README.md

Docling

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

Features

• 🗂️ Parsing of multiple document formats incl. PDF, DOCX, XLSX, HTML, images, and more
• 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
• 🧬 Unified, expressive DoclingDocument representation format
• ↪️ Various export formats and options, including Markdown, HTML, and lossless JSON
• 🔒 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
• 💻 Simple and convenient CLI

Coming soon

• 📝 Metadata extraction, including title, authors, references & language

Installation

To use Docling, simply install docling from your package manager, e.g. pip:

pip install docling

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

More detailed installation instructions are available in the docs.

Getting started

To convert individual documents, use convert(), for example:

from docling.document_converter import DocumentConverter

source = "https://arxiv.org/pdf/2408.09869"  # document per local path or URL
converter = DocumentConverter()
result = converter.convert(source)
print(result.document.export_to_markdown())  # output: "## Docling Technical Report[...]"

More advanced usage options are available in the docs.

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.

IBM ❤️ Open Source AI

Docling has been brought to you by IBM.