options: AcceleratorOptions, + ): + """ + Initializes the CodeFormulaModel with the given configuration. + + Parameters + ---------- + enabled : bool + True if the model is enabled, False otherwise. + artifacts_path : Path + Path to the directory containing the model artifacts. + options : CodeFormulaModelOptions + Configuration options for the model. + accelerator_options : AcceleratorOptions + Options specifying the device and number of threads for acceleration. + """ + self.enabled = enabled + self.options = options + + if self.enabled: + device = decide_device(accelerator_options.device) + + from docling_ibm_models.code_formula_model.code_formula_predictor import ( + CodeFormulaPredictor, + ) + + if artifacts_path is None: + artifacts_path = self.download_models_hf() + else: + artifacts_path = Path(artifacts_path) + + self.code_formula_model = CodeFormulaPredictor( + artifacts_path=artifacts_path, + device=device, + num_threads=accelerator_options.num_threads, + ) + + @staticmethod + def download_models_hf( + local_dir: Optional[Path] = None, force: bool = False + ) -> Path: + from huggingface_hub import snapshot_download + from huggingface_hub.utils import disable_progress_bars + + disable_progress_bars() + download_path = snapshot_download( + repo_id="ds4sd/CodeFormula", + force_download=force, + local_dir=local_dir, + revision="v1.0.0", + ) + + return Path(download_path) + + def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool: + """ + Determines if a given element in a document can be processed by the model. + + Parameters + ---------- + doc : DoclingDocument + The document being processed. + element : NodeItem + The element within the document to check. + + Returns + ------- + bool + True if the element can be processed, False otherwise. + """ + return self.enabled and ( + (isinstance(element, CodeItem) and self.options.do_code_enrichment) + or ( + isinstance(element, TextItem) + and element.label == DocItemLabel.FORMULA + and self.options.do_formula_enrichment + ) + ) + + def _extract_code_language(self, input_string: str) -> Tuple[str, Optional[str]]: + """Extracts a programming language from the beginning of a string. + + This function checks if the input string starts with a pattern of the form + ``<_some_language_>``. If it does, it extracts the language string and returns + a tuple of (remainder, language). Otherwise, it returns the original string + and `None`. + + Args: + input_string (str): The input string, which may start with ``<_language_>``. + + Returns: + Tuple[str, Optional[str]]: + A tuple where: + - The first element is either: + - The remainder of the string (everything after ``<_language_>``), + if a match is found; or + - The original string, if no match is found. + - The second element is the extracted language if a match is found; + otherwise, `None`. + """ + pattern = r"^<_([^>]+)_>\s*(.*)" + match = re.match(pattern, input_string, flags=re.DOTALL) + if match: + language = str(match.group(1)) # the captured programming language + remainder = str(match.group(2)) # everything after the <_language_> + return remainder, language + else: + return input_string, None + + def _get_code_language_enum(self, value: Optional[str]) -> CodeLanguageLabel: + """ + Converts a string to a corresponding `CodeLanguageLabel` enum member. + + If the provided string does not match any value in `CodeLanguageLabel`, + it defaults to `CodeLanguageLabel.UNKNOWN`. + + Args: + value (Optional[str]): The string representation of the code language or None. + + Returns: + CodeLanguageLabel: The corresponding enum member if the value is valid, + otherwise `CodeLanguageLabel.UNKNOWN`. + """ + if not isinstance(value, str): + return CodeLanguageLabel.UNKNOWN + + try: + return CodeLanguageLabel(value) + except ValueError: + return CodeLanguageLabel.UNKNOWN + + def __call__( + self, + doc: DoclingDocument, + element_batch: Iterable[ItemAndImageEnrichmentElement], + ) -> Iterable[NodeItem]: + """ + Processes the given batch of elements and enriches them with predictions. + + Parameters + ---------- + doc : DoclingDocument + The document being processed. + element_batch : Iterable[ItemAndImageEnrichmentElement] + A batch of elements to be processed. + + Returns + ------- + Iterable[Any] + An iterable of enriched elements. + """ + if not self.enabled: + for element in element_batch: + yield element.item + return + + labels: List[str] = [] + images: List[Image.Image] = [] + elements: List[TextItem] = [] + for el in element_batch: + assert isinstance(el.item, TextItem) + elements.append(el.item) + labels.append(el.item.label) + images.append(el.image) + + outputs = self.code_formula_model.predict(images, labels) + + for item, output in zip(elements, outputs): + if isinstance(item, CodeItem): + output, code_language = self._extract_code_language(output) + item.code_language = self._get_code_language_enum(code_language) + item.text = output + + yield item diff --git a/docling/models/layout_model.py b/docling/models/layout_model.py index c1b7dabe..9fa0ecb4 100644 --- a/docling/models/layout_model.py +++ b/docling/models/layout_model.py @@ -40,7 +40,7 @@ class LayoutModel(BasePageModel): DocItemLabel.PAGE_FOOTER, DocItemLabel.CODE, DocItemLabel.LIST_ITEM, - # "Formula", + DocItemLabel.FORMULA, ] PAGE_HEADER_LABELS = [DocItemLabel.PAGE_HEADER, DocItemLabel.PAGE_FOOTER] diff --git a/docling/models/page_assemble_model.py b/docling/models/page_assemble_model.py index 6239dbfa..4acf8c95 100644 --- a/docling/models/page_assemble_model.py +++ b/docling/models/page_assemble_model.py @@ -135,31 +135,6 @@ def __call__( ) elements.append(fig) body.append(fig) - elif cluster.label == LayoutModel.FORMULA_LABEL: - equation = None - if page.predictions.equations_prediction: - equation = page.predictions.equations_prediction.equation_map.get( - cluster.id, None - ) - if ( - not equation - ): # fallback: add empty formula, if it isn't present - text = self.sanitize_text( - [ - cell.text.replace("\x02", "-").strip() - for cell in cluster.cells - if len(cell.text.strip()) > 0 - ] - ) - equation = TextElement( - label=cluster.label, - id=cluster.id, - cluster=cluster, - page_no=page.page_no, - text=text, - ) - elements.append(equation) - body.append(equation) elif cluster.label in LayoutModel.CONTAINER_LABELS: container_el = ContainerElement( label=cluster.label, diff --git a/docling/models/tesseract_ocr_cli_model.py b/docling/models/tesseract_ocr_cli_model.py index 16e1629d..3d5c8006 100644 --- a/docling/models/tesseract_ocr_cli_model.py +++ b/docling/models/tesseract_ocr_cli_model.py @@ -20,7 +20,6 @@ class TesseractOcrCliModel(BaseOcrModel): - def __init__(self, enabled: bool, options: TesseractCliOcrOptions): super().__init__(enabled=enabled, options=options) self.options: TesseractCliOcrOptions diff --git a/docling/pipeline/base_pipeline.py b/docling/pipeline/base_pipeline.py index 034e6d42..75a08e76 100644 --- a/docling/pipeline/base_pipeline.py +++ b/docling/pipeline/base_pipeline.py @@ -3,7 +3,7 @@ import time import traceback from abc import ABC, abstractmethod -from typing import Callable, Iterable, List +from typing import Any, Callable, Iterable, List from docling_core.types.doc import DoclingDocument, NodeItem @@ -18,7 +18,7 @@ from docling.datamodel.document import ConversionResult, InputDocument from docling.datamodel.pipeline_options import PipelineOptions from docling.datamodel.settings import settings -from docling.models.base_model import BaseEnrichmentModel +from docling.models.base_model import GenericEnrichmentModel from docling.utils.profiling import ProfilingScope, TimeRecorder from docling.utils.utils import chunkify @@ -30,7 +30,7 @@ def __init__(self, pipeline_options: PipelineOptions): self.pipeline_options = pipeline_options self.keep_images = False self.build_pipe: List[Callable] = [] - self.enrichment_pipe: List[BaseEnrichmentModel] = [] + self.enrichment_pipe: List[GenericEnrichmentModel[Any]] = [] def execute(self, in_doc: InputDocument, raises_on_error: bool) -> ConversionResult: conv_res = ConversionResult(input=in_doc) @@ -66,7 +66,7 @@ def _assemble_document(self, conv_res: ConversionResult) -> ConversionResult: def _enrich_document(self, conv_res: ConversionResult) -> ConversionResult: def _prepare_elements( - conv_res: ConversionResult, model: BaseEnrichmentModel + conv_res: ConversionResult, model: GenericEnrichmentModel[Any] ) -> Iterable[NodeItem]: for doc_element, _level in conv_res.document.iterate_items(): prepared_element = model.prepare_element( diff --git a/docling/pipeline/standard_pdf_pipeline.py b/docling/pipeline/standard_pdf_pipeline.py index 758f4e94..97bcc6b6 100644 --- a/docling/pipeline/standard_pdf_pipeline.py +++ b/docling/pipeline/standard_pdf_pipeline.py @@ -1,7 +1,7 @@ import logging import sys from pathlib import Path -from typing import Iterable, Optional +from typing import Optional from docling_core.types.doc import DocItem, ImageRef, PictureItem, TableItem @@ -17,8 +17,8 @@ TesseractCliOcrOptions, TesseractOcrOptions, ) -from docling.models.base_model import BasePageModel from docling.models.base_ocr_model import BaseOcrModel +from docling.models.code_formula_model import CodeFormulaModel, CodeFormulaModelOptions from docling.models.ds_glm_model import GlmModel, GlmOptions from docling.models.easyocr_model import EasyOcrModel from docling.models.layout_model import LayoutModel @@ -93,8 +93,25 @@ def __init__(self, pipeline_options: PdfPipelineOptions): self.enrichment_pipe = [ # Other models working on `NodeItem` elements in the DoclingDocument + # Code Formula Enrichment Model + CodeFormulaModel( + enabled=pipeline_options.do_code_enrichment + or pipeline_options.do_formula_enrichment, + artifacts_path=pipeline_options.artifacts_path, + options=CodeFormulaModelOptions( + do_code_enrichment=pipeline_options.do_code_enrichment, + do_formula_enrichment=pipeline_options.do_formula_enrichment, + ), + accelerator_options=pipeline_options.accelerator_options, + ), ] + if ( + self.pipeline_options.do_formula_enrichment + or self.pipeline_options.do_code_enrichment + ): + self.keep_backend = True + @staticmethod def download_models_hf( local_dir: Optional[Path] = None, force: bool = False diff --git a/docling/utils/glm_utils.py b/docling/utils/glm_utils.py index 1c3b3f67..da29cdd1 100644 --- a/docling/utils/glm_utils.py +++ b/docling/utils/glm_utils.py @@ -270,7 +270,6 @@ def to_docling_document(doc_glm, update_name_label=False) -> DoclingDocument: container_el = doc.add_group(label=group_label) _add_child_elements(container_el, doc, obj, pelem) - elif "text" in obj: text = obj["text"][span_i:span_j] @@ -304,6 +303,10 @@ def to_docling_document(doc_glm, update_name_label=False) -> DoclingDocument: current_list = None doc.add_heading(text=text, prov=prov) + elif label == DocItemLabel.CODE: + current_list = None + + doc.add_code(text=text, prov=prov) else: current_list = None diff --git a/docs/examples/develop_picture_enrichment.py b/docs/examples/develop_picture_enrichment.py index 7ad06e4a..81009fe8 100644 --- a/docs/examples/develop_picture_enrichment.py +++ b/docs/examples/develop_picture_enrichment.py @@ -22,7 +22,6 @@ class ExamplePictureClassifierPipelineOptions(PdfPipelineOptions): class ExamplePictureClassifierEnrichmentModel(BaseEnrichmentModel): - def __init__(self, enabled: bool): self.enabled = enabled @@ -54,7 +53,6 @@ def __call__( class ExamplePictureClassifierPipeline(StandardPdfPipeline): - def __init__(self, pipeline_options: ExamplePictureClassifierPipelineOptions): super().__init__(pipeline_options) self.pipeline_options: ExamplePictureClassifierPipeline diff --git a/poetry.lock b/poetry.lock index 64226c33..73fc85db 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -231,21 +231,6 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"] -[[package]] -name = "autoflake" -version = "2.3.1" -description = "Removes unused imports and unused variables" -optional = false -python-versions = ">=3.8" -files = [ - {file = "autoflake-2.3.1-py3-none-any.whl", hash = "sha256:3ae7495db9084b7b32818b4140e6dc4fc280b712fb414f5b8fe57b0a8e85a840"}, - {file = "autoflake-2.3.1.tar.gz", hash = "sha256:c98b75dc5b0a86459c4f01a1d32ac7eb4338ec4317a4469515ff1e687ecd909e"}, -] - -[package.dependencies] -pyflakes = ">=3.0.0" -tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""} - [[package]] name = "autopep8" version = "2.2.0" @@ -876,13 +861,13 @@ files = [ [[package]] name = "docling-core" -version = 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+version = "2025.1" description = "Provider of IANA time zone data" optional = false python-versions = ">=2" files = [ - {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"}, - {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"}, + {file = "tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639"}, + {file = "tzdata-2025.1.tar.gz", hash = "sha256:24894909e88cdb28bd1636c6887801df64cb485bd593f2fd83ef29075a81d694"}, ] [[package]] @@ -7302,13 +7290,13 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "virtualenv" -version = "20.29.0" +version = "20.29.1" description = "Virtual Python Environment builder" optional = false python-versions = ">=3.8" files = [ - {file = "virtualenv-20.29.0-py3-none-any.whl", hash = "sha256:c12311863497992dc4b8644f8ea82d3b35bb7ef8ee82e6630d76d0197c39baf9"}, - {file = "virtualenv-20.29.0.tar.gz", hash = "sha256:6345e1ff19d4b1296954cee076baaf58ff2a12a84a338c62b02eda39f20aa982"}, + {file = "virtualenv-20.29.1-py3-none-any.whl", hash = "sha256:4e4cb403c0b0da39e13b46b1b2476e505cb0046b25f242bee80f62bf990b2779"}, + {file = "virtualenv-20.29.1.tar.gz", hash = "sha256:b8b8970138d32fb606192cb97f6cd4bb644fa486be9308fb9b63f81091b5dc35"}, ] [package.dependencies] @@ -7763,4 +7751,4 @@ tesserocr = ["tesserocr"] [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "0c0bb9470144867d7f11dec7c3020ac3d4d9ef18b355c0852a9cb89b6270981d" +content-hash = "8bb0b67294a50c0340c5cc02ce60d3608ef4d1968ae50f7e0b8b4c8a26c34734" diff --git a/pyproject.toml b/pyproject.toml index 63f60160..c3e1fa67 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,9 +25,9 @@ packages = [{include = "docling"}] # actual dependencies: ###################### python = "^3.9" -docling-core = { version = "^2.13.1", extras = ["chunking"] } pydantic = "^2.0.0" 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voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Listing 1: Simple Java Program +public static void print() { System.out.println( "Java Code" ); } +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Formula +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt. +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. + \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/code_and_formula.json b/tests/data/groundtruth/docling_v1/code_and_formula.json new file mode 100644 index 00000000..8b90e794 --- /dev/null +++ b/tests/data/groundtruth/docling_v1/code_and_formula.json @@ -0,0 +1 @@ +{"_name": "", "type": "pdf-document", "description": {"title": null, "abstract": null, "authors": null, "affiliations": null, "subjects": null, "keywords": null, "publication_date": null, "languages": null, "license": null, "publishers": null, "url_refs": null, "references": null, "publication": null, "reference_count": null, "citation_count": null, "citation_date": null, "advanced": null, "analytics": null, "logs": [], "collection": null, "acquisition": null}, "file-info": {"filename": "code_and_formula.pdf", "filename-prov": null, "document-hash": "71dd5c52aa241a6d5275d5f681023b6bb3dee3346e171075d1247c87d49e7a82", "#-pages": 2, "collection-name": null, "description": null, "page-hashes": [{"hash": "d3280840ba2dff9749506fce3f05166336b768d2096f6b332fcbfacbc0540edf", "model": "default", "page": 1}, {"hash": "e9aac72f810d71aeb3710b13689d8c55e6eeda390fb497f09e865f1e94f74f2b", "model": "default", "page": 2}]}, "main-text": [{"prov": [{"bbox": [133.76800537109375, 653.6340942382812, 273.4540100097656, 667.99462890625], "page": 1, "span": [0, 17], "__ref_s3_data": null}], "text": "Java Code Example", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [133.76800537109375, 501.4163513183594, 477.48065185546875, 642.8859252929688], "page": 1, "span": [0, 887], "__ref_s3_data": null}], "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [236.17599487304688, 480.4953308105469, 375.069580078125, 490.45794677734375], "page": 1, "span": [0, 30], "__ref_s3_data": null}], "text": "Listing 1: Simple Java Program", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [134.23899841308594, 443.9358215332031, 337.5928649902344, 474.2005310058594], "page": 1, "span": [0, 65], "__ref_s3_data": null}], "text": "public static void print() { System.out.println( \"Java Code\" ); }", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [133.76800537109375, 290.80633544921875, 477.47589111328125, 432.27593994140625], "page": 1, "span": [0, 887], "__ref_s3_data": null}], "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [303.13299560546875, 86.87435150146484, 308.1142883300781, 96.83694458007812], "page": 1, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [133.76800537109375, 703.5241088867188, 191.51429748535156, 717.8846435546875], "page": 2, "span": [0, 7], "__ref_s3_data": null}], "text": "Formula", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [133.76800537109375, 551.3063354492188, 477.48065185546875, 692.7759399414062], "page": 2, "span": [0, 887], "__ref_s3_data": null}], "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [133.76800537109375, 491.53033447265625, 477.4748229980469, 549.3139038085938], "page": 2, "span": [0, 369], "__ref_s3_data": null}], "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.5539855957031, 467.6203308105469, 330.69659423828125, 479.4553527832031], "page": 2, "span": [0, 12], "__ref_s3_data": null}], "text": "a 2 + 8 = 12", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [133.76800537109375, 318.1803283691406, 477.47589111328125, 459.64996337890625], "page": 2, "span": [0, 887], "__ref_s3_data": null}], "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [133.76800537109375, 246.44935607910156, 477.4748229980469, 316.1879577636719], "page": 2, "span": [0, 415], "__ref_s3_data": null}], "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [133.76800537109375, 174.71835327148438, 477.4748229980469, 244.4569549560547], "page": 2, "span": [0, 415], "__ref_s3_data": null}], "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [303.13299560546875, 136.7633514404297, 308.1142883300781, 146.7259521484375], "page": 2, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}], "figures": [], "tables": [], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 792.0, "page": 1, "width": 612.0}, {"height": 841.8900146484375, "page": 2, "width": 595.2760009765625}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/code_and_formula.md b/tests/data/groundtruth/docling_v1/code_and_formula.md new file mode 100644 index 00000000..4d77dc5c --- /dev/null +++ b/tests/data/groundtruth/docling_v1/code_and_formula.md @@ -0,0 +1,19 @@ +## Java Code Example + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +Listing 1: Simple Java Program + +public static void print() { System.out.println( "Java Code" ); } + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +## Formula + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt. + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/code_and_formula.pages.json b/tests/data/groundtruth/docling_v1/code_and_formula.pages.json new file mode 100644 index 00000000..27682fdd --- /dev/null +++ b/tests/data/groundtruth/docling_v1/code_and_formula.pages.json @@ -0,0 +1 @@ +[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Java Code Example", "bbox": {"l": 133.76801, "t": 124.00536999999997, "r": 273.45401, "b": 138.36590999999999, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801, "t": 149.11406999999997, "r": 477.47687, "b": 159.07665999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801, "t": 161.06908999999996, "r": 477.47781000000003, "b": 171.03168000000005, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801, "t": 173.02405, "r": 477.46985, "b": 182.98663, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801, "t": 184.97906, "r": 477.47186, "b": 194.94164999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801, "t": 196.93408, "r": 477.46993999999995, "b": 206.89666999999997, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801, "t": 208.88909999999998, "r": 477.4806500000001, "b": 218.85168, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801, "t": 220.84509000000003, "r": 355.20087, "b": 230.80768, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.60107, "t": 220.84509000000003, "r": 477.47495000000004, "b": 230.80768, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801, "t": 232.80005000000006, "r": 477.47083, "b": 242.76262999999994, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801, "t": 244.75507000000005, "r": 477.47092, "b": 254.71765000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801, "t": 256.71007999999995, "r": 477.46588, "b": 266.67267000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. 