helwod/docling repository overview

⁠Source Repository

https://github.com/docling-project/docling⁠

⁠

⁠Docling

⁠What is Docling ?

Docling simplifies document processing by parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the generative AI ecosystem.

⁠Features

• 🗂️ Parsing of multiple document 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 representation format
• ↪️ Various export formats⁠ and options, including Markdown, HTML, WebVTT, DocLang, DocTags⁠ and lossless JSON
• 📜 Support for several application-specific XML schemas including DocLang⁠, USPTO⁠ patents, JATS⁠ articles, and XBRL⁠ financial reports.
• 🔒 Local execution capabilities for sensitive data and air-gapped environments
• 🤖 Plug-and-play 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)
• 🎙️ Audio support with Automatic Speech Recognition (ASR) models
• 🔌 Connect to any agent using the MCP server⁠
• 🌐 Run Docling as a service with the API server⁠ (docling-serve)
• 💻 Simple and convenient CLI

⁠What's new

• 🎬 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

⁠Coming soon

• 📝 Metadata extraction, including title, authors, references & language
• 📝 Complex chemistry understanding (Molecular structures)

⁠Quickstart

⁠1. Install

pip install docling

Works on macOS, Linux and Windows environments for both x86_64 and arm64 architectures.

More detailed installation instructions⁠ are available in the docs.

⁠2. Convert a document (CLI)

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 and other VLMs via Docling CLI:

docling --pipeline vlm --vlm-model granite_docling https://arxiv.org/pdf/2206.01062

⁠3. Python usage (recommended)

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 and configuration⁠ options.

⁠Documentation

Check out Docling's documentation⁠ for details on installation, usage, concepts, recipes, extensions, and more.

⁠Examples

Go hands-on with our examples⁠, demonstrating how to address different application use cases with Docling.

⁠Integrations

To further accelerate your AI application development, check out Docling's native integrations⁠ with popular frameworks and tools.

⁠Get help and support

Please feel free to connect with us using the discussion section⁠.

⁠Technical report

For more details on Docling's inner workings, check out the Docling Technical Report⁠.

⁠Contributing

Please read Contributing to Docling⁠ for details.

⁠References

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}
}

⁠License

The Docling codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.

⁠LF AI & Data

Docling is hosted as a project in the LF AI & Data Foundation⁠.

⁠IBM ❤️ Open Source AI

The project was started by the AI for knowledge team at IBM Research Zurich.