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, Apple Pages, 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 Apple Pages (
.pages) documents, both container generations (Pages 5+ and iWork '09) - 📝 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 AI & Data Foundation.
IBM ❤️ Open Source AI
The project was started by the AI for knowledge team at IBM Research Zurich.
Release files for docling 2.128.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| docling-2.128.0.tar.gz | 9.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| docling-2.128.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 14.3 kB
Release files / docling-2.128.0.tar.gz
| Download URL | docling-2.128.0.tar.gz |
|---|---|
| Size | 9.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / docling-2.128.0-py3-none-any.whl
| Download URL | docling-2.128.0-py3-none-any.whl |
|---|---|
| Size | 5.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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