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At vero eos et", "bbox": {"l": 133.76801, "t": 256.71007999999995, "r": 477.46588, "b": 266.67267000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801, "t": 268.66510000000005, "r": 477.47586, "b": 278.62769000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801, "t": 280.62103, "r": 351.47672, "b": 290.58365, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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Stet clita kasd gubergren,", "bbox": {"l": 133.76801, "t": 431.45505, "r": 477.47387999999995, "b": 441.41766000000007, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801, "t": 443.41003, "r": 477.47083, "b": 453.37265, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801, "t": 455.36505, "r": 477.47092, "b": 465.32767, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801, "t": 467.32004, "r": 477.46585, "b": 477.28265, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801, "t": 479.27603, "r": 477.47586, "b": 489.23865, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801, "t": 491.23105, "r": 351.47672, "b": 501.19366, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801021944917, "t": 173.0241009553406, "r": 477.46985078329914, "b": 182.9866910103483, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801021944917, "t": 184.97906102134914, "r": 477.4718607833024, "b": 194.94165107635683, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801021944917, "t": 196.93511108736357, "r": 477.4699407832993, "b": 206.89770114237137, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "section_header", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}, "confidence": 0.95060133934021, "cells": [{"id": 0, "text": "Formula", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.4806507833169, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}, "confidence": 0.9878177046775818, "cells": [{"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.47687078331063, "b": 159.0766608783307, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801021944917, "t": 161.06909088933185, "r": 477.4778107833122, "b": 171.03167094433945, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801021944917, "t": 173.0241009553406, "r": 477.46985078329914, "b": 182.9866910103483, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801021944917, "t": 184.97906102134914, "r": 477.4718607833024, "b": 194.94165107635683, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801021944917, "t": 196.93511108736357, "r": 477.4699407832993, "b": 206.89770114237137, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 133.76801021944917, "t": 292.5761116154388, "r": 477.47482078330734, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}, "confidence": 0.9855858683586121, "cells": [{"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "formula", "bbox": {"l": 280.5539904602546, "t": 362.4346620011579, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}, "confidence": 0.8711639046669006, "cells": [{"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 133.76801021944917, "t": 382.24005211051195, "r": 477.47589078330907, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}, "confidence": 0.9880034923553467, "cells": [{"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 133.76801021944917, "t": 525.7020529026275, "r": 477.47482078330734, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}, "confidence": 0.9844803214073181, "cells": [{"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 133.76801021944917, "t": 597.4330632986853, "r": 477.47482078330734, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}, "confidence": 0.9872003793716431, "cells": [{"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "page_footer", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}, "confidence": 0.850279688835144, "cells": [{"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}, "confidence": 0.95060133934021, "cells": [{"id": 0, "text": "Formula", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Formula"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.4806507833169, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}, "confidence": 0.9878177046775818, "cells": [{"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.47687078331063, "b": 159.0766608783307, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801021944917, "t": 161.06909088933185, "r": 477.4778107833122, "b": 171.03167094433945, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801021944917, "t": 173.0241009553406, "r": 477.46985078329914, "b": 182.9866910103483, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801021944917, "t": 184.97906102134914, "r": 477.4718607833024, "b": 194.94165107635683, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801021944917, "t": 196.93511108736357, "r": 477.4699407832993, "b": 206.89770114237137, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 133.76801021944917, "t": 292.5761116154388, "r": 477.47482078330734, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}, "confidence": 0.9855858683586121, "cells": [{"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.5539904602546, "t": 362.4346620011579, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}, "confidence": 0.8711639046669006, "cells": [{"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a 2 + 8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.76801021944917, "t": 382.24005211051195, "r": 477.47589078330907, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}, "confidence": 0.9880034923553467, "cells": [{"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 133.76801021944917, "t": 525.7020529026275, "r": 477.47482078330734, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}, "confidence": 0.9844803214073181, "cells": [{"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 133.76801021944917, "t": 597.4330632986853, "r": 477.47482078330734, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}, "confidence": 0.9872003793716431, "cells": [{"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"label": "page_footer", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}, "confidence": 0.850279688835144, "cells": [{"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}], "body": [{"label": "section_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}, "confidence": 0.95060133934021, "cells": [{"id": 0, "text": "Formula", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Formula"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.4806507833169, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}, "confidence": 0.9878177046775818, "cells": [{"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.47687078331063, "b": 159.0766608783307, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801021944917, "t": 161.06909088933185, "r": 477.4778107833122, "b": 171.03167094433945, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801021944917, "t": 173.0241009553406, "r": 477.46985078329914, "b": 182.9866910103483, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801021944917, "t": 184.97906102134914, "r": 477.4718607833024, "b": 194.94165107635683, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801021944917, "t": 196.93511108736357, "r": 477.4699407832993, "b": 206.89770114237137, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 133.76801021944917, "t": 292.5761116154388, "r": 477.47482078330734, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}, "confidence": 0.9855858683586121, "cells": [{"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.5539904602546, "t": 362.4346620011579, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}, "confidence": 0.8711639046669006, "cells": [{"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a 2 + 8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.76801021944917, "t": 382.24005211051195, "r": 477.47589078330907, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}, "confidence": 0.9880034923553467, "cells": [{"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 133.76801021944917, "t": 525.7020529026275, "r": 477.47482078330734, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}, "confidence": 0.9844803214073181, "cells": [{"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 133.76801021944917, "t": 597.4330632986853, "r": 477.47482078330734, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}, "confidence": 0.9872003793716431, "cells": [{"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}], "headers": [{"label": "page_footer", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}, "confidence": 0.850279688835144, "cells": [{"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/code_and_formula.doctags.txt b/tests/data/groundtruth/docling_v2/code_and_formula.doctags.txt new file mode 100644 index 00000000..ad417540 --- /dev/null +++ b/tests/data/groundtruth/docling_v2/code_and_formula.doctags.txt @@ -0,0 +1,14 @@ + +Java Code Example +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Listing 1: Simple Java Program +public static void print() { System.out.println( "Java Code" ); } +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Formula +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt. +a 2 + 8 = 12 +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. + \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/code_and_formula.json b/tests/data/groundtruth/docling_v2/code_and_formula.json new file mode 100644 index 00000000..adcc14d1 --- /dev/null +++ b/tests/data/groundtruth/docling_v2/code_and_formula.json @@ -0,0 +1 @@ +{"schema_name": "DoclingDocument", "version": "1.0.0", "name": "code_and_formula", "origin": {"mimetype": "application/pdf", "binary_hash": 2394749058180317456, "filename": "code_and_formula.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/texts/5"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/texts/8"}, {"cref": "#/texts/9"}, {"cref": "#/texts/10"}, {"cref": "#/texts/11"}, {"cref": "#/texts/12"}, {"cref": "#/texts/13"}], "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 133.76800537109375, "t": 667.99462890625, "r": 273.4540100097656, "b": 653.6340942382812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Java Code Example", "text": "Java Code Example", "level": 1}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 133.76800537109375, "t": 642.8859252929688, "r": 477.48065185546875, "b": 501.4163513183594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 887]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 303.13299560546875, "t": 96.83694458007812, "r": 308.1142883300781, "b": 86.87435150146484, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 717.8846435546875, "r": 191.51429748535156, "b": 703.5241088867188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Formula", "text": "Formula", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 692.7759399414062, "r": 477.48065185546875, "b": 551.3063354492188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 887]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 549.3139038085938, "r": 477.4748229980469, "b": 491.53033447265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 369]}], "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt.", "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 2, "bbox": {"l": 280.5539855957031, "t": 479.4553527832031, "r": 330.69659423828125, "b": 467.6203308105469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "a 2 + 8 = 12", "text": "a 2 + 8 = 12"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 459.64996337890625, "r": 477.47589111328125, "b": 318.1803283691406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 887]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 316.1879577636719, "r": 477.4748229980469, "b": 246.44935607910156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 415]}], "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.", "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 244.4569549560547, "r": 477.4748229980469, "b": 174.71835327148438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 415]}], "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.", "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 303.13299560546875, "t": 146.7259521484375, "r": 308.1142883300781, "b": 136.7633514404297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}], "pictures": [], "tables": [], "key_value_items": [], "pages": {"1": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 1}, "2": {"size": {"width": 595.2760009765625, "height": 841.8900146484375}, "image": null, "page_no": 2}}} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/code_and_formula.md b/tests/data/groundtruth/docling_v2/code_and_formula.md new file mode 100644 index 00000000..2e2636a3 --- /dev/null +++ b/tests/data/groundtruth/docling_v2/code_and_formula.md @@ -0,0 +1,25 @@ +## Java Code Example + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +Listing 1: Simple Java Program + +``` +public static void print() { System.out.println( "Java Code" ); } +``` + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +## Formula + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt. + +a 2 + 8 = 12 + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. + +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/code_and_formula.pages.json b/tests/data/groundtruth/docling_v2/code_and_formula.pages.json new file mode 100644 index 00000000..27682fdd --- /dev/null +++ b/tests/data/groundtruth/docling_v2/code_and_formula.pages.json @@ -0,0 +1 @@ +[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Java Code Example", "bbox": {"l": 133.76801, "t": 124.00536999999997, "r": 273.45401, "b": 138.36590999999999, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801, "t": 149.11406999999997, "r": 477.47687, "b": 159.07665999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801, "t": 161.06908999999996, "r": 477.47781000000003, "b": 171.03168000000005, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801, "t": 173.02405, "r": 477.46985, "b": 182.98663, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801, "t": 184.97906, "r": 477.47186, "b": 194.94164999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801, "t": 196.93408, "r": 477.46993999999995, "b": 206.89666999999997, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801, "t": 208.88909999999998, "r": 477.4806500000001, "b": 218.85168, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801, "t": 220.84509000000003, "r": 355.20087, "b": 230.80768, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.60107, "t": 220.84509000000003, "r": 477.47495000000004, "b": 230.80768, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801, "t": 232.80005000000006, "r": 477.47083, "b": 242.76262999999994, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801, "t": 244.75507000000005, "r": 477.47092, "b": 254.71765000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801, "t": 256.71007999999995, "r": 477.46588, "b": 266.67267000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801, "t": 268.66510000000005, "r": 477.47586, "b": 278.62769000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801, "t": 280.62103, "r": 351.47672, "b": 290.58365, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Listing 1: Simple Java Program", "bbox": {"l": 236.17598999999998, "t": 301.54204999999996, "r": 375.06958, "b": 311.50467, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "public static void", "bbox": {"l": 134.575, "t": 317.79947000000004, "r": 235.44695, "b": 326.14719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "print() {", "bbox": {"l": 241.8759, "t": 317.79947000000004, "r": 292.24908, "b": 326.14719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "System.out.println(", "bbox": {"l": 157.17101, "t": 328.75745, "r": 264.02368, "b": 337.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "\"Java Code\"", "bbox": {"l": 264.15811, "t": 328.75745, "r": 325.35376, "b": 337.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ");", "bbox": {"l": 326.2952, "t": 328.75745, "r": 337.59286, "b": 337.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "}", "bbox": {"l": 134.239, "t": 339.71646, "r": 138.94637, "b": 348.06418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201, "t": 359.72406, "r": 477.46985, "b": 369.68667999999997, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801, "t": 371.67905, "r": 477.4758, "b": 381.64166000000006, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801, "t": 383.63403, "r": 477.4758, "b": 393.59665, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801, "t": 395.58905, "r": 477.47284, "b": 405.55167, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801, "t": 407.54404, "r": 477.47589, "b": 417.50665, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801, "t": 419.50003000000004, "r": 477.47177000000005, "b": 429.46265, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801, "t": 431.45505, "r": 477.47387999999995, "b": 441.41766000000007, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801, "t": 443.41003, "r": 477.47083, "b": 453.37265, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801, "t": 455.36505, "r": 477.47092, "b": 465.32767, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801, "t": 467.32004, "r": 477.46585, "b": 477.28265, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801, "t": 479.27603, "r": 477.47586, "b": 489.23865, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801, "t": 491.23105, "r": 351.47672, "b": 501.19366, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1", "bbox": {"l": 303.133, "t": 695.163055, "r": 308.11429, "b": 705.125648, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "section_header", "bbox": {"l": 133.76801, "t": 124.00536999999997, "r": 273.45401, "b": 138.36590999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9581764936447144, "cells": [{"id": 0, "text": "Java Code Example", "bbox": {"l": 133.76801, "t": 124.00536999999997, "r": 273.45401, "b": 138.36590999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 133.76801, "t": 149.11406999999997, "r": 477.4806500000001, "b": 290.58365, "coord_origin": "TOPLEFT"}, "confidence": 0.9872456789016724, "cells": [{"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801, "t": 149.11406999999997, "r": 477.47687, "b": 159.07665999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801, "t": 161.06908999999996, "r": 477.47781000000003, "b": 171.03168000000005, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801, "t": 173.02405, "r": 477.46985, "b": 182.98663, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801, "t": 184.97906, "r": 477.47186, "b": 194.94164999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801, "t": 196.93408, "r": 477.46993999999995, "b": 206.89666999999997, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. 