Docling

IBM released Docling, an open-source document processing tool that parses diverse formats including PDF, DOCX, and images, with integrations for generative AI ecosystems like LangChain and LlamaIndex. The tool supports local execution for sensitive data and offers advanced features such as OCR, visual language models, and audio transcription.

Docling simplifies document processing by parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the generative AI ecosystem. - 🗂️ Parsing of multiple document formats https://docling-project.github.io/docling/usage/supported formats/ including PDF, DOCX, PPTX, XLSX, HTML, EPUB, WAV, MP3, WebVTT, Box Notes, email formats EML, MSG , images PNG, TIFF, JPEG, ... , LaTeX, DocLang, plain text, and more - 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more - 🧬 A unified, expressive DoclingDocument https://docling-project.github.io/docling/concepts/docling document/ representation format - ↪️ Various export formats https://docling-project.github.io/docling/usage/supported formats/ and options, including Markdown, HTML, WebVTT, DocLang, DocTags https://arxiv.org/abs/2503.11576 and lossless JSON - 📜 Support for several application-specific XML schemas including DocLang https://doclang.ai , USPTO https://www.uspto.gov/patents patents, JATS https://jats.nlm.nih.gov/ articles, and XBRL https://www.xbrl.org/ financial reports. - 🔒 Local execution capabilities for sensitive data and air-gapped environments - 🤖 Plug-and-play integrations https://docling-project.github.io/docling/integrations/ incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI - 🔍 Extensive OCR support for scanned PDFs and images - 👓 Support for several Visual Language Models, such as GraniteDocling https://huggingface.co/ibm-granite/granite-docling-258M - 🎙️ Audio support with Automatic Speech Recognition ASR models - 🔌 Connect to any agent using the MCP server https://docling-project.github.io/docling/usage/mcp/ - 🌐 Run Docling as a service with the API server https://docling-project.github.io/docling/usage/api server/ docling-serve - 💻 Simple and convenient CLI - 🎬 Parsing of video files MP4, AVI, MOV, MKV, and WebM with an ASR transcript and representative keyframes - 📄 Parsing of ODF OpenDocument Format files for text documents .odt , spreadsheets .ods , and presentations .odp - 💼 Parsing of XBRL eXtensible Business Reporting Language documents for financial reports - 📧 Parsing of email files .eml , .msg - 📚 Parsing of EPUB Electronic Publication files for e-books - 📝 Parsing of plain-text files .txt , .text and Markdown supersets .qmd , .Rmd - 📊 Chart understanding Barchart, Piechart, LinePlot : convert them into tables or code and add detailed descriptions - 📝 Metadata extraction, including title, authors, references & language - 📝 Complex chemistry understanding Molecular structures pip install docling Note:Python 3.9 support was dropped in docling version 2.70.0. Please use Python 3.10 or higher. Works on macOS, Linux and Windows environments for both x86 64 and arm64 architectures. More detailed installation instructions https://docling-project.github.io/docling/getting started/installation/ are available in the docs. docling https://arxiv.org/pdf/2206.01062 This generates a .md file in the current directory containing structured document content. You can also use 🥚 GraniteDocling https://huggingface.co/ibm-granite/granite-docling-258M and other VLMs via Docling CLI: docling --pipeline vlm --vlm-model granite docling https://arxiv.org/pdf/2206.01062 python from docling.document converter import DocumentConverter source = "https://arxiv.org/pdf/2408.09869" a document via a local path or URL converter = DocumentConverter result = converter.convert source print result.document.export to markdown output: " Docling Technical Report ... " More advanced usage https://docling-project.github.io/docling/usage/ and configuration https://docling-project.github.io/docling/getting started/installation/ options. Check out Docling's documentation https://docling-project.github.io/docling/ for details on installation, usage, concepts, recipes, extensions, and more. Go hands-on with our examples https://docling-project.github.io/docling/examples/ , demonstrating how to address different application use cases with Docling. To further accelerate your AI application development, check out Docling's native integrations https://docling-project.github.io/docling/integrations/ with popular frameworks and tools. Please feel free to connect with us using the discussion section https://github.com/docling-project/docling/discussions . For more details on Docling's inner workings, check out the Docling Technical Report https://arxiv.org/abs/2408.09869 . Please read Contributing to Docling https://github.com/docling-project/docling/blob/main/CONTRIBUTING.md for details. If you use Docling in your projects, please consider citing the following: @techreport{Docling, author = {Deep Search Team}, month = {8}, title = {Docling Technical Report}, url = {https://arxiv.org/abs/2408.09869}, eprint = {2408.09869}, doi = {10.48550/arXiv.2408.09869}, version = {1.0.0}, year = {2024} } The Docling codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages. Docling is hosted as a project in the LF AI & Data Foundation https://lfaidata.foundation/projects/ . The project was started by the AI for knowledge team at IBM Research Zurich.