Docling

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 formatsincluding 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 DoclingDocumentrepresentation format - ↪️ Various export formatsand options, including Markdown, HTML, WebVTT, DocLang,DocTagsand lossless JSON - 📜 Support for several application-specific XML schemas including DocLang,USPTOpatents,JATSarticles, andXBRLfinancial reports. - 🔒 Local execution capabilities for sensitive data and air-gapped environments

• 🤖 Plug-and-play integrationsincl. 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

• 🎬 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 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 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 and configuration options.

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

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

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

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

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

Please read Contributing to Docling 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.

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