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Stet clita kasd gubergren,", "bbox": {"l": 133.76801, "t": 431.45505, "r": 477.47387999999995, "b": 441.41766000000007, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801, "t": 443.41003, "r": 477.47083, "b": 453.37265, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801, "t": 455.36505, "r": 477.47092, "b": 465.32767, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801, "t": 467.32004, "r": 477.46585, "b": 477.28265, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801, "t": 479.27603, "r": 477.47586, "b": 489.23865, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801, "t": 491.23105, "r": 351.47672, "b": 501.19366, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "section_header", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}, "confidence": 0.95060133934021, "cells": [{"id": 0, "text": "Formula", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.4806507833169, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}, "confidence": 0.9878177046775818, "cells": [{"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.47687078331063, "b": 159.0766608783307, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801021944917, "t": 161.06909088933185, "r": 477.4778107833122, "b": 171.03167094433945, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. 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Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 133.76801021944917, "t": 292.5761116154388, "r": 477.47482078330734, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}, "confidence": 0.9855858683586121, "cells": [{"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "formula", "bbox": {"l": 280.5539904602546, "t": 362.4346620011579, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}, "confidence": 0.8711639046669006, "cells": [{"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 133.76801021944917, "t": 382.24005211051195, "r": 477.47589078330907, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}, "confidence": 0.9880034923553467, "cells": [{"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 133.76801021944917, "t": 525.7020529026275, "r": 477.47482078330734, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}, "confidence": 0.9844803214073181, "cells": [{"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 133.76801021944917, "t": 597.4330632986853, "r": 477.47482078330734, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}, "confidence": 0.9872003793716431, "cells": [{"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "page_footer", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}, "confidence": 0.850279688835144, "cells": [{"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}, "confidence": 0.95060133934021, "cells": [{"id": 0, "text": "Formula", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Formula"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.4806507833169, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}, "confidence": 0.9878177046775818, "cells": [{"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.47687078331063, "b": 159.0766608783307, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801021944917, "t": 161.06909088933185, "r": 477.4778107833122, "b": 171.03167094433945, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801021944917, "t": 173.0241009553406, "r": 477.46985078329914, "b": 182.9866910103483, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801021944917, "t": 184.97906102134914, "r": 477.4718607833024, "b": 194.94165107635683, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801021944917, "t": 196.93511108736357, "r": 477.4699407832993, "b": 206.89770114237137, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 133.76801021944917, "t": 292.5761116154388, "r": 477.47482078330734, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}, "confidence": 0.9855858683586121, "cells": [{"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.5539904602546, "t": 362.4346620011579, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}, "confidence": 0.8711639046669006, "cells": [{"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a 2 + 8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.76801021944917, "t": 382.24005211051195, "r": 477.47589078330907, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}, "confidence": 0.9880034923553467, "cells": [{"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 133.76801021944917, "t": 525.7020529026275, "r": 477.47482078330734, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}, "confidence": 0.9844803214073181, "cells": [{"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 133.76801021944917, "t": 597.4330632986853, "r": 477.47482078330734, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}, "confidence": 0.9872003793716431, "cells": [{"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"label": "page_footer", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}, "confidence": 0.850279688835144, "cells": [{"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}], "body": [{"label": "section_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}, "confidence": 0.95060133934021, "cells": [{"id": 0, "text": "Formula", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Formula"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.4806507833169, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}, "confidence": 0.9878177046775818, "cells": [{"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.47687078331063, "b": 159.0766608783307, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801021944917, "t": 161.06909088933185, "r": 477.4778107833122, "b": 171.03167094433945, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801021944917, "t": 173.0241009553406, "r": 477.46985078329914, "b": 182.9866910103483, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801021944917, "t": 184.97906102134914, "r": 477.4718607833024, "b": 194.94165107635683, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801021944917, "t": 196.93511108736357, "r": 477.4699407832993, "b": 206.89770114237137, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 133.76801021944917, "t": 292.5761116154388, "r": 477.47482078330734, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}, "confidence": 0.9855858683586121, "cells": [{"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.5539904602546, "t": 362.4346620011579, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}, "confidence": 0.8711639046669006, "cells": [{"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a 2 + 8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.76801021944917, "t": 382.24005211051195, "r": 477.47589078330907, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}, "confidence": 0.9880034923553467, "cells": [{"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 133.76801021944917, "t": 525.7020529026275, "r": 477.47482078330734, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}, "confidence": 0.9844803214073181, "cells": [{"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 133.76801021944917, "t": 597.4330632986853, "r": 477.47482078330734, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}, "confidence": 0.9872003793716431, "cells": [{"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}], "headers": [{"label": "page_footer", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}, "confidence": 0.850279688835144, "cells": [{"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}]}}] \ No newline at end of file diff --git a/tests/test_code_formula.py b/tests/test_code_formula.py new file mode 100644 index 00000000..05e87246 --- /dev/null +++ b/tests/test_code_formula.py @@ -0,0 +1,66 @@ +from pathlib import Path + +from docling_core.types.doc import CodeItem, TextItem +from docling_core.types.doc.labels import CodeLanguageLabel, DocItemLabel + +from docling.backend.docling_parse_backend import DoclingParseDocumentBackend +from docling.backend.docling_parse_v2_backend import DoclingParseV2DocumentBackend +from docling.datamodel.base_models import InputFormat +from docling.datamodel.document import ConversionResult +from docling.datamodel.pipeline_options import PdfPipelineOptions +from docling.document_converter import DocumentConverter, PdfFormatOption +from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline + + +def get_converter(): + + pipeline_options = PdfPipelineOptions() + pipeline_options.generate_page_images = True + + pipeline_options.do_ocr = False + pipeline_options.do_table_structure = False + pipeline_options.do_code_enrichment = True + pipeline_options.do_formula_enrichment = True + + converter = DocumentConverter( + format_options={ + InputFormat.PDF: PdfFormatOption( + backend=DoclingParseV2DocumentBackend, + pipeline_cls=StandardPdfPipeline, + pipeline_options=pipeline_options, + ) + } + ) + + return converter + + +def test_code_and_formula_conversion(): + pdf_path = Path("tests/data/code_and_formula.pdf") + converter = get_converter() + + print(f"converting {pdf_path}") + + doc_result: ConversionResult = converter.convert(pdf_path) + + results = doc_result.document.texts + + code_blocks = [el for el in results if isinstance(el, CodeItem)] + assert len(code_blocks) == 1 + + gt = 'public static void print() {\n System.out.println("Java Code");\n}' + + predicted = code_blocks[0].text.strip() + assert predicted == gt, f"mismatch in text {predicted=}, {gt=}" + assert code_blocks[0].code_language == CodeLanguageLabel.JAVA + + formula_blocks = [ + el + for el in results + if isinstance(el, TextItem) and el.label == DocItemLabel.FORMULA + ] + assert len(formula_blocks) == 1 + + gt = "a ^ { 2 } + 8 = 1 2" + predicted = formula_blocks[0].text + assert predicted == gt, f"mismatch in text {predicted=}, {gt=}" From e9768ae6a5910e19ed95917c1888651289a70720 Mon Sep 17 00:00:00 2001 From: Yusik Kim <107410898+kmyusk@users.noreply.github.com> Date: Fri, 24 Jan 2025 17:35:29 +0100 Subject: [PATCH 04/12] chore: expose draw_clusters function (#803) feat: expose draw_clusters function add type annotations to function signature Signed-off-by: Yusik Kim --- docling/models/layout_model.py | 94 ++++------------------------------ docling/utils/visualization.py | 80 +++++++++++++++++++++++++++++ 2 files changed, 89 insertions(+), 85 deletions(-) create mode 100644 docling/utils/visualization.py diff --git a/docling/models/layout_model.py b/docling/models/layout_model.py index 9fa0ecb4..69193c94 100644 --- a/docling/models/layout_model.py +++ b/docling/models/layout_model.py @@ -1,28 +1,21 @@ import copy import logging -import random -import time from pathlib import Path -from typing import Iterable, List +from typing import Iterable -from docling_core.types.doc import CoordOrigin, DocItemLabel +from docling_core.types.doc import DocItemLabel from docling_ibm_models.layoutmodel.layout_predictor import LayoutPredictor -from PIL import Image, ImageDraw, ImageFont - -from docling.datamodel.base_models import ( - BoundingBox, - Cell, - Cluster, - LayoutPrediction, - Page, -) +from PIL import Image + +from docling.datamodel.base_models import BoundingBox, Cluster, LayoutPrediction, Page from docling.datamodel.document import ConversionResult -from docling.datamodel.pipeline_options import AcceleratorDevice, AcceleratorOptions +from docling.datamodel.pipeline_options import AcceleratorOptions from docling.datamodel.settings import settings from docling.models.base_model import BasePageModel from docling.utils.accelerator_utils import decide_device from docling.utils.layout_postprocessor import LayoutPostprocessor from docling.utils.profiling import TimeRecorder +from docling.utils.visualization import draw_clusters _log = logging.getLogger(__name__) @@ -82,78 +75,9 @@ def draw_clusters_and_cells_side_by_side( left_image = copy.deepcopy(page.image) right_image = copy.deepcopy(page.image) - # Function to draw clusters on an image - def draw_clusters(image, clusters): - draw = ImageDraw.Draw(image, "RGBA") - # Create a smaller font for the labels - try: - font = ImageFont.truetype("arial.ttf", 12) - except OSError: - # Fallback to default font if arial is not available - font = ImageFont.load_default() - for c_tl in clusters: - all_clusters = [c_tl, *c_tl.children] - for c in all_clusters: - # Draw cells first (underneath) - cell_color = (0, 0, 0, 40) # Transparent black for cells - for tc in c.cells: - cx0, cy0, cx1, cy1 = tc.bbox.as_tuple() - cx0 *= scale_x - cx1 *= scale_x - cy0 *= scale_x - cy1 *= scale_y - - draw.rectangle( - [(cx0, cy0), (cx1, cy1)], - outline=None, - fill=cell_color, - ) - # Draw cluster rectangle - x0, y0, x1, y1 = c.bbox.as_tuple() - x0 *= scale_x - x1 *= scale_x - y0 *= scale_x - y1 *= scale_y - - cluster_fill_color = (*list(DocItemLabel.get_color(c.label)), 70) - cluster_outline_color = ( - *list(DocItemLabel.get_color(c.label)), - 255, - ) - draw.rectangle( - [(x0, y0), (x1, y1)], - outline=cluster_outline_color, - fill=cluster_fill_color, - ) - # Add label name and confidence - label_text = f"{c.label.name} ({c.confidence:.2f})" - # Create semi-transparent background for text - text_bbox = draw.textbbox((x0, y0), label_text, font=font) - text_bg_padding = 2 - draw.rectangle( - [ - ( - text_bbox[0] - text_bg_padding, - text_bbox[1] - text_bg_padding, - ), - ( - text_bbox[2] + text_bg_padding, - text_bbox[3] + text_bg_padding, - ), - ], - fill=(255, 255, 255, 180), # Semi-transparent white - ) - # Draw text - draw.text( - (x0, y0), - label_text, - fill=(0, 0, 0, 255), # Solid black - font=font, - ) - # Draw clusters on both images - draw_clusters(left_image, left_clusters) - draw_clusters(right_image, right_clusters) + draw_clusters(left_image, left_clusters, scale_x, scale_y) + draw_clusters(right_image, right_clusters, scale_x, scale_y) # Combine the images side by side combined_width = left_image.width * 2 combined_height = left_image.height diff --git a/docling/utils/visualization.py b/docling/utils/visualization.py new file mode 100644 index 00000000..465b7749 --- /dev/null +++ b/docling/utils/visualization.py @@ -0,0 +1,80 @@ +from docling_core.types.doc import DocItemLabel +from PIL import Image, ImageDraw, ImageFont +from PIL.ImageFont import FreeTypeFont + +from docling.datamodel.base_models import Cluster + + +def draw_clusters( + image: Image.Image, clusters: list[Cluster], scale_x: float, scale_y: float +) -> None: + """ + Draw clusters on an image + """ + draw = ImageDraw.Draw(image, "RGBA") + # Create a smaller font for the labels + font: ImageFont.ImageFont | FreeTypeFont + try: + font = ImageFont.truetype("arial.ttf", 12) + except OSError: + # Fallback to default font if arial is not available + font = ImageFont.load_default() + for c_tl in clusters: + all_clusters = [c_tl, *c_tl.children] + for c in all_clusters: + # Draw cells first (underneath) + cell_color = (0, 0, 0, 40) # Transparent black for cells + for tc in c.cells: + cx0, cy0, cx1, cy1 = tc.bbox.as_tuple() + cx0 *= scale_x + cx1 *= scale_x + cy0 *= scale_x + cy1 *= scale_y + + draw.rectangle( + [(cx0, cy0), (cx1, cy1)], + outline=None, + fill=cell_color, + ) + # Draw cluster rectangle + x0, y0, x1, y1 = c.bbox.as_tuple() + x0 *= scale_x + x1 *= scale_x + y0 *= scale_x + y1 *= scale_y + + cluster_fill_color = (*list(DocItemLabel.get_color(c.label)), 70) + cluster_outline_color = ( + *list(DocItemLabel.get_color(c.label)), + 255, + ) + draw.rectangle( + [(x0, y0), (x1, y1)], + outline=cluster_outline_color, + fill=cluster_fill_color, + ) + # Add label name and confidence + label_text = f"{c.label.name} ({c.confidence:.2f})" + # Create semi-transparent background for text + text_bbox = draw.textbbox((x0, y0), label_text, font=font) + text_bg_padding = 2 + draw.rectangle( + [ + ( + text_bbox[0] - text_bg_padding, + text_bbox[1] - text_bg_padding, + ), + ( + text_bbox[2] + text_bg_padding, + text_bbox[3] + text_bg_padding, + ), + ], + fill=(255, 255, 255, 180), # Semi-transparent white + ) + # Draw text + draw.text( + (x0, y0), + label_text, + fill=(0, 0, 0, 255), # Solid black + font=font, + ) From 88a0e66adc19238f57a942b0504926cdaeacd8cc Mon Sep 17 00:00:00 2001 From: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Date: Fri, 24 Jan 2025 18:05:23 +0100 Subject: [PATCH 05/12] feat: add Docling JSON ingestion (#783) * feat: add Docling JSON ingestion Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> * update conversion as per review comments, add tests, revert Docling JSON disambiguation, document intricacies Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> * Update docling/backend/json/docling_json_backend.py Co-authored-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> --------- Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Co-authored-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> --- docling/backend/abstract_backend.py | 1 - docling/backend/json/__init__.py | 0 docling/backend/json/docling_json_backend.py | 58 ++++++++++++++++++++ docling/datamodel/base_models.py | 3 + docling/datamodel/document.py | 2 + docling/document_converter.py | 4 ++ tests/test_backend_docling_json.py | 58 ++++++++++++++++++++ tests/test_input_doc.py | 19 +++++++ 8 files changed, 144 insertions(+), 1 deletion(-) create mode 100644 docling/backend/json/__init__.py create mode 100644 docling/backend/json/docling_json_backend.py create mode 100644 tests/test_backend_docling_json.py diff --git a/docling/backend/abstract_backend.py b/docling/backend/abstract_backend.py index b47b11cd..491330b3 100644 --- a/docling/backend/abstract_backend.py +++ b/docling/backend/abstract_backend.py @@ -27,7 +27,6 @@ def is_valid(self) -> bool: def supports_pagination(cls) -> bool: pass - @abstractmethod def unload(self): if isinstance(self.path_or_stream, BytesIO): self.path_or_stream.close() diff --git a/docling/backend/json/__init__.py b/docling/backend/json/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/docling/backend/json/docling_json_backend.py b/docling/backend/json/docling_json_backend.py new file mode 100644 index 00000000..73ac6972 --- /dev/null +++ b/docling/backend/json/docling_json_backend.py @@ -0,0 +1,58 @@ +from io import BytesIO +from pathlib import Path +from typing import Union + +from docling_core.types.doc import DoclingDocument +from typing_extensions import override + +from docling.backend.abstract_backend import DeclarativeDocumentBackend +from docling.datamodel.base_models import InputFormat +from docling.datamodel.document import InputDocument + + +class DoclingJSONBackend(DeclarativeDocumentBackend): + @override + def __init__( + self, in_doc: InputDocument, path_or_stream: Union[BytesIO, Path] + ) -> None: + super().