
docling
Get your documents ready for gen AI
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Docling simplifies document processing, parsing diverse formats including advanced PDF understanding, and providing seamless integrations with the general AI ecosystem. It offers features such as parsing multiple document formats, advanced PDF understanding, unified DoclingDocument representation format, various export formats, local execution capabilities, plug-and-play integrations with agentic AI tools, extensive OCR support, and a simple CLI. Coming soon features include metadata extraction, visual language models, chart understanding, and complex chemistry understanding. Docling is installed via pip and works on macOS, Linux, and Windows environments. It provides detailed documentation, examples, integrations with popular frameworks, and support through the discussion section. The codebase is under the MIT license and has been developed by IBM.
README:
Docling simplifies document processing, parsing diverse formats β including advanced PDF understanding β and providing seamless integrations with the gen AI ecosystem.
- ποΈ Parsing of multiple document formats incl. PDF, DOCX, PPTX, XLSX, HTML, WAV, MP3, images (PNG, TIFF, JPEG, ...), and more
- π Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
- 𧬠Unified, expressive DoclingDocument representation format
- βͺοΈ Various export formats and options, including Markdown, HTML, DocTags and lossless JSON
- π Local execution capabilities for sensitive data and air-gapped environments
- π€ Plug-and-play integrations incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
- π Extensive OCR support for scanned PDFs and images
- π Support of several Visual Language Models (SmolDocling)
- ποΈ Audio support with Automatic Speech Recognition (ASR) models
- π Connect to any agent using the MCP server
- π» Simple and convenient CLI
- π€ Structured information extraction [π§ͺ beta]
- π New layout model (Heron) by default, for faster PDF parsing
- π MCP server for agentic applications
- π Metadata extraction, including title, authors, references & language
- π Chart understanding (Barchart, Piechart, LinePlot, etc)
- π Complex chemistry understanding (Molecular structures)
- π Parsing of Web Video Text Tracks (WebVTT) files
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.
To convert individual documents with python, 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.
Docling has a built-in CLI to run conversions.
docling https://arxiv.org/pdf/2206.01062
You can also use π₯SmolDocling and other VLMs via Docling CLI:
docling --pipeline vlm --vlm-model smoldocling https://arxiv.org/pdf/2206.01062
This will use MLX acceleration on supported Apple Silicon hardware.
Read more here
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.
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