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, Numbers & Keynote, WAV, MP3, WebVTT, Box Notes, email formats (EML, MSG), iCalendar (ICS), 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) and iXBRL (Inline eXtensible Business Reporting Language) documents for financial reports
• 📧 Parsing of email files (.eml, .msg)
• 📅 Parsing of calendar files (.ics, .ical, .icalendar, .ifb) with events, tasks, journal entries and free/busy times, with attendees, reminders and attachment names
• 📚 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 --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[...]"

This is the beginning of the README. Read the rest at the original. Read the full README ↗