__init__(in_doc, path_or_stream) + + # given we need to store any actual conversion exception for raising it from + # convert(), this captures the successful result or the actual error in a + # mutually exclusive way: + self._doc_or_err = self._get_doc_or_err() + + @override + def is_valid(self) -> bool: + return isinstance(self._doc_or_err, DoclingDocument) + + @classmethod + @override + def supports_pagination(cls) -> bool: + return False + + @classmethod + @override + def supported_formats(cls) -> set[InputFormat]: + return {InputFormat.JSON_DOCLING} + + def _get_doc_or_err(self) -> Union[DoclingDocument, Exception]: + try: + json_data: Union[str, bytes] + if isinstance(self.path_or_stream, Path): + with open(self.path_or_stream, encoding="utf-8") as f: + json_data = f.read() + elif isinstance(self.path_or_stream, BytesIO): + json_data = self.path_or_stream.getvalue() + else: + raise RuntimeError(f"Unexpected: {type(self.path_or_stream)=}") + return DoclingDocument.model_validate_json(json_data=json_data) + except Exception as e: + return e + + @override + def convert(self) -> DoclingDocument: + if isinstance(self._doc_or_err, DoclingDocument): + return self._doc_or_err + else: + raise self._doc_or_err diff --git a/docling/datamodel/base_models.py b/docling/datamodel/base_models.py index 99d30108..d1e7ce3a 100644 --- a/docling/datamodel/base_models.py +++ b/docling/datamodel/base_models.py @@ -41,6 +41,7 @@ class InputFormat(str, Enum): MD = "md" XLSX = "xlsx" XML_USPTO = "xml_uspto" + JSON_DOCLING = "json_docling" class OutputFormat(str, Enum): @@ -62,6 +63,7 @@ class OutputFormat(str, Enum): InputFormat.ASCIIDOC: ["adoc", "asciidoc", "asc"], InputFormat.XLSX: ["xlsx"], InputFormat.XML_USPTO: ["xml", "txt"], + InputFormat.JSON_DOCLING: ["json"], } FormatToMimeType: Dict[InputFormat, List[str]] = { @@ -90,6 +92,7 @@ class OutputFormat(str, Enum): "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ], InputFormat.XML_USPTO: ["application/xml", "text/plain"], + InputFormat.JSON_DOCLING: ["application/json"], } MimeTypeToFormat: dict[str, list[InputFormat]] = { diff --git a/docling/datamodel/document.py b/docling/datamodel/document.py index 136428e8..a2a93aa3 100644 --- a/docling/datamodel/document.py +++ b/docling/datamodel/document.py @@ -350,6 +350,8 @@ def _mime_from_extension(ext): mime = FormatToMimeType[InputFormat.HTML][0] elif ext in FormatToExtensions[InputFormat.MD]: mime = FormatToMimeType[InputFormat.MD][0] + elif ext in FormatToExtensions[InputFormat.JSON_DOCLING]: + mime = FormatToMimeType[InputFormat.JSON_DOCLING][0] return mime @staticmethod diff --git a/docling/document_converter.py b/docling/document_converter.py index cb073949..13203ea7 100644 --- a/docling/document_converter.py +++ b/docling/document_converter.py @@ -11,6 +11,7 @@ from docling.backend.asciidoc_backend import AsciiDocBackend from docling.backend.docling_parse_v2_backend import DoclingParseV2DocumentBackend from docling.backend.html_backend import HTMLDocumentBackend +from docling.backend.json.docling_json_backend import DoclingJSONBackend from docling.backend.md_backend import MarkdownDocumentBackend from docling.backend.msexcel_backend import MsExcelDocumentBackend from docling.backend.mspowerpoint_backend import MsPowerpointDocumentBackend @@ -136,6 +137,9 @@ def _get_default_option(format: InputFormat) -> FormatOption: InputFormat.PDF: FormatOption( pipeline_cls=StandardPdfPipeline, backend=DoclingParseV2DocumentBackend ), + InputFormat.JSON_DOCLING: FormatOption( + pipeline_cls=SimplePipeline, backend=DoclingJSONBackend + ), } if (options := format_to_default_options.get(format)) is not None: return options diff --git a/tests/test_backend_docling_json.py b/tests/test_backend_docling_json.py new file mode 100644 index 00000000..a38d9da1 --- /dev/null +++ b/tests/test_backend_docling_json.py @@ -0,0 +1,58 @@ +"""Test methods in module docling.backend.json.docling_json_backend.py.""" + +from io import BytesIO +from pathlib import Path + +import pytest +from pydantic import ValidationError + +from docling.backend.json.docling_json_backend import DoclingJSONBackend +from docling.datamodel.base_models import InputFormat +from docling.datamodel.document import DoclingDocument, InputDocument + +GT_PATH: Path = Path("./tests/data/groundtruth/docling_v2/2206.01062.json") + + +def test_convert_valid_docling_json(): + """Test ingestion of valid Docling JSON.""" + cls = DoclingJSONBackend + path_or_stream = GT_PATH + in_doc = InputDocument( + path_or_stream=path_or_stream, + format=InputFormat.JSON_DOCLING, + backend=cls, + ) + backend = cls( + in_doc=in_doc, + path_or_stream=path_or_stream, + ) + assert backend.is_valid() + + act_doc = backend.convert() + act_data = act_doc.export_to_dict() + + exp_doc = DoclingDocument.load_from_json(GT_PATH) + exp_data = exp_doc.export_to_dict() + + assert act_data == exp_data + + +def test_invalid_docling_json(): + """Test ingestion of invalid Docling JSON.""" + cls = DoclingJSONBackend + path_or_stream = BytesIO(b"{}") + in_doc = InputDocument( + path_or_stream=path_or_stream, + format=InputFormat.JSON_DOCLING, + backend=cls, + filename="foo", + ) + backend = cls( + in_doc=in_doc, + path_or_stream=path_or_stream, + ) + + assert not backend.is_valid() + + with pytest.raises(ValidationError): + backend.convert() diff --git a/tests/test_input_doc.py b/tests/test_input_doc.py index 8b084667..f6c516aa 100644 --- a/tests/test_input_doc.py +++ b/tests/test_input_doc.py @@ -124,6 +124,25 @@ def test_guess_format(tmp_path): doc_path.write_text("xyz", encoding="utf-8") assert dci._guess_format(doc_path) == None + # Valid Docling JSON + test_str = '{"name": ""}' + stream = DocumentStream(name="test.json", stream=BytesIO(f"{test_str}".encode())) + assert dci._guess_format(stream) == InputFormat.JSON_DOCLING + doc_path = temp_dir / "test.json" + doc_path.write_text(test_str, encoding="utf-8") + assert dci._guess_format(doc_path) == InputFormat.JSON_DOCLING + + # Non-Docling JSON + # TODO: Docling JSON is currently the single supported JSON flavor and the pipeline + # will try to validate *any* JSON (based on suffix/MIME) as Docling JSON; proper + # disambiguation seen as part of https://github.com/DS4SD/docling/issues/802 + test_str = "{}" + stream = DocumentStream(name="test.json", stream=BytesIO(f"{test_str}".encode())) + assert dci._guess_format(stream) == InputFormat.JSON_DOCLING + doc_path = temp_dir / "test.json" + doc_path.write_text(test_str, encoding="utf-8") + assert dci._guess_format(doc_path) == InputFormat.JSON_DOCLING + def _make_input_doc(path): in_doc = InputDocument( From 16a218d871c48fd9cc636b77f7b597dc40cbeeec Mon Sep 17 00:00:00 2001 From: Matteo <43417658+Matteo-Omenetti@users.noreply.github.com> Date: Fri, 24 Jan 2025 18:05:51 +0100 Subject: [PATCH 06/12] feat: New document picture classifier (#805) * figure classifier Signed-off-by: Matteo Omenetti * gt for e2e tests Signed-off-by: Matteo Omenetti * tests Signed-off-by: Matteo Omenetti --------- Signed-off-by: Matteo Omenetti --- docling/datamodel/pipeline_options.py | 1 + docling/models/document_picture_classifier.py | 187 ++++++++++++++++++ docling/pipeline/standard_pdf_pipeline.py | 11 ++ poetry.lock | 40 ++-- pyproject.toml | 2 +- .../picture_classification.doctags.txt | 17 ++ .../docling_v1/picture_classification.json | 1 + .../docling_v1/picture_classification.md | 15 ++ .../picture_classification.pages.json | 1 + .../picture_classification.doctags.txt | 15 ++ .../docling_v2/picture_classification.json | 1 + .../docling_v2/picture_classification.md | 17 ++ .../picture_classification.pages.json | 1 + tests/data/picture_classification.pdf | Bin 0 -> 212855 bytes tests/test_document_picture_classifier.py | 81 ++++++++ 15 files changed, 369 insertions(+), 21 deletions(-) create mode 100644 docling/models/document_picture_classifier.py create mode 100644 tests/data/groundtruth/docling_v1/picture_classification.doctags.txt create mode 100644 tests/data/groundtruth/docling_v1/picture_classification.json create mode 100644 tests/data/groundtruth/docling_v1/picture_classification.md create mode 100644 tests/data/groundtruth/docling_v1/picture_classification.pages.json create mode 100644 tests/data/groundtruth/docling_v2/picture_classification.doctags.txt create mode 100644 tests/data/groundtruth/docling_v2/picture_classification.json create mode 100644 tests/data/groundtruth/docling_v2/picture_classification.md create mode 100644 tests/data/groundtruth/docling_v2/picture_classification.pages.json create mode 100644 tests/data/picture_classification.pdf create mode 100644 tests/test_document_picture_classifier.py diff --git a/docling/datamodel/pipeline_options.py b/docling/datamodel/pipeline_options.py index efdf3b1c..00ab7b41 100644 --- a/docling/datamodel/pipeline_options.py +++ b/docling/datamodel/pipeline_options.py @@ -221,6 +221,7 @@ class PdfPipelineOptions(PipelineOptions): do_ocr: bool = True # True: perform OCR, replace programmatic PDF text do_code_enrichment: bool = False # True: perform code OCR do_formula_enrichment: bool = False # True: perform formula OCR, return Latex code + do_picture_classification: bool = False # True: classify pictures in documents table_structure_options: TableStructureOptions = TableStructureOptions() ocr_options: Union[ diff --git a/docling/models/document_picture_classifier.py b/docling/models/document_picture_classifier.py new file mode 100644 index 00000000..6e2d90b4 --- /dev/null +++ b/docling/models/document_picture_classifier.py @@ -0,0 +1,187 @@ +from pathlib import Path +from typing import Iterable, List, Literal, Optional, Tuple, Union + +from docling_core.types.doc import ( + DoclingDocument, + NodeItem, + PictureClassificationClass, + PictureClassificationData, + PictureItem, +) +from PIL import Image +from pydantic import BaseModel + +from docling.datamodel.pipeline_options import AcceleratorOptions +from docling.models.base_model import BaseEnrichmentModel +from docling.utils.accelerator_utils import decide_device + + +class DocumentPictureClassifierOptions(BaseModel): + """ + Options for configuring the DocumentPictureClassifier. + + Attributes + ---------- + kind : Literal["document_picture_classifier"] + Identifier for the type of classifier. + """ + + kind: Literal["document_picture_classifier"] = "document_picture_classifier" + + +class DocumentPictureClassifier(BaseEnrichmentModel): + """ + A model for classifying pictures in documents. + + This class enriches document pictures with predicted classifications + based on a predefined set of classes. + + Attributes + ---------- + enabled : bool + Whether the classifier is enabled for use. + options : DocumentPictureClassifierOptions + Configuration options for the classifier. + document_picture_classifier : DocumentPictureClassifierPredictor + The underlying prediction model, loaded if the classifier is enabled. + + Methods + ------- + __init__(enabled, artifacts_path, options, accelerator_options) + Initializes the classifier with specified configurations. + is_processable(doc, element) + Checks if the given element can be processed by the classifier. + __call__(doc, element_batch) + Processes a batch of elements and adds classification annotations. + """ + + images_scale = 2 + + def __init__( + self, + enabled: bool, + artifacts_path: Optional[Union[Path, str]], + options: DocumentPictureClassifierOptions, + accelerator_options: AcceleratorOptions, + ): + """ + Initializes the DocumentPictureClassifier. + + Parameters + ---------- + enabled : bool + Indicates whether the classifier is enabled. + artifacts_path : Optional[Union[Path, str]], + Path to the directory containing model artifacts. + options : DocumentPictureClassifierOptions + Configuration options for the classifier. + accelerator_options : AcceleratorOptions + Options for configuring the device and parallelism. + """ + self.enabled = enabled + self.options = options + + if self.enabled: + device = decide_device(accelerator_options.device) + from docling_ibm_models.document_figure_classifier_model.document_figure_classifier_predictor import ( + DocumentFigureClassifierPredictor, + ) + + if artifacts_path is None: + artifacts_path = self.download_models_hf() + else: + artifacts_path = Path(artifacts_path) + + self.document_picture_classifier = DocumentFigureClassifierPredictor( + artifacts_path=artifacts_path, + device=device, + num_threads=accelerator_options.num_threads, + ) + + @staticmethod + def download_models_hf( + local_dir: Optional[Path] = None, force: bool = False + ) -> Path: + from huggingface_hub import snapshot_download + from huggingface_hub.utils import disable_progress_bars + + disable_progress_bars() + download_path = snapshot_download( + repo_id="ds4sd/DocumentFigureClassifier", + force_download=force, + local_dir=local_dir, + revision="v1.0.0", + ) + + return Path(download_path) + + def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool: + """ + Determines if the given element can be processed by the classifier. + + Parameters + ---------- + doc : DoclingDocument + The document containing the element. + element : NodeItem + The element to be checked. + + Returns + ------- + bool + True if the element is a PictureItem and processing is enabled; False otherwise. + """ + return self.enabled and isinstance(element, PictureItem) + + def __call__( + self, + doc: DoclingDocument, + element_batch: Iterable[NodeItem], + ) -> Iterable[NodeItem]: + """ + Processes a batch of elements and enriches them with classification predictions. + + Parameters + ---------- + doc : DoclingDocument + The document containing the elements to be processed. + element_batch : Iterable[NodeItem] + A batch of pictures to classify. + + Returns + ------- + Iterable[NodeItem] + An iterable of NodeItem objects after processing. The field + 'data.classification' is added containing the classification for each picture. + """ + if not self.enabled: + for element in element_batch: + yield element + return + + images: List[Image.Image] = [] + elements: List[PictureItem] = [] + for el in element_batch: + assert isinstance(el, PictureItem) + elements.append(el) + img = el.get_image(doc) + assert img is not None + images.append(img) + + outputs = self.document_picture_classifier.predict(images) + + for element, output in zip(elements, outputs): + element.annotations.append( + PictureClassificationData( + provenance="DocumentPictureClassifier", + predicted_classes=[ + PictureClassificationClass( + class_name=pred[0], + confidence=pred[1], + ) + for pred in output + ], + ) + ) + + yield element diff --git a/docling/pipeline/standard_pdf_pipeline.py b/docling/pipeline/standard_pdf_pipeline.py index 97bcc6b6..fe2201d6 100644 --- a/docling/pipeline/standard_pdf_pipeline.py +++ b/docling/pipeline/standard_pdf_pipeline.py @@ -19,6 +19,10 @@ ) from docling.models.base_ocr_model import BaseOcrModel from docling.models.code_formula_model import CodeFormulaModel, CodeFormulaModelOptions +from docling.models.document_picture_classifier import ( + DocumentPictureClassifier, + DocumentPictureClassifierOptions, +) from docling.models.ds_glm_model import GlmModel, GlmOptions from docling.models.easyocr_model import EasyOcrModel from docling.models.layout_model import LayoutModel @@ -104,6 +108,13 @@ def __init__(self, pipeline_options: PdfPipelineOptions): ), accelerator_options=pipeline_options.accelerator_options, ), + # Document Picture Classifier + DocumentPictureClassifier( + enabled=pipeline_options.do_picture_classification, + artifacts_path=pipeline_options.artifacts_path, + options=DocumentPictureClassifierOptions(), + accelerator_options=pipeline_options.accelerator_options, + ), ] if ( diff --git a/poetry.lock b/poetry.lock index 73fc85db..c3905141 100644 --- a/poetry.lock +++ b/poetry.lock @@ -888,13 +888,13 @@ chunking = ["semchunk (>=2.2.0,<3.0.0)", "transformers (>=4.34.0,<5.0.0)"] [[package]] name = "docling-ibm-models" -version = "3.2.1" +version = "3.3.0" description = "This package contains the AI models used by the Docling PDF conversion package" optional = false python-versions = "<4.0,>=3.9" files = [ - {file = "docling_ibm_models-3.2.1-py3-none-any.whl", hash = "sha256:55bca5673381cc5862f4de584345020d071414c46bc1b9f6436d674e3610ec97"}, - {file = "docling_ibm_models-3.2.1.tar.gz", hash = "sha256:abd1bdc58f00600065eedbfbd34876704d5004cd20884a2c0a61ca2ee5a927dd"}, + {file = "docling_ibm_models-3.3.0-py3-none-any.whl", hash = "sha256:f1c99d345cb524239c7a2090969920e4311fd2fe22dad9bd609bc38039ec56eb"}, + {file = "docling_ibm_models-3.3.0.tar.gz", hash = "sha256:5a7497053871179d59870c830945aa8664a34aac48b7e68edf602720ee7f6c49"}, ] [package.dependencies] @@ -1046,13 +1046,13 @@ testing = ["hatch", "pre-commit", "pytest", "tox"] [[package]] name = "executing" -version = "2.1.0" +version = "2.2.0" description = "Get the currently executing AST node of a frame, and other information" optional = false python-versions = ">=3.8" files = [ - {file = "executing-2.1.0-py2.py3-none-any.whl", hash = "sha256:8d63781349375b5ebccc3142f4b30350c0cd9c79f921cde38be2be4637e98eaf"}, - {file = "executing-2.1.0.tar.gz", hash = "sha256:8ea27ddd260da8150fa5a708269c4a10e76161e2496ec3e587da9e3c0fe4b9ab"}, + {file = "executing-2.2.0-py2.py3-none-any.whl", hash = "sha256:11387150cad388d62750327a53d3339fad4888b39a6fe233c3afbb54ecffd3aa"}, + {file = "executing-2.2.0.tar.gz", hash = "sha256:5d108c028108fe2551d1a7b2e8b713341e2cb4fc0aa7dcf966fa4327a5226755"}, ] [package.extras] @@ -3674,14 +3674,14 @@ files = [ [[package]] name = "nvidia-nvjitlink-cu12" -version = "12.6.85" +version = "12.8.61" description = "Nvidia JIT LTO Library" optional = false python-versions = ">=3" files = [ - {file = "nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:eedc36df9e88b682efe4309aa16b5b4e78c2407eac59e8c10a6a47535164369a"}, - {file = "nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cf4eaa7d4b6b543ffd69d6abfb11efdeb2db48270d94dfd3a452c24150829e41"}, - {file = "nvidia_nvjitlink_cu12-12.6.85-py3-none-win_amd64.whl", hash = "sha256:e61120e52ed675747825cdd16febc6a0730537451d867ee58bee3853b1b13d1c"}, + {file = "nvidia_nvjitlink_cu12-12.8.61-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:45fd79f2ae20bd67e8bc411055939049873bfd8fac70ff13bd4865e0b9bdab17"}, + {file = "nvidia_nvjitlink_cu12-12.8.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9b80ecab31085dda3ce3b41d043be0ec739216c3fc633b8abe212d5a30026df0"}, + {file = "nvidia_nvjitlink_cu12-12.8.61-py3-none-win_amd64.whl", hash = "sha256:1166a964d25fdc0eae497574d38824305195a5283324a21ccb0ce0c802cbf41c"}, ] [[package]] @@ -4612,13 +4612,13 @@ files = [ [[package]] name = "pydantic" -version = "2.10.5" +version = "2.10.6" description = "Data validation using Python type hints" optional = false python-versions = ">=3.8" files = [ - {file = "pydantic-2.10.5-py3-none-any.whl", hash = "sha256:4dd4e322dbe55472cb7ca7e73f4b63574eecccf2835ffa2af9021ce113c83c53"}, - {file = "pydantic-2.10.5.tar.gz", hash = "sha256:278b38dbbaec562011d659ee05f63346951b3a248a6f3642e1bc68894ea2b4ff"}, + {file = "pydantic-2.10.6-py3-none-any.whl", hash = "sha256:427d664bf0b8a2b34ff5dd0f5a18df00591adcee7198fbd71981054cef37b584"}, + {file = "pydantic-2.10.6.tar.gz", hash = "sha256:ca5daa827cce33de7a42be142548b0096bf05a7e7b365aebfa5f8eeec7128236"}, ] [package.dependencies] @@ -6124,13 +6124,13 @@ files = [ [[package]] name = "sentence-transformers" -version = "3.3.1" +version = "3.4.0" description = "State-of-the-Art Text Embeddings" optional = false python-versions = ">=3.9" files = [ - {file = "sentence_transformers-3.3.1-py3-none-any.whl", hash = "sha256:abffcc79dab37b7d18d21a26d5914223dd42239cfe18cb5e111c66c54b658ae7"}, - {file = "sentence_transformers-3.3.1.tar.gz", hash = "sha256:9635dbfb11c6b01d036b9cfcee29f7716ab64cf2407ad9f403a2e607da2ac48b"}, + {file = "sentence_transformers-3.4.0-py3-none-any.whl", hash = "sha256:f7d4ad81260149172a98108a3481d8e82c11d31f40d41885f43d481149237743"}, + {file = "sentence_transformers-3.4.0.tar.gz", hash = "sha256:334288062d4b888cdd7b75913fead46b1e42bfe836f8343d23478d17f799e650"}, ] [package.dependencies] @@ -7487,13 +7487,13 @@ files = [ [[package]] name = "xlsxwriter" -version = "3.2.0" +version = "3.2.1" description = "A Python module for creating Excel XLSX files." optional = false python-versions = ">=3.6" files = [ - {file = "XlsxWriter-3.2.0-py3-none-any.whl", hash = "sha256:ecfd5405b3e0e228219bcaf24c2ca0915e012ca9464a14048021d21a995d490e"}, - {file = "XlsxWriter-3.2.0.tar.gz", hash = "sha256:9977d0c661a72866a61f9f7a809e25ebbb0fb7036baa3b9fe74afcfca6b3cb8c"}, + {file = "XlsxWriter-3.2.1-py3-none-any.whl", hash = "sha256:7e8f7c60b7a1660ef791d46ab5de78469cb978b991ca841af61f5832d2f9f4fe"}, + {file = "XlsxWriter-3.2.1.tar.gz", hash = "sha256:97618759cb264fb6a93397f660cca156ffa9561743b1823dafb60dc4474e1902"}, ] [[package]] @@ -7751,4 +7751,4 @@ tesserocr = ["tesserocr"] [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "8bb0b67294a50c0340c5cc02ce60d3608ef4d1968ae50f7e0b8b4c8a26c34734" +content-hash = "7fcfc061454f229745d6f305e1fa593468a684059717195c6ae4174bec13d362" diff --git a/pyproject.toml b/pyproject.toml index c3e1fa67..6945e5a7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,7 @@ packages = [{include = "docling"}] python = "^3.9" pydantic = "^2.0.0" docling-core = { version = "^2.15.1", extras = ["chunking"] } -docling-ibm-models = "^3.2.1" +docling-ibm-models = "^3.3.0" deepsearch-glm = "^1.0.0" docling-parse = "^3.1.0" filetype = "^1.2.0" diff --git a/tests/data/groundtruth/docling_v1/picture_classification.doctags.txt b/tests/data/groundtruth/docling_v1/picture_classification.doctags.txt new file mode 100644 index 00000000..cbcde73f --- /dev/null +++ b/tests/data/groundtruth/docling_v1/picture_classification.doctags.txt @@ -0,0 +1,17 @@ + +Figures Example +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Figure 1: This is an example image. +

