dignified-python

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][supported_formats] including PDF, DOCX, PPTX, XLSX, HTML, EPUB, Apple Pages, Numbers & Keynote, 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][docling_document] representation format
• ↪️ Various [export formats][supported_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][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 Apple Pages (.pages) documents, Numbers (.numbers) spreadsheets and Keynote (.key) presentations
• 📝 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

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 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 A