+ +Figure 1: This is an example image. +
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Figure 2: This is an example image. +
+ +Figure 2: This is an example image. +
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. 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Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [303.13299560546875, 87.43224334716797, 308.1142883300781, 96.27903747558594], "page": 2, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}], "figures": [{"prov": [{"bbox": [134.92005920410156, 281.78173828125, 475.66351318359375, 487.109375], "page": 1, "span": [0, 35], "__ref_s3_data": null}], "text": "Figure 1: This is an example image.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [218.8155517578125, 283.10589599609375, 391.96246337890625, 513.984619140625], "page": 2, "span": [0, 35], "__ref_s3_data": null}], "text": "Figure 2: This is an example image.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 792.0, "page": 1, "width": 612.0}, {"height": 792.0, "page": 2, "width": 612.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/picture_classification.md b/tests/data/groundtruth/docling_v1/picture_classification.md new file mode 100644 index 00000000..6b9d1faf --- /dev/null +++ b/tests/data/groundtruth/docling_v1/picture_classification.md @@ -0,0 +1,15 @@ +## Figures Example + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +Figure 1: This is an example image. + + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +Figure 2: This is an example image. + + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/picture_classification.pages.json b/tests/data/groundtruth/docling_v1/picture_classification.pages.json new file mode 100644 index 00000000..744f54fb --- /dev/null +++ b/tests/data/groundtruth/docling_v1/picture_classification.pages.json @@ -0,0 +1 @@ +[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Figures Example", "bbox": {"l": 133.76801, "t": 124.80877999999996, "r": 252.35513, "b": 137.54816000000005, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801, "t": 149.67193999999995, "r": 477.47971, "b": 158.51873999999998, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801, "t": 161.62694999999997, "r": 477.4806500000001, "b": 170.47375, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801, "t": 173.58196999999996, "r": 477.47571000000005, "b": 182.42877, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801, "t": 185.53698999999995, "r": 477.47466999999995, "b": 194.38378999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801, "t": 197.49199999999996, "r": 477.47668, "b": 206.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801, "t": 209.44701999999995, "r": 477.48068000000006, "b": 218.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801, "t": 221.40301999999997, "r": 355.20874, "b": 230.24982, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.60098, "t": 221.40301999999997, "r": 477.48276, "b": 230.24982, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801, "t": 233.35802999999999, "r": 477.47772, "b": 242.20483000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801, "t": 245.31304999999998, "r": 477.47971, "b": 254.15985, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. 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Stet clita kasd gubergren,", "bbox": {"l": 133.76801, "t": 624.7759599999999, "r": 477.48172000000005, "b": 633.62274, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. 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Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 226.89100646972656, "t": 262.86505126953125, "r": 384.35479736328125, "b": 254.0182647705078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Figure 1: This is an example image.", "text": "Figure 1: This is an example image."}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 133.76800537109375, "t": 238.95504760742188, "r": 477.4817199707031, "b": 122.51225280761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 747]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua."}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 303.13299560546875, "t": 96.27903747558594, "r": 308.1142883300781, "b": 87.43224334716797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 664.1490478515625, "r": 477.4817199707031, "b": 523.7951049804688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 887]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 226.89100646972656, "t": 268.7890319824219, "r": 384.35479736328125, "b": 259.9422607421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Figure 2: This is an example image.", "text": "Figure 2: This is an example image."}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 245.71804809570312, "r": 477.4817199707031, "b": 117.32023620605469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 804]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 303.13299560546875, "t": 96.27903747558594, "r": 308.1142883300781, "b": 87.43224334716797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [], "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 134.92005920410156, "t": 487.109375, "r": 475.66351318359375, "b": 281.78173828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "captions": [{"cref": "#/texts/2"}], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [], "label": "picture", "prov": [{"page_no": 2, "bbox": {"l": 218.8155517578125, "t": 513.984619140625, "r": 391.96246337890625, "b": 283.10589599609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "captions": [{"cref": "#/texts/6"}], "references": [], "footnotes": [], "image": null, "annotations": []}], "tables": [], "key_value_items": [], "pages": {"1": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 1}, "2": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 2}}} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/picture_classification.md b/tests/data/groundtruth/docling_v2/picture_classification.md new file mode 100644 index 00000000..8e233a96 --- /dev/null +++ b/tests/data/groundtruth/docling_v2/picture_classification.md @@ -0,0 +1,17 @@ +## Figures Example + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +Figure 1: This is an example image. + + + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +Figure 2: This is an example image. + + + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/picture_classification.pages.json b/tests/data/groundtruth/docling_v2/picture_classification.pages.json new file mode 100644 index 00000000..744f54fb --- /dev/null +++ b/tests/data/groundtruth/docling_v2/picture_classification.pages.json @@ -0,0 +1 @@ +[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Figures Example", "bbox": {"l": 133.76801, "t": 124.80877999999996, "r": 252.35513, "b": 137.54816000000005, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801, "t": 149.67193999999995, "r": 477.47971, "b": 158.51873999999998, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801, "t": 161.62694999999997, "r": 477.4806500000001, "b": 170.47375, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801, "t": 173.58196999999996, "r": 477.47571000000005, "b": 182.42877, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801, "t": 185.53698999999995, "r": 477.47466999999995, "b": 194.38378999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801, "t": 197.49199999999996, "r": 477.47668, "b": 206.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801, "t": 209.44701999999995, "r": 477.48068000000006, "b": 218.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801, "t": 221.40301999999997, "r": 355.20874, "b": 230.24982, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.60098, "t": 221.40301999999997, "r": 477.48276, "b": 230.24982, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801, "t": 233.35802999999999, "r": 477.47772, "b": 242.20483000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801, "t": 245.31304999999998, "r": 477.47971, "b": 254.15985, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801, "t": 257.26806999999997, "r": 477.47473, "b": 266.11487, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801, "t": 269.22308, "r": 477.47571000000005, "b": 278.06989, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801, "t": 281.17911, "r": 351.48471, "b": 290.02588, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Figure 1: This is an example image.", "bbox": {"l": 226.89101, "t": 529.13495, "r": 384.3548, "b": 537.98174, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201, "t": 553.04495, "r": 477.47786999999994, "b": 561.89174, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801, "t": 564.99995, "r": 477.47860999999995, "b": 573.84674, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801, "t": 576.95496, "r": 477.47571000000005, "b": 585.80174, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801, "t": 588.90996, "r": 477.47559, "b": 597.75674, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801, "t": 600.86595, "r": 477.48169000000007, "b": 609.7127399999999, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801, "t": 612.82095, "r": 477.48062, "b": 621.66774, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801, "t": 624.7759599999999, "r": 477.48172000000005, "b": 633.62274, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. 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PictureClassificationData + +from docling.backend.docling_parse_v2_backend import DoclingParseV2DocumentBackend +from docling.datamodel.base_models import InputFormat +from docling.datamodel.document import ConversionResult +from docling.datamodel.pipeline_options import PdfPipelineOptions +from docling.document_converter import DocumentConverter, PdfFormatOption +from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline + + +def get_converter(): + + pipeline_options = PdfPipelineOptions() + pipeline_options.generate_page_images = True + + pipeline_options.do_ocr = False + pipeline_options.do_table_structure = False + pipeline_options.do_code_enrichment = False + pipeline_options.do_formula_enrichment = False + pipeline_options.do_picture_classification = True + pipeline_options.generate_picture_images = True + pipeline_options.images_scale = 2 + + converter = DocumentConverter( + format_options={ + InputFormat.PDF: PdfFormatOption( + backend=DoclingParseV2DocumentBackend, + pipeline_cls=StandardPdfPipeline, + pipeline_options=pipeline_options, + ) + } + ) + + return converter + + +def test_picture_classifier(): + pdf_path = Path("tests/data/picture_classification.pdf") + converter = get_converter() + + print(f"converting {pdf_path}") + + doc_result: ConversionResult = converter.convert(pdf_path) + + results = doc_result.document.pictures + + assert len(results) == 2 + + res = results[0] + assert len(res.annotations) == 1 + assert type(res.annotations[0]) == PictureClassificationData + classification_data = res.annotations[0] + assert classification_data.provenance == "DocumentPictureClassifier" + assert ( + len(classification_data.predicted_classes) == 16 + ), "Number of predicted classes is not equal to 16" + confidences = [pred.confidence for pred in classification_data.predicted_classes] + assert confidences == sorted( + confidences, reverse=True + ), "Predictions are not sorted in descending order of confidence" + assert ( + classification_data.predicted_classes[0].class_name == "bar_chart" + ), "The prediction is wrong for the bar chart image." + + res = results[1] + assert len(res.annotations) == 1 + assert type(res.annotations[0]) == PictureClassificationData + classification_data = res.annotations[0] + assert classification_data.provenance == "DocumentPictureClassifier" + assert ( + len(classification_data.predicted_classes) == 16 + ), "Number of predicted classes is not equal to 16" + confidences = [pred.confidence for pred in classification_data.predicted_classes] + assert confidences == sorted( + confidences, reverse=True + ), "Predictions are not sorted in descending order of confidence" + assert ( + classification_data.predicted_classes[0].class_name == "map" + ), "The prediction is wrong for the bar chart image." From a458e298ca64da2c6df29d953e95645525817bed Mon Sep 17 00:00:00 2001 From: "Peter W. J. Staar" <91719829+PeterStaar-IBM@users.noreply.github.com> Date: Fri, 24 Jan 2025 18:48:02 +0100 Subject: [PATCH 07/12] fix: added extraction of byte-images in excel (#804) * fix(msexcel): ignore Mypy checking for _find_images_in_sheet function Signed-off-by: Jiun An Tsai * fixed some issues Signed-off-by: Peter Staar * reformatted the code Signed-off-by: Peter Staar * pinned pillow in pyproject Signed-off-by: Peter Staar --------- Signed-off-by: Jiun An Tsai Signed-off-by: Peter Staar Signed-off-by: Michele Dolfi Co-authored-by: Jiun An Tsai Co-authored-by: Michele Dolfi --- docling/backend/msexcel_backend.py | 87 ++++++++++-------- poetry.lock | 4 +- pyproject.toml | 1 + .../groundtruth/docling_v2/test-01.xlsx.itxt | 3 +- .../groundtruth/docling_v2/test-01.xlsx.json | 30 +++++- .../groundtruth/docling_v2/test-01.xlsx.md | 4 +- tests/data/xlsx/test-01.xlsx | Bin 21387 -> 170052 bytes tests/test_backend_msexcel.py | 2 +- 8 files changed, 87 insertions(+), 44 deletions(-) diff --git a/docling/backend/msexcel_backend.py b/docling/backend/msexcel_backend.py index 2d200d75..19c25341 100644 --- a/docling/backend/msexcel_backend.py +++ b/docling/backend/msexcel_backend.py @@ -26,6 +26,7 @@ from typing import Any, List +from PIL import Image as PILImage from pydantic import BaseModel @@ -325,49 +326,61 @@ def _find_images_in_sheet( self, doc: DoclingDocument, sheet: Worksheet ) -> DoclingDocument: - # FIXME: mypy does not agree with _images ... - """ - # Iterate over images in the sheet - for idx, image in enumerate(sheet._images): # Access embedded images + # Iterate over byte images in the sheet + for idx, image in enumerate(sheet._images): # type: ignore - image_bytes = BytesIO(image.ref.blob) - pil_image = Image.open(image_bytes) + try: + pil_image = PILImage.open(image.ref) - doc.add_picture( - parent=self.parents[0], - image=ImageRef.from_pil(image=pil_image, dpi=72), - caption=None, - ) - """ + doc.add_picture( + parent=self.parents[0], + image=ImageRef.from_pil(image=pil_image, dpi=72), + caption=None, + ) + except: + _log.error("could not extract the image from excel sheets") - # FIXME: mypy does not agree with _charts ... """ - for idx, chart in enumerate(sheet._charts): # Access embedded charts - chart_path = f"chart_{idx + 1}.png" - _log.info( - f"Chart found, but dynamic rendering is required for: {chart_path}" - ) + for idx, chart in enumerate(sheet._charts): # type: ignore + try: + chart_path = f"chart_{idx + 1}.png" + _log.info( + f"Chart found, but dynamic rendering is required for: {chart_path}" + ) - _log.info(f"Chart {idx + 1}:") - - # Chart type - _log.info(f"Type: {type(chart).__name__}") - - # Title - if chart.title: - _log.info(f"Title: {chart.title}") - else: - _log.info("No title") - - # Data series - for series in chart.series: - _log.info(" => series ...") - _log.info(f"Data Series: {series.title}") - _log.info(f"Values: {series.values}") - _log.info(f"Categories: {series.categories}") + _log.info(f"Chart {idx + 1}:") - # Position - # _log.info(f"Anchor Cell: {chart.anchor}") + # Chart type + # _log.info(f"Type: {type(chart).__name__}") + print(f"Type: {type(chart).__name__}") + + # Extract series data + for series_idx, series in enumerate(chart.series): + #_log.info(f"Series {series_idx + 1}:") + print(f"Series {series_idx + 1} type: {type(series).__name__}") + #print(f"x-values: {series.xVal}") + #print(f"y-values: {series.yVal}") + + print(f"xval type: {type(series.xVal).__name__}") + + xvals = [] + for _ in series.xVal.numLit.pt: + print(f"xval type: {type(_).__name__}") + if hasattr(_, 'v'): + xvals.append(_.v) + + print(f"x-values: {xvals}") + + yvals = [] + for _ in series.yVal: + if hasattr(_, 'v'): + yvals.append(_.v) + + print(f"y-values: {yvals}") + + except Exception as exc: + print(exc) + continue """ return doc diff --git a/poetry.lock b/poetry.lock index c3905141..c3ab3cca 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -7751,4 +7751,4 @@ tesserocr = ["tesserocr"] [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "7fcfc061454f229745d6f305e1fa593468a684059717195c6ae4174bec13d362" +content-hash = "08d30cee8d77f9beee32d5dbec1643367ecae2b4c4b47b57fcb337711471eb5c" diff --git a/pyproject.toml b/pyproject.toml index 6945e5a7..3d0f548c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,6 +56,7 @@ onnxruntime = [ { version = ">=1.7.0,<1.20.0", optional = true, markers = "python_version < '3.10'" }, { version = "^1.7.0", optional = true, markers = "python_version >= '3.10'" } ] +pillow = "^10.0.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "^24.4.2"} diff --git a/tests/data/groundtruth/docling_v2/test-01.xlsx.itxt b/tests/data/groundtruth/docling_v2/test-01.xlsx.itxt index cab5f63b..216d68f8 100644 --- a/tests/data/groundtruth/docling_v2/test-01.xlsx.itxt +++ b/tests/data/groundtruth/docling_v2/test-01.xlsx.itxt @@ -7,4 +7,5 @@ item-0 at level 0: unspecified: group _root_ item-6 at level 2: table with [5x3] item-7 at level 1: section: group sheet: Sheet3 item-8 at level 2: table with [7x3] - item-9 at level 2: table with [7x3] \ No newline at end of file + item-9 at level 2: table with [7x3] + item-10 at level 2: picture \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/test-01.xlsx.json b/tests/data/groundtruth/docling_v2/test-01.xlsx.json index 9a9e0d52..b6642298 100644 --- a/tests/data/groundtruth/docling_v2/test-01.xlsx.json +++ b/tests/data/groundtruth/docling_v2/test-01.xlsx.json @@ -4,7 +4,7 @@ "name": "test-01", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", - "binary_hash": 9744611217659152490, + "binary_hash": 13665052226482254103, "filename": "test-01.xlsx" }, "furniture": { @@ -73,6 +73,9 @@ }, { "$ref": "#/tables/5" + }, + { + "$ref": "#/pictures/0" } ], "name": "sheet: Sheet3", @@ -80,7 +83,30 @@ } ], "texts": [], - "pictures": [], + "pictures": [ + { + "self_ref": "#/pictures/0", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "label": "picture", + "prov": [], + "captions": [], + "references": [], + "footnotes": [], + "image": { + "mimetype": "image/png", + "dpi": 72, + "size": { + "width": 421.0, + "height": 430.0 + }, + "uri": 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" 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z?O#QZ%2Pw3P^H|J!U91Qq2NML9is92gU?w2kZ>`t<#)UYGU0h} zt|b8Z7K2Xk1uFn*DhBP~>(&6YQ4H!R{7F&XJaiTW0P+9neU}XYjYFU@{F)sADU^V6 RqOyQ2uulL0yd8H6{{U&M9FYJ3 diff --git a/tests/test_backend_msexcel.py b/tests/test_backend_msexcel.py index d13959ed..0a4440dc 100644 --- a/tests/test_backend_msexcel.py +++ b/tests/test_backend_msexcel.py @@ -53,7 +53,7 @@ def test_e2e_xlsx_conversions(): converter = get_converter() for xlsx_path in xlsx_paths: - # print(f"converting {xlsx_path}") + print(f"converting {xlsx_path}") gt_path = ( xlsx_path.parent.parent / "groundtruth" / "docling_v2" / xlsx_path.name From 9e4ca90db1116a240cf9d55f1ab141adb2cf389c Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Fri, 24 Jan 2025 18:21:14 +0000 Subject: [PATCH 08/12] chore: bump version to 2.16.0 [skip ci] --- CHANGELOG.md | 22 ++++++++++++++++++++++ pyproject.toml | 2 +- 2 files changed, 23 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 21882618..906e8300 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,25 @@ +## [v2.16.0](https://github.com/DS4SD/docling/releases/tag/v2.16.0) - 2025-01-24 + +### Feature + +* New document picture classifier ([#805](https://github.com/DS4SD/docling/issues/805)) ([`16a218d`](https://github.com/DS4SD/docling/commit/16a218d871c48fd9cc636b77f7b597dc40cbeeec)) +* Add Docling JSON ingestion ([#783](https://github.com/DS4SD/docling/issues/783)) ([`88a0e66`](https://github.com/DS4SD/docling/commit/88a0e66adc19238f57a942b0504926cdaeacd8cc)) +* Code and equation model for PDF and code blocks in markdown ([#752](https://github.com/DS4SD/docling/issues/752)) ([`3213b24`](https://github.com/DS4SD/docling/commit/3213b247ad6870ff984271f09f7720be68d9479b)) +* Add "auto" language for TesseractOcr ([#759](https://github.com/DS4SD/docling/issues/759)) ([`8543c22`](https://github.com/DS4SD/docling/commit/8543c22687fee40459d393bf4adcfc059712de02)) + +### Fix + +* Added extraction of byte-images in excel ([#804](https://github.com/DS4SD/docling/issues/804)) ([`a458e29`](https://github.com/DS4SD/docling/commit/a458e298ca64da2c6df29d953e95645525817bed)) +* Update docling-parse-v2 backend version with new parsing fixes ([#769](https://github.com/DS4SD/docling/issues/769)) ([`670a08b`](https://github.com/DS4SD/docling/commit/670a08bdedda847ff3b6942bcaa1a2adef79afe2)) + +### Documentation + +* Fix minor typos ([#801](https://github.com/DS4SD/docling/issues/801)) ([`c58f75d`](https://github.com/DS4SD/docling/commit/c58f75d0f75040e32820cc2915ec00755211c02f)) +* Add Azure RAG example ([#675](https://github.com/DS4SD/docling/issues/675)) ([`9020a93`](https://github.com/DS4SD/docling/commit/9020a934be35b0798c972eb77a22fb62ce654ca5)) +* Fix links between docs pages ([#697](https://github.com/DS4SD/docling/issues/697)) ([`c49b352`](https://github.com/DS4SD/docling/commit/c49b3526fb7b72e8007f785b1fcfdf58c2457756)) +* Fix correct Accelerator pipeline options in docs/examples/custom_convert.py ([#733](https://github.com/DS4SD/docling/issues/733)) ([`7686083`](https://github.com/DS4SD/docling/commit/768608351d40376c3504546f52e967195536b3d5)) +* Example to translate documents ([#739](https://github.com/DS4SD/docling/issues/739)) ([`f7e1cbf`](https://github.com/DS4SD/docling/commit/f7e1cbf629ae5f3e279296e72f656b7a453ab7a3)) + ## [v2.15.1](https://github.com/DS4SD/docling/releases/tag/v2.15.1) - 2025-01-10 ### Fix diff --git a/pyproject.toml b/pyproject.toml index 3d0f548c..f45f1b61 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "docling" -version = "2.15.1" # DO NOT EDIT, updated automatically +version = "2.16.0" # DO NOT EDIT, updated automatically description = "SDK and CLI for parsing PDF, DOCX, HTML, and more, to a unified document representation for powering downstream workflows such as gen AI applications." authors = ["Christoph Auer ", "Michele Dolfi ", "Maxim Lysak ", "Nikos Livathinos ", "Ahmed Nassar ", "Panos Vagenas ", "Peter Staar "] license = "MIT" From 3be2fb581fe5a2ebd5cec9c86bb22eb1dec6fd0f Mon Sep 17 00:00:00 2001 From: Nikos Livathinos <100353117+nikos-livathinos@users.noreply.github.com> Date: Sun, 26 Jan 2025 08:07:56 +0100 Subject: [PATCH 09/12] feat: Introduce automatic language detection in TesseractOcrCliModel (#800) * feat: Introduce automatic language detection in tesseract_ocr_cli model. Extend unit tests. Signed-off-by: Nikos Livathinos * docs: Add example how to use "auto" language with tesseract OCR engines Signed-off-by: Nikos Livathinos * fix: Refactor the TesseractOcrModel and TesseractOcrCliModel to validate if the auto-detected language is installed in the system and if not fall back to a default option without language. Signed-off-by: Nikos Livathinos --------- Signed-off-by: Nikos Livathinos --- docling/models/tesseract_ocr_cli_model.py | 76 +++++++++++++++++++++-- docling/models/tesseract_ocr_model.py | 74 +++++++++++----------- docling/utils/ocr_utils.py | 9 +++ docs/examples/tesseract_lang_detection.py | 37 +++++++++++ mkdocs.yml | 1 + tests/test_e2e_ocr_conversion.py | 1 + 6 files changed, 157 insertions(+), 41 deletions(-) create mode 100644 docling/utils/ocr_utils.py create mode 100644 docs/examples/tesseract_lang_detection.py diff --git a/docling/models/tesseract_ocr_cli_model.py b/docling/models/tesseract_ocr_cli_model.py index 3d5c8006..cdc5671d 100644 --- a/docling/models/tesseract_ocr_cli_model.py +++ b/docling/models/tesseract_ocr_cli_model.py @@ -4,7 +4,7 @@ import os import tempfile from subprocess import DEVNULL, PIPE, Popen -from typing import Iterable, Optional, Tuple +from typing import Iterable, List, Optional, Tuple import pandas as pd from docling_core.types.doc import BoundingBox, CoordOrigin @@ -14,6 +14,7 @@ from docling.datamodel.pipeline_options import TesseractCliOcrOptions from docling.datamodel.settings import settings from docling.models.base_ocr_model import BaseOcrModel +from docling.utils.ocr_utils import map_tesseract_script from docling.utils.profiling import TimeRecorder _log = logging.getLogger(__name__) @@ -28,10 +29,13 @@ def __init__(self, enabled: bool, options: TesseractCliOcrOptions): self._name: Optional[str] = None self._version: Optional[str] = None + self._tesseract_languages: Optional[List[str]] = None + self._script_prefix: Optional[str] = None if self.enabled: try: self._get_name_and_version() + self._set_languages_and_prefix() except Exception as exc: raise RuntimeError( @@ -73,12 +77,20 @@ def _get_name_and_version(self) -> Tuple[str, str]: return name, version def _run_tesseract(self, ifilename: str): - + r""" + Run tesseract CLI + """ cmd = [self.options.tesseract_cmd] - if self.options.lang is not None and len(self.options.lang) > 0: + if "auto" in self.options.lang: + lang = self._detect_language(ifilename) + if lang is not None: + cmd.append("-l") + cmd.append(lang) + elif self.options.lang is not None and len(self.options.lang) > 0: cmd.append("-l") cmd.append("+".join(self.options.lang)) + if self.options.path is not None: cmd.append("--tessdata-dir") cmd.append(self.options.path) @@ -106,6 +118,63 @@ def _run_tesseract(self, ifilename: str): return df_filtered + def _detect_language(self, ifilename: str): + r""" + Run tesseract in PSM 0 mode to detect the language + """ + assert self._tesseract_languages is not None + + cmd = [self.options.tesseract_cmd] + cmd.extend(["--psm", "0", "-l", "osd", ifilename, "stdout"]) + _log.info("command: {}".format(" ".join(cmd))) + proc = Popen(cmd, stdout=PIPE, stderr=DEVNULL) + output, _ = proc.communicate() + decoded_data = output.decode("utf-8") + df = pd.read_csv( + io.StringIO(decoded_data), sep=":", header=None, names=["key", "value"] + ) + scripts = df.loc[df["key"] == "Script"].value.tolist() + if len(scripts) == 0: + _log.warning("Tesseract cannot detect the script of the page") + return None + + script = map_tesseract_script(scripts[0].strip()) + lang = f"{self._script_prefix}{script}" + + # Check if the detected language has been installed + if lang not in self._tesseract_languages: + msg = f"Tesseract detected the script '{script}' and language '{lang}'." + msg += " However this language is not installed in your system and will be ignored." + _log.warning(msg) + return None + + _log.debug( + f"Using tesseract model for the detected script '{script}' and language '{lang}'" + ) + return lang + + def _set_languages_and_prefix(self): + r""" + Read and set the languages installed in tesseract and decide the script prefix + """ + # Get all languages + cmd = [self.options.tesseract_cmd] + cmd.append("--list-langs") + _log.info("command: {}".format(" ".join(cmd))) + proc = Popen(cmd, stdout=PIPE, stderr=DEVNULL) + output, _ = proc.communicate() + decoded_data = output.decode("utf-8") + df = pd.read_csv(io.StringIO(decoded_data), header=None) + self._tesseract_languages = df[0].tolist()[1:] + + # Decide the script prefix + if any([l.startswith("script/") for l in self._tesseract_languages]): + script_prefix = "script/" + else: + script_prefix = "" + + self._script_prefix = script_prefix + def __call__( self, conv_res: ConversionResult, page_batch: Iterable[Page] ) -> Iterable[Page]: @@ -120,7 +189,6 @@ def __call__( yield page else: with TimeRecorder(conv_res, "ocr"): - ocr_rects = self.get_ocr_rects(page) all_ocr_cells = [] diff --git a/docling/models/tesseract_ocr_model.py b/docling/models/tesseract_ocr_model.py index 6a1b60ee..5b70155e 100644 --- a/docling/models/tesseract_ocr_model.py +++ b/docling/models/tesseract_ocr_model.py @@ -8,6 +8,7 @@ from docling.datamodel.pipeline_options import TesseractOcrOptions from docling.datamodel.settings import settings from docling.models.base_ocr_model import BaseOcrModel +from docling.utils.ocr_utils import map_tesseract_script from docling.utils.profiling import TimeRecorder _log = logging.getLogger(__name__) @@ -20,6 +21,7 @@ def __init__(self, enabled: bool, options: TesseractOcrOptions): self.scale = 3 # multiplier for 72 dpi == 216 dpi. self.reader = None + self.osd_reader = None if self.enabled: install_errmsg = ( @@ -47,8 +49,8 @@ def __init__(self, enabled: bool, options: TesseractOcrOptions): except: raise ImportError(install_errmsg) - _, tesserocr_languages = tesserocr.get_languages() - if not tesserocr_languages: + _, self._tesserocr_languages = tesserocr.get_languages() + if not self._tesserocr_languages: raise ImportError(missing_langs_errmsg) # Initialize the tesseractAPI @@ -57,7 +59,7 @@ def __init__(self, enabled: bool, options: TesseractOcrOptions): self.script_readers: dict[str, tesserocr.PyTessBaseAPI] = {} - if any([l.startswith("script/") for l in tesserocr_languages]): + if any([l.startswith("script/") for l in self._tesserocr_languages]): self.script_prefix = "script/" else: self.script_prefix = "" @@ -72,14 +74,14 @@ def __init__(self, enabled: bool, options: TesseractOcrOptions): tesserocr_kwargs["path"] = self.options.path if lang == "auto": - self.reader = tesserocr.PyTessBaseAPI( + self.reader = tesserocr.PyTessBaseAPI(**tesserocr_kwargs) + self.osd_reader = tesserocr.PyTessBaseAPI( **{"lang": "osd", "psm": tesserocr.PSM.OSD_ONLY} | tesserocr_kwargs ) else: self.reader = tesserocr.PyTessBaseAPI( **{"lang": lang} | tesserocr_kwargs, ) - self.reader_RIL = tesserocr.RIL def __del__(self): @@ -96,8 +98,6 @@ def __call__( yield from page_batch return - import tesserocr - for page in page_batch: assert page._backend is not None if not page._backend.is_valid(): @@ -105,6 +105,7 @@ def __call__( else: with TimeRecorder(conv_res, "ocr"): assert self.reader is not None + assert self._tesserocr_languages is not None ocr_rects = self.get_ocr_rects(page) @@ -117,43 +118,42 @@ def __call__( scale=self.scale, cropbox=ocr_rect ) - # Retrieve text snippets with their bounding boxes - self.reader.SetImage(high_res_image) + local_reader = self.reader + if "auto" in self.options.lang: + assert self.osd_reader is not None - if self.options.lang == ["auto"]: - osd = self.reader.DetectOrientationScript() + self.osd_reader.SetImage(high_res_image) + osd = self.osd_reader.DetectOrientationScript() # No text, probably if osd is None: continue script = osd["script_name"] - - if script == "Katakana" or script == "Hiragana": - script = "Japanese" - elif script == "Han": - script = "HanS" - elif script == "Korean": - script = "Hangul" - - _log.debug( - f'Using model for the detected script "{script}"' - ) - - if script not in self.script_readers: - self.script_readers[script] = tesserocr.PyTessBaseAPI( - path=self.reader.GetDatapath(), - lang=f"{self.script_prefix}{script}", - psm=tesserocr.PSM.AUTO, - init=True, - oem=tesserocr.OEM.DEFAULT, - ) - - local_reader = self.script_readers[script] - local_reader.SetImage(high_res_image) - else: - local_reader = self.reader - + script = map_tesseract_script(script) + lang = f"{self.script_prefix}{script}" + + # Check if the detected languge is present in the system + if lang not in self._tesserocr_languages: + msg = f"Tesseract detected the script '{script}' and language '{lang}'." + msg += " However this language is not installed in your system and will be ignored." + _log.warning(msg) + else: + if script not in self.script_readers: + import tesserocr + + self.script_readers[script] = ( + tesserocr.PyTessBaseAPI( + path=self.reader.GetDatapath(), + lang=lang, + psm=tesserocr.PSM.AUTO, + init=True, + oem=tesserocr.OEM.DEFAULT, + ) + ) + local_reader = self.script_readers[script] + + local_reader.SetImage(high_res_image) boxes = local_reader.GetComponentImages( self.reader_RIL.TEXTLINE, True ) diff --git a/docling/utils/ocr_utils.py b/docling/utils/ocr_utils.py new file mode 100644 index 00000000..59503f1f --- /dev/null +++ b/docling/utils/ocr_utils.py @@ -0,0 +1,9 @@ +def map_tesseract_script(script: str) -> str: + r""" """ + if script == "Katakana" or script == "Hiragana": + script = "Japanese" + elif script == "Han": + script = "HanS" + elif script == "Korean": + script = "Hangul" + return script diff --git a/docs/examples/tesseract_lang_detection.py b/docs/examples/tesseract_lang_detection.py new file mode 100644 index 00000000..b75e4707 --- /dev/null +++ b/docs/examples/tesseract_lang_detection.py @@ -0,0 +1,37 @@ +from pathlib import Path + +from docling.datamodel.base_models import InputFormat +from docling.datamodel.pipeline_options import ( + PdfPipelineOptions, + TesseractCliOcrOptions, + TesseractOcrOptions, +) +from docling.document_converter import DocumentConverter, PdfFormatOption + + +def main(): + input_doc = Path("./tests/data/2206.01062.pdf") + + # Set lang=["auto"] with a tesseract OCR engine: TesseractOcrOptions, TesseractCliOcrOptions + # ocr_options = TesseractOcrOptions(lang=["auto"]) + ocr_options = TesseractCliOcrOptions(lang=["auto"]) + + pipeline_options = PdfPipelineOptions( + do_ocr=True, force_full_page_ocr=True, ocr_options=ocr_options + ) + + converter = DocumentConverter( + format_options={ + InputFormat.PDF: PdfFormatOption( + pipeline_options=pipeline_options, + ) + } + ) + + doc = converter.convert(input_doc).document + md = doc.export_to_markdown() + print(md) + + +if __name__ == "__main__": + main() diff --git a/mkdocs.yml b/mkdocs.yml index 0f3e9dd0..2b2e2da0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -75,6 +75,7 @@ nav: - "Table export": examples/export_tables.py - "Multimodal export": examples/export_multimodal.py - "Force full page OCR": examples/full_page_ocr.py + - "Automatic OCR language detection with tesseract": examples/tesseract_lang_detection.py - "Accelerator options": examples/run_with_accelerator.py - "Simple translation": examples/translate.py - โœ‚๏ธ Chunking: diff --git a/tests/test_e2e_ocr_conversion.py b/tests/test_e2e_ocr_conversion.py index b3cdd312..4a542d21 100644 --- a/tests/test_e2e_ocr_conversion.py +++ b/tests/test_e2e_ocr_conversion.py @@ -62,6 +62,7 @@ def test_e2e_conversions(): TesseractOcrOptions(force_full_page_ocr=True), TesseractOcrOptions(force_full_page_ocr=True, lang=["auto"]), TesseractCliOcrOptions(force_full_page_ocr=True), + TesseractCliOcrOptions(force_full_page_ocr=True, lang=["auto"]), RapidOcrOptions(force_full_page_ocr=True), ] From c2ae1cc4cab0f9e693c7ca460fe8afa5b515ee94 Mon Sep 17 00:00:00 2001 From: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> Date: Sun, 26 Jan 2025 08:10:33 +0100 Subject: [PATCH 10/12] docs: description of supported formats and backends (#788) * chore: remove type-ignore marks for attaching text to non GroupItems After commit b74208 of docling-core, text items can be attached to any NodeItem and therefore the ignore[arg-type] type marks can be removed. Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> * test: remove unnecessary imports Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> * docs: add documentation on supported formats and backends Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> * docs: add notebook example with XML backends Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> --------- Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> --- docling/backend/xml/uspto_backend.py | 50 +- docs/examples/backend_xml_rag.ipynb | 1078 ++++++++++++++++++++++++++ docs/usage.md | 40 + mkdocs.yml | 3 +- tests/test_backend_msexcel.py | 7 +- tests/test_backend_patent_uspto.py | 9 +- tests/test_backend_pubmed.py | 1 - 7 files changed, 1147 insertions(+), 41 deletions(-) create mode 100644 docs/examples/backend_xml_rag.ipynb diff --git a/docling/backend/xml/uspto_backend.py b/docling/backend/xml/uspto_backend.py index ef253b21..21001ab7 100644 --- a/docling/backend/xml/uspto_backend.py +++ b/docling/backend/xml/uspto_backend.py @@ -389,7 +389,7 @@ def _add_property(self, name: str, text: str) -> None: if name == self.Element.TITLE.value: if text: self.parents[self.level + 1] = self.doc.add_title( - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], text=text, ) self.level += 1 @@ -406,7 +406,7 @@ def _add_property(self, name: str, text: str) -> None: abstract_item = self.doc.add_heading( heading_text, level=heading_level, - parent=self.parents[heading_level], # type: ignore[arg-type] + parent=self.parents[heading_level], ) self.doc.add_text( label=DocItemLabel.PARAGRAPH, @@ -434,7 +434,7 @@ def _add_property(self, name: str, text: str) -> None: claims_item = self.doc.add_heading( heading_text, level=heading_level, - parent=self.parents[heading_level], # type: ignore[arg-type] + parent=self.parents[heading_level], ) for text in self.claims: self.doc.add_text( @@ -452,7 +452,7 @@ def _add_property(self, name: str, text: str) -> None: self.doc.add_text( label=DocItemLabel.PARAGRAPH, text=text, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) self.text = "" @@ -460,7 +460,7 @@ def _add_property(self, name: str, text: str) -> None: self.parents[self.level + 1] = self.doc.add_heading( text=text, level=self.level, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) self.level += 1 self.text = "" @@ -470,7 +470,7 @@ def _add_property(self, name: str, text: str) -> None: empty_table = TableData(num_rows=0, num_cols=0, table_cells=[]) self.doc.add_table( data=empty_table, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) def _apply_style(self, text: str, style_tag: str) -> str: @@ -721,7 +721,7 @@ def _add_property(self, name: str, text: str) -> None: if self.Element.TITLE.value in self.property and text.strip(): title = text.strip() self.parents[self.level + 1] = self.doc.add_title( - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], text=title, ) self.level += 1 @@ -749,7 +749,7 @@ def _add_property(self, name: str, text: str) -> None: self.parents[self.level + 1] = self.doc.add_heading( text=text.strip(), level=self.level, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) self.level += 1 @@ -769,7 +769,7 @@ def _add_property(self, name: str, text: str) -> None: claims_item = self.doc.add_heading( heading_text, level=heading_level, - parent=self.parents[heading_level], # type: ignore[arg-type] + parent=self.parents[heading_level], ) for text in self.claims: self.doc.add_text( @@ -787,7 +787,7 @@ def _add_property(self, name: str, text: str) -> None: abstract_item = self.doc.add_heading( heading_text, level=heading_level, - parent=self.parents[heading_level], # type: ignore[arg-type] + parent=self.parents[heading_level], ) self.doc.add_text( label=DocItemLabel.PARAGRAPH, text=abstract, parent=abstract_item @@ -799,7 +799,7 @@ def _add_property(self, name: str, text: str) -> None: self.doc.add_text( label=DocItemLabel.PARAGRAPH, text=paragraph, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) elif self.Element.CLAIM.value in self.property: # we may need a space after a paragraph in claim text @@ -811,7 +811,7 @@ def _add_property(self, name: str, text: str) -> None: empty_table = TableData(num_rows=0, num_cols=0, table_cells=[]) self.doc.add_table( data=empty_table, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) def _apply_style(self, text: str, style_tag: str) -> str: @@ -938,7 +938,7 @@ def store_section(self, section: str) -> None: self.parents[self.level + 1] = self.doc.add_heading( heading.value, level=self.level, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) self.level += 1 @@ -959,7 +959,7 @@ def store_content(self, section: str, field: str, value: str) -> None: if field == self.Field.TITLE.value: self.parents[self.level + 1] = self.doc.add_title( - parent=self.parents[self.level], text=value # type: ignore[arg-type] + parent=self.parents[self.level], text=value ) self.level += 1 @@ -971,14 +971,14 @@ def store_content(self, section: str, field: str, value: str) -> None: self.doc.add_text( label=DocItemLabel.PARAGRAPH, text=value, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) elif field == self.Field.NUMBER.value and section == self.Section.CLAIMS.value: self.doc.add_text( label=DocItemLabel.PARAGRAPH, text="", - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) elif ( @@ -996,7 +996,7 @@ def store_content(self, section: str, field: str, value: str) -> None: last_claim = self.doc.add_text( label=DocItemLabel.PARAGRAPH, text="", - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) last_claim.text += f" {value}" if last_claim.text else value @@ -1012,7 +1012,7 @@ def store_content(self, section: str, field: str, value: str) -> None: self.parents[self.level + 1] = self.doc.add_heading( value, level=self.level, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) self.level += 1 @@ -1029,7 +1029,7 @@ def store_content(self, section: str, field: str, value: str) -> None: self.doc.add_text( label=DocItemLabel.PARAGRAPH, text=value, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) def parse(self, patent_content: str) -> Optional[DoclingDocument]: @@ -1283,7 +1283,7 @@ def _add_property(self, name: str, text: str) -> None: title = text.strip() if title: self.parents[self.level + 1] = self.doc.add_text( - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], label=DocItemLabel.TITLE, text=title, ) @@ -1301,7 +1301,7 @@ def _add_property(self, name: str, text: str) -> None: abstract_item = self.doc.add_heading( heading_text, level=heading_level, - parent=self.parents[heading_level], # type: ignore[arg-type] + parent=self.parents[heading_level], ) self.doc.add_text( label=DocItemLabel.PARAGRAPH, @@ -1331,7 +1331,7 @@ def _add_property(self, name: str, text: str) -> None: claims_item = self.doc.add_heading( heading_text, level=heading_level, - parent=self.parents[heading_level], # type: ignore[arg-type] + parent=self.parents[heading_level], ) for text in self.claims: self.doc.add_text( @@ -1350,14 +1350,14 @@ def _add_property(self, name: str, text: str) -> None: self.parents[self.level + 1] = self.doc.add_heading( text=text, level=self.level, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) self.level += 1 else: self.doc.add_text( label=DocItemLabel.PARAGRAPH, text=text, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) self.text = "" @@ -1366,7 +1366,7 @@ def _add_property(self, name: str, text: str) -> None: empty_table = TableData(num_rows=0, num_cols=0, table_cells=[]) self.doc.add_table( data=empty_table, - parent=self.parents[self.level], # type: ignore[arg-type] + parent=self.parents[self.level], ) def _apply_style(self, text: str, style_tag: str) -> str: diff --git a/docs/examples/backend_xml_rag.ipynb b/docs/examples/backend_xml_rag.ipynb new file mode 100644 index 00000000..aef8ce00 --- /dev/null +++ b/docs/examples/backend_xml_rag.ipynb @@ -0,0 +1,1078 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conversion of custom XML" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Step | Tech | Execution | \n", + "| --- | --- | --- |\n", + "| Embedding | Hugging Face / Sentence Transformers | ๐Ÿ’ป Local |\n", + "| Vector store | Milvus | ๐Ÿ’ป Local |\n", + "| Gen AI | Hugging Face Inference API | ๐ŸŒ Remote | " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is an example of using [Docling](https://ds4sd.github.io/docling/) for converting structured data (XML) into a unified document\n", + "representation format, `DoclingDocument`, and leverage its riched structured content for RAG applications.\n", + "\n", + "Data used in this example consist of patents from the [United States Patent and Trademark Office (USPTO)](https://www.uspto.gov/) and medical\n", + "articles from [PubMed Centralยฎ (PMC)](https://pmc.ncbi.nlm.nih.gov/).\n", + "\n", + "In this notebook, we accomplish the following:\n", + "- [Simple conversion](#simple-conversion) of supported XML files in a nutshell\n", + "- An [end-to-end application](#end-to-end-application) using public collections of XML files supported by Docling\n", + " - [Setup](#setup) the API access for generative AI\n", + " - [Fetch the data](#fetch-the-data) from USPTO and PubMed Centralยฎ sites, using Docling custom backends\n", + " - [Parse, chunk, and index](#parse-chunk-and-index) the documents in a vector database\n", + " - [Perform RAG](#question-answering-with-rag) using [LlamaIndex Docling extension](../../integrations/llamaindex/)\n", + " - [Delete the temporary files](#delete-temporary-files) used in notebook\n", + "\n", + "For more details on document chunking with Docling, refer to the [Chunking](../../concepts/chunking/) documentation. For RAG with Docling and LlamaIndex, also check the example [RAG with LlamaIndex](../rag_llamaindex/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple conversion\n", + "\n", + "The XML file format defines and stores data in a format that is both human-readable and machine-readable.\n", + "Because of this flexibility, Docling requires custom backend processors to interpret XML definitions and convert them into `DoclingDocument` objects.\n", + "\n", + "Some public data collections in XML format are already supported by Docling (USTPO patents and PMC articles). In these cases, the document conversion is straightforward and the same as with any other supported format, such as PDF or HTML. The execution example in [Simple Conversion](../minimal/) is the recommended usage of Docling for a single file:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ConversionStatus.SUCCESS\n" + ] + } + ], + "source": [ + "from docling.document_converter import DocumentConverter\n", + "\n", + "# a sample PMC article:\n", + "source = \"../../tests/data/pubmed/elife-56337.nxml\"\n", + "converter = DocumentConverter()\n", + "result = converter.convert(source)\n", + "print(result.status)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the document is converted, it can be exported to any format supported by Docling. For instance, to markdown (showing here the first lines only):" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# KRAB-zinc finger protein gene expansion in response to active retrotransposons in the murine lineage\n", + "\n", + "Wolf Gernot; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; de Iaco Alberto; 2: School of Life Sciences, ร‰cole Polytechnique Fรฉdรฉrale de Lausanne (EPFL): Lausanne: Switzerland; Sun Ming-An; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Bruno Melania; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Tinkham Matthew; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Hoang Don; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Mitra Apratim; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Ralls Sherry; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Trono Didier; 2: School of Life Sciences, ร‰cole Polytechnique Fรฉdรฉrale de Lausanne (EPFL): Lausanne: Switzerland; Macfarlan Todd S; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States\n", + "\n", + "## Abstract\n", + "\n", + "The Krรผppel-associated box zinc finger protein (KRAB-ZFP) family diversified in mammals. The majority of human KRAB-ZFPs bind transposable elements (TEs), however, since most TEs are inactive in humans it is unclear whether KRAB-ZFPs emerged to suppress TEs. We demonstrate that many recently emerged murine KRAB-ZFPs also bind to TEs, including the active ETn, IAP, and L1 families. Using a CRISPR/Cas9-based engineering approach, we genetically deleted five large clusters of KRAB-ZFPs and demonstrate that target TEs are de-repressed, unleashing TE-encoded enhancers. Homozygous knockout mice lacking one of two KRAB-ZFP gene clusters on chromosome 2 and chromosome 4 were nonetheless viable. In pedigrees of chromosome 4 cluster KRAB-ZFP mutants, we identified numerous novel ETn insertions with a modest increase in mutants. Our data strongly support the current model that recent waves of retrotransposon activity drove the expansion of KRAB-ZFP genes in mice and that many KRAB-ZFPs play a redundant role restricting TE activity.\n", + "\n" + ] + } + ], + "source": [ + "md_doc = result.document.export_to_markdown()\n", + "\n", + "delim = \"\\n\"\n", + "print(delim.join(md_doc.split(delim)[:8]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the XML file is not supported, a `ConversionError` message will be raised." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Input document docling_test.xml does not match any allowed format.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File format not allowed: docling_test.xml\n" + ] + } + ], + "source": [ + "from io import BytesIO\n", + "\n", + "from docling.datamodel.base_models import DocumentStream\n", + "from docling.exceptions import ConversionError\n", + "\n", + "xml_content = (\n", + " b'Random content'\n", + ")\n", + "stream = DocumentStream(name=\"docling_test.xml\", stream=BytesIO(xml_content))\n", + "try:\n", + " result = converter.convert(stream)\n", + "except ConversionError as ce:\n", + " print(ce)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can always refer to the [Usage](../../usage/#supported-formats) documentation page for a list of supported formats." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## End-to-end application\n", + "\n", + "This section describes a step-by-step application for processing XML files from supported public collections and use them for question-answering." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Requirements can be installed as shown below. The `--no-warn-conflicts` argument is meant for Colab's pre-populated Python environment, feel free to remove for stricter usage." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install -q --progress-bar off --no-warn-conflicts llama-index-core llama-index-readers-docling llama-index-node-parser-docling llama-index-embeddings-huggingface llama-index-llms-huggingface-api llama-index-vector-stores-milvus llama-index-readers-file python-dotenv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook uses HuggingFace's Inference API. For an increased LLM quota, a token can be provided via the environment variable `HF_TOKEN`.\n", + "\n", + "If you're running this notebook in Google Colab, make sure you [add](https://medium.com/@parthdasawant/how-to-use-secrets-in-google-colab-450c38e3ec75) your API key as a secret." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from warnings import filterwarnings\n", + "\n", + "from dotenv import load_dotenv\n", + "\n", + "\n", + "def _get_env_from_colab_or_os(key):\n", + " try:\n", + " from google.colab import userdata\n", + "\n", + " try:\n", + " return userdata.get(key)\n", + " except userdata.SecretNotFoundError:\n", + " pass\n", + " except ImportError:\n", + " pass\n", + " return os.getenv(key)\n", + "\n", + "\n", + "load_dotenv()\n", + "\n", + "filterwarnings(action=\"ignore\", category=UserWarning, module=\"pydantic\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now define the main parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "from tempfile import mkdtemp\n", + "\n", + "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n", + "from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n", + "\n", + "EMBED_MODEL_ID = \"BAAI/bge-small-en-v1.5\"\n", + "EMBED_MODEL = HuggingFaceEmbedding(model_name=EMBED_MODEL_ID)\n", + "TEMP_DIR = Path(mkdtemp())\n", + "MILVUS_URI = str(TEMP_DIR / \"docling.db\")\n", + "GEN_MODEL = HuggingFaceInferenceAPI(\n", + " token=_get_env_from_colab_or_os(\"HF_TOKEN\"),\n", + " model_name=\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n", + ")\n", + "embed_dim = len(EMBED_MODEL.get_text_embedding(\"hi\"))\n", + "# https://github.com/huggingface/transformers/issues/5486:\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fetch the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook we will use XML data from collections supported by Docling:\n", + "- Medical articles from the [PubMed Centralยฎ (PMC)](https://pmc.ncbi.nlm.nih.gov/). They are available in an [FTP server](https://ftp.ncbi.nlm.nih.gov/pub/pmc/) as `.tar.gz` files. Each file contains the full article data in XML format, among other supplementary files like images or spreadsheets.\n", + "- Patents from the [United States Patent and Trademark Office](https://www.uspto.gov/). They are available in the [Bulk Data Storage System (BDSS)](https://bulkdata.uspto.gov/) as zip files. Each zip file may contain several patents in XML format.\n", + "\n", + "The raw files will be downloaded form the source and saved in a temporary directory." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### PMC articles\n", + "\n", + "The [OA file](https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_file_list.csv) is a manifest file of all the PMC articles, including the URL path to download the source files. In this notebook we will use as example the article [Pathogens spread by high-altitude windborne mosquitoes](https://pmc.ncbi.nlm.nih.gov/articles/PMC11703268/), which is available in the archive file [PMC11703268.tar.gz](https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_package/e3/6b/PMC11703268.tar.gz)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_package/e3/6b/PMC11703268.tar.gz...\n", + "Extracting and storing the XML file containing the article text...\n", + "Stored XML file nihpp-2024.12.26.630351v1.nxml\n" + ] + } + ], + "source": [ + "import tarfile\n", + "from io import BytesIO\n", + "\n", + "import requests\n", + "\n", + "# PMC article PMC11703268\n", + "url: str = \"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_package/e3/6b/PMC11703268.tar.gz\"\n", + "\n", + "print(f\"Downloading {url}...\")\n", + "buf = BytesIO(requests.get(url).content)\n", + "print(\"Extracting and storing the XML file containing the article text...\")\n", + "with tarfile.open(fileobj=buf, mode=\"r:gz\") as tar_file:\n", + " for tarinfo in tar_file:\n", + " if tarinfo.isreg():\n", + " file_path = Path(tarinfo.name)\n", + " if file_path.suffix == \".nxml\":\n", + " with open(TEMP_DIR / file_path.name, \"wb\") as file_obj:\n", + " file_obj.write(tar_file.extractfile(tarinfo).read())\n", + " print(f\"Stored XML file {file_path.name}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### USPTO patents\n", + "\n", + "Since each USPTO file is a concatenation of several patents, we need to split its content into valid XML pieces. The following code downloads a sample zip file, split its content in sections, and dumps each section as an XML file. For simplicity, this pipeline is shown here in a sequential manner, but it could be parallelized." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import zipfile\n", + "\n", + "# Patent grants from December 17-23, 2024\n", + "url: str = (\n", + " \"https://bulkdata.uspto.gov/data/patent/grant/redbook/fulltext/2024/ipg241217.zip\"\n", + ")\n", + "XML_SPLITTER: str = ' 0\n", + " ): # cases like 0 and is_patent:\n", + " doc_num += 1\n", + " patent_id = f\"ipg241217-{doc_num}\"\n", + " with open(TEMP_DIR / f\"{patent_id}.xml\", \"wb\") as file_obj:\n", + " file_obj.write(patent_buffer.getbuffer())\n", + " is_patent = False\n", + " patent_buffer = BytesIO()\n", + " elif decoded_line.startswith(\"" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index.from_documents(\n", + " documents=reader.load_data(TEMP_DIR / \"nihpp-2024.12.26.630351v1.nxml\"),\n", + " transformations=[node_parser],\n", + " storage_context=StorageContext.from_defaults(vector_store=vector_store),\n", + " embed_model=EMBED_MODEL,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question-answering with RAG" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The retriever can be used to identify highly relevant documents:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Node ID: 5afd36c0-a739-4a88-a51c-6d0f75358db5\n", + "Text: The portable fitness monitoring device 102 may be a device such\n", + "as, for example, a mobile phone, a personal digital assistant, a music\n", + "file player (e.g. and MP3 player), an intelligent article for wearing\n", + "(e.g. a fitness monitoring garment, wrist band, or watch), a dongle\n", + "(e.g. a small hardware device that protects software) that includes a\n", + "fitn...\n", + "Score: 0.772\n", + "\n", + "Node ID: f294b5fd-9089-43cb-8c4e-d1095a634ff1\n", + "Text: US Patent Application US 20120071306 entitled โ€œPortable\n", + "Multipurpose Whole Body Exercise Deviceโ€ discloses a portable\n", + "multipurpose whole body exercise device which can be used for general\n", + "fitness, Pilates-type, core strengthening, therapeutic, and\n", + "rehabilitative exercises as well as stretching and physical therapy\n", + "and which includes storable acc...\n", + "Score: 0.749\n", + "\n", + "Node ID: 8251c7ef-1165-42e1-8c91-c99c8a711bf7\n", + "Text: Program products, methods, and systems for providing fitness\n", + "monitoring services of the present invention can include any software\n", + "application executed by one or more computing devices. A computing\n", + "device can be any type of computing device having one or more\n", + "processors. For example, a computing device can be a workstation,\n", + "mobile device (e.g., ...\n", + "Score: 0.744\n", + "\n" + ] + } + ], + "source": [ + "retriever = index.as_retriever(similarity_top_k=3)\n", + "results = retriever.retrieve(\"What patents are related to fitness devices?\")\n", + "\n", + "for item in results:\n", + " print(item)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the query engine, we can run the question-answering with the RAG pattern on the set of indexed documents.\n", + "\n", + "First, we can prompt the LLM directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ Prompt โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\n",
+       "โ”‚ Do mosquitoes in high altitude expand viruses over large distances?                                             โ”‚\n",
+       "โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;31mโ•ญโ”€\u001b[0m\u001b[1;31mโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\u001b[0m\u001b[1;31m Prompt \u001b[0m\u001b[1;31mโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\u001b[0m\u001b[1;31mโ”€โ•ฎ\u001b[0m\n", + "\u001b[1;31mโ”‚\u001b[0m Do mosquitoes in high altitude expand viruses over large distances? \u001b[1;31mโ”‚\u001b[0m\n", + "\u001b[1;31mโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ Generated Content โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\n",
+       "โ”‚ Mosquitoes can be found at high altitudes, but their ability to transmit viruses over long distances is not     โ”‚\n",
+       "โ”‚ primarily dependent on altitude. Mosquitoes are vectors for various diseases, such as malaria, dengue fever,    โ”‚\n",
+       "โ”‚ and Zika virus, and their transmission range is more closely related to their movement, the presence of a host, โ”‚\n",
+       "โ”‚ and environmental conditions that support their survival and reproduction.                                      โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ At high altitudes, the environment can be less suitable for mosquitoes due to factors such as colder            โ”‚\n",
+       "โ”‚ temperatures, lower humidity, and stronger winds, which can limit their population size and distribution.       โ”‚\n",
+       "โ”‚ However, some species of mosquitoes have adapted to high-altitude environments and can still transmit diseases  โ”‚\n",
+       "โ”‚ in these areas.                                                                                                 โ”‚\n",
+       "โ”‚                                                                                                                 โ”‚\n",
+       "โ”‚ It is possible for mosquitoes to be transported by wind or human activities to higher altitudes, but this is    โ”‚\n",
+       "โ”‚ not a significant factor in their ability to transmit viruses over long distances. Instead, long-distance       โ”‚\n",
+       "โ”‚ transmission of viruses is more often associated with human travel and transportation, which can rapidly spread โ”‚\n",
+       "โ”‚ infected mosquitoes or humans to new areas, leading to the spread of disease.                                   โ”‚\n",
+       "โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;32mโ•ญโ”€\u001b[0m\u001b[1;32mโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\u001b[0m\u001b[1;32m Generated Content \u001b[0m\u001b[1;32mโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\u001b[0m\u001b[1;32mโ”€โ•ฎ\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m Mosquitoes can be found at high altitudes, but their ability to transmit viruses over long distances is not \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m primarily dependent on altitude. Mosquitoes are vectors for various diseases, such as malaria, dengue fever, \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m and Zika virus, and their transmission range is more closely related to their movement, the presence of a host, \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m and environmental conditions that support their survival and reproduction. \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m At high altitudes, the environment can be less suitable for mosquitoes due to factors such as colder \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m temperatures, lower humidity, and stronger winds, which can limit their population size and distribution. \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m However, some species of mosquitoes have adapted to high-altitude environments and can still transmit diseases \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m in these areas. \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m It is possible for mosquitoes to be transported by wind or human activities to higher altitudes, but this is \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m not a significant factor in their ability to transmit viruses over long distances. Instead, long-distance \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m transmission of viruses is more often associated with human travel and transportation, which can rapidly spread \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m infected mosquitoes or humans to new areas, leading to the spread of disease. \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from llama_index.core.base.llms.types import ChatMessage, MessageRole\n", + "from rich.console import Console\n", + "from rich.panel import Panel\n", + "\n", + "console = Console()\n", + "query = \"Do mosquitoes in high altitude expand viruses over large distances?\"\n", + "\n", + "usr_msg = ChatMessage(role=MessageRole.USER, content=query)\n", + "response = GEN_MODEL.chat(messages=[usr_msg])\n", + "\n", + "console.print(Panel(query, title=\"Prompt\", border_style=\"bold red\"))\n", + "console.print(\n", + " Panel(\n", + " response.message.content.strip(),\n", + " title=\"Generated Content\",\n", + " border_style=\"bold green\",\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can compare the response when the model is prompted with the indexed PMC article as supporting context:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ Generated Content with RAG โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\n",
+       "โ”‚ Yes, mosquitoes in high altitude can expand viruses over large distances. A study intercepted 1,017 female      โ”‚\n",
+       "โ”‚ mosquitoes at altitudes of 120-290 m above ground over Mali and Ghana and screened them for infection with      โ”‚\n",
+       "โ”‚ arboviruses, plasmodia, and filariae. The study found that 3.5% of the mosquitoes were infected with            โ”‚\n",
+       "โ”‚ flaviviruses, and 1.1% were infectious. Additionally, the study identified 19 mosquito-borne pathogens,         โ”‚\n",
+       "โ”‚ including three arboviruses that affect humans (dengue, West Nile, and Mโ€™Poko viruses). The study provides      โ”‚\n",
+       "โ”‚ compelling evidence that mosquito-borne pathogens are often spread by windborne mosquitoes at altitude.         โ”‚\n",
+       "โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;32mโ•ญโ”€\u001b[0m\u001b[1;32mโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\u001b[0m\u001b[1;32m Generated Content with RAG \u001b[0m\u001b[1;32mโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€\u001b[0m\u001b[1;32mโ”€โ•ฎ\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m Yes, mosquitoes in high altitude can expand viruses over large distances. A study intercepted 1,017 female \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m mosquitoes at altitudes of 120-290 m above ground over Mali and Ghana and screened them for infection with \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m arboviruses, plasmodia, and filariae. The study found that 3.5% of the mosquitoes were infected with \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m flaviviruses, and 1.1% were infectious. Additionally, the study identified 19 mosquito-borne pathogens, \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m including three arboviruses that affect humans (dengue, West Nile, and Mโ€™Poko viruses). The study provides \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ”‚\u001b[0m compelling evidence that mosquito-borne pathogens are often spread by windborne mosquitoes at altitude. \u001b[1;32mโ”‚\u001b[0m\n", + "\u001b[1;32mโ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n", + "\n", + "filters = MetadataFilters(\n", + " filters=[\n", + " ExactMatchFilter(key=\"filename\", value=\"nihpp-2024.12.26.630351v1.nxml\"),\n", + " ]\n", + ")\n", + "\n", + "query_engine = index.as_query_engine(llm=GEN_MODEL, filter=filters, similarity_top_k=3)\n", + "result = query_engine.query(query)\n", + "\n", + "console.print(\n", + " Panel(\n", + " result.response.strip(),\n", + " title=\"Generated Content with RAG\",\n", + " border_style=\"bold green\",\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Delete temporary files\n", + "\n", + "The XML files used in this notebook, as well as the Milvus local database will be removed." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "shutil.rmtree(TEMP_DIR)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/usage.md b/docs/usage.md index 9a5b555a..824f0f22 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -24,6 +24,20 @@ docling https://arxiv.org/pdf/2206.01062 To see all available options (export formats etc.) run `docling --help`. More details in the [CLI reference page](./reference/cli.md). +### Supported formats + +The document conversion in Docling supports several popular formats, including: + +- **PDF** (Portable Document Format): the format developed by Adobe to present documents compatible across application software, hardware, and operating systems. +- **.docx**, **.xlsx**, **.pptx** (Word, Excel, and PowerPoint): the Open XML formats suppored by Microsof Office. +- **Markdown**: a lightweight markup language to add formatting elements to plain text documents. +- **AsciiDoc**: a plain text markup language for writing technical content. +- **HTML** (Hypertext Markup Language): the standard markup language for creating web pages. +- **XHTML** (Extensible Hypertext Markup Language): the XML-based version of HTML. +- **XML** (Extensible Markup Language): a markup format for storing and transmitting data. Due to its flexibility, Docling requires custom implementations to identify the +semantics of the data. Currently, Docling supports the parsing of [USPTO](https://www.uspto.gov/patents) patents and [PubMed Centralยฎ (PMC)](https://pmc.ncbi.nlm.nih.gov/) articles. + + ### Advanced options #### Adjust pipeline features @@ -126,6 +140,32 @@ result = converter.convert(source) You can limit the CPU threads used by Docling by setting the environment variable `OMP_NUM_THREADS` accordingly. The default setting is using 4 CPU threads. +#### Use specific backend converters + +By default, Docling will try to identify the document format to apply the appropriate conversion backend (see the list of [supported formats](#supported-formats)). +You can restrict the `DocumentConverter` to a set of allowed document formats, as shown in the [Multi-format conversion](./examples/run_with_formats.py) example. +Alternatively, you can also use the specific backend that matches your document content. For instance, you can use `HTMLDocumentBackend` for HTML pages: + +```python +import urllib.request +from io import BytesIO +from docling.backend.html_backend import HTMLDocumentBackend +from docling.datamodel.base_models import InputFormat +from docling.datamodel.document import InputDocument + +url = "https://en.wikipedia.org/wiki/Duck" +text = urllib.request.urlopen(url).read() +in_doc = InputDocument( + path_or_stream=BytesIO(text), + format=InputFormat.HTML, + backend=HTMLDocumentBackend, + filename="duck.html", +) +backend = HTMLDocumentBackend(in_doc=in_doc, path_or_stream=BytesIO(text)) +result = backend.convert() +print(result.export_to_markdown()) +``` + ## Chunking You can chunk a Docling document using a [chunker](concepts/chunking.md), such as a diff --git a/mkdocs.yml b/mkdocs.yml index 2b2e2da0..bbff382e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -77,7 +77,8 @@ nav: - "Force full page OCR": examples/full_page_ocr.py - "Automatic OCR language detection with tesseract": examples/tesseract_lang_detection.py - "Accelerator options": examples/run_with_accelerator.py - - "Simple translation": examples/translate.py + - "Simple translation": examples/translate.py + - examples/backend_xml_rag.ipynb - โœ‚๏ธ Chunking: - examples/hybrid_chunking.ipynb - ๐Ÿค– RAG with AI dev frameworks: diff --git a/tests/test_backend_msexcel.py b/tests/test_backend_msexcel.py index 0a4440dc..e664ed34 100644 --- a/tests/test_backend_msexcel.py +++ b/tests/test_backend_msexcel.py @@ -2,13 +2,8 @@ import os from pathlib import Path -from docling.backend.msword_backend import MsWordDocumentBackend from docling.datamodel.base_models import InputFormat -from docling.datamodel.document import ( - ConversionResult, - InputDocument, - SectionHeaderItem, -) +from docling.datamodel.document import ConversionResult from docling.document_converter import DocumentConverter GENERATE = False diff --git a/tests/test_backend_patent_uspto.py b/tests/test_backend_patent_uspto.py index 466568ac..21bc88c5 100644 --- a/tests/test_backend_patent_uspto.py +++ b/tests/test_backend_patent_uspto.py @@ -3,23 +3,16 @@ import json import logging import os -import unittest from pathlib import Path from tempfile import NamedTemporaryFile import pytest -import yaml from docling_core.types import DoclingDocument from docling_core.types.doc import DocItemLabel, TableData, TextItem from docling.backend.xml.uspto_backend import PatentUsptoDocumentBackend, XmlTable from docling.datamodel.base_models import InputFormat -from docling.datamodel.document import ( - ConversionResult, - InputDocument, - SectionHeaderItem, -) -from docling.document_converter import DocumentConverter +from docling.datamodel.document import InputDocument GENERATE: bool = True DATA_PATH: Path = Path("./tests/data/uspto/") diff --git a/tests/test_backend_pubmed.py b/tests/test_backend_pubmed.py index 4476bd24..8481c3dd 100644 --- a/tests/test_backend_pubmed.py +++ b/tests/test_backend_pubmed.py @@ -1,5 +1,4 @@ import json -import logging import os from io import BytesIO from pathlib import Path From b885b2fa3c2519c399ed4b9a3dd4c2f6f62235d1 Mon Sep 17 00:00:00 2001 From: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> Date: Mon, 27 Jan 2025 02:40:35 -0600 Subject: [PATCH 11/12] docs: added markdown headings to enable TOC in github pages (#808) * docs: added markdown headings to enable TOC in github pages Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> * minor renames Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> * part 3 heading Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> --------- Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> --- docs/examples/rag_azuresearch.ipynb | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/docs/examples/rag_azuresearch.ipynb b/docs/examples/rag_azuresearch.ipynb index 4350ec2b..4f405dea 100644 --- a/docs/examples/rag_azuresearch.ipynb +++ b/docs/examples/rag_azuresearch.ipynb @@ -33,6 +33,7 @@ "metadata": {}, "source": [ "\n", + "## A recipe ๐Ÿง‘โ€๐Ÿณ ๐Ÿฅ ๐Ÿ’š\n", "\n", "This notebook demonstrates how to build a Retrieval-Augmented Generation (RAG) system using:\n", "- [Docling](https://ds4sd.github.io/docling/) for document parsing and chunking\n", @@ -61,7 +62,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Part 0: Prerequisites\n", + "### Part 0: Prerequisites\n", " - **Azure AI Search** resource\n", " - **Azure OpenAI** resource with a deployed embedding and chat completion model (e.g. `text-embedding-3-small` and `gpt-4o`) \n", " - **Docling 2.12+** (installs `docling_core` automatically) Docling installed (Python 3.8+ environment)\n", @@ -114,7 +115,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Part 1: Parse the PDF with Docling\n", + "### Part 1: Parse the PDF with Docling\n", "\n", "Weโ€™ll parse the **Microsoft GraphRAG Research Paper** (~15 pages). Parsing should be relatively quick, even on CPU, but it will be faster on a GPU or MPS device if available.\n", "\n", @@ -235,7 +236,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Part 2: Hierarchical Chunking\n", + "### Part 2: Hierarchical Chunking\n", "We convert the `Document` into smaller chunks for embedding and indexing. The built-in `HierarchicalChunker` preserves structure. " ] }, @@ -276,7 +277,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Part 3: Create Azure Search Index and Push Chunk Embeddings\n", + "### Part 3: Create Azure Search Index and Push Chunk Embeddings\n", "Weโ€™ll define a vector index in Azure AI Search, then embed each chunk using Azure OpenAI and upload in batches." ] }, @@ -382,7 +383,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Embed and Upsert to Azure AI Search\n" + "#### Generate Embeddings and Upload to Azure AI Search\n" ] }, { @@ -497,8 +498,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Part 4: RAG Query with Azure OpenAI\n", - "Combine retrieval from Azure Search with Chat Completions (aka. grounding your LLM)" + "### Part 4: Perform RAG I\n", + "Combine retrieval from Azure AI Search with Azure OpenAI Chat Completions (aka. grounding your LLM)" ] }, { From 8a4ec77576b8a9fd60d0047939665d00cf93b4dd Mon Sep 17 00:00:00 2001 From: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> Date: Mon, 27 Jan 2025 04:24:26 -0600 Subject: [PATCH 12/12] docs: typo (#814) * Update rag_azuresearch.ipynb Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> * typo Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> --------- Signed-off-by: Farzad Sunavala <40604067+farzad528@users.noreply.github.com> --- docs/examples/rag_azuresearch.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/examples/rag_azuresearch.ipynb b/docs/examples/rag_azuresearch.ipynb index 4f405dea..dcfd19e3 100644 --- a/docs/examples/rag_azuresearch.ipynb +++ b/docs/examples/rag_azuresearch.ipynb @@ -277,7 +277,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Part 3: Create Azure Search Index and Push Chunk Embeddings\n", + "### Part 3: Create Azure AI Search Index and Push Chunk Embeddings\n", "Weโ€™ll define a vector index in Azure AI Search, then embed each chunk using Azure OpenAI and upload in batches." ] }, @@ -498,7 +498,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Part 4: Perform RAG I\n", + "### Part 4: Perform RAG over PDF\n", "Combine retrieval from Azure AI Search with Azure OpenAI Chat Completions (aka. grounding your LLM)" ] },