<p align="center">
<a href="https://github.com/docling-project/docling">
<img loading="lazy" alt="Docling" src="https://github.com/docling-project/docling/raw/main/docs/assets/docling_processing.png" width="100%"/>
</a>
</p>
# Docling
<p align="center">
<a href="https://trendshift.io/repositories/12132" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12132" alt="DS4SD%2Fdocling | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</p>
[](https://arxiv.org/abs/2408.09869)
[](https://docling-project.github.io/docling/)
[](https://pypi.org/project/docling/)
[](https://pypi.org/project/docling/)
[](https://github.com/astral-sh/uv)
[](https://github.com/astral-sh/ruff)
[](https://pydantic.dev)
[](https://github.com/pre-commit/pre-commit)
[](https://opensource.org/licenses/MIT)
[](https://pepy.tech/projects/docling)
[](https://apify.com/vancura/docling)
[](https://app.dosu.dev/097760a8-135e-4789-8234-90c8837d7f1c/ask?utm_source=github)
[](https://docling.ai/discord)
[](https://www.bestpractices.dev/projects/10101)
[](https://lfaidata.foundation/projects/)
Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
## Features
* 🗂️ Parsing of [multiple document formats][supported_formats] incl. PDF, DOCX, PPTX, XLSX, HTML, WAV, MP3, VTT, images (PNG, TIFF, JPEG, ...), and more
* 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
* 🧬 Unified, expressive [DoclingDocument][docling_document] representation format
* ↪️ Various [export formats][supported_formats] and options, including Markdown, HTML, [DocTags](https://arxiv.org/abs/2503.11576) and lossless JSON
* 🔒 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 of several Visual Language Models ([GraniteDocling](https://huggingface.co/ibm-granite/granite-docling-258M))
* 🎙️ Audio support with Automatic Speech Recognition (ASR) models
* 🔌 Connect to any agent using the [MCP server](https://docling-project.github.io/docling/usage/mcp/)
* 💻 Simple and convenient CLI
### What's new
* 📤 Structured [information extraction][extraction] \[🧪 beta\]
* 📑 New layout model (**Heron**) by default, for faster PDF parsing
* 🔌 [MCP server](https://docling-project.github.io/docling/usage/mcp/) for agentic applications
* 💬 Parsing of Web Video Text Tracks (WebVTT) files
### Coming soon
* 📝 Metadata extraction, including title, authors, references & language
* 📝 Chart understanding (Barchart, Piechart, LinePlot, etc)
* 📝 Complex chemistry understanding (Molecular structures)
## Installation
To use Docling, simply install `docling` from your package manager, e.g. pip:
```bash
pip install docling
```
Works on macOS, Linux and Windows environments. Both x86_64 and arm64 architectures.
More [detailed installation instructions](https://docling-project.github.io/docling/installation/) are available in the docs.
## Getting started
To convert individual documents with python, use `convert()`, for example:
```python
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](https://docling-project.github.io/docling/usage/advanced_options/) are available in
the docs.
## CLI
Docling has a built-in CLI to run conversions.
```bash
docling https://arxiv.org/pdf/2206.01062
```
You can also use 🥚[GraniteDocling](https://huggingface.co/ibm-granite/granite-docling-258M) and other VLMs via Docling CLI:
```bash
docling --pipeline vlm --vlm-model granite_docling https://arxiv.org/pdf/2206.01062
```
This will use MLX acceleration on supported Apple Silicon hardware.
Read more [here](https://docling-project.github.io/docling/usage/)
## Documentation
Check out Docling's [documentation](https://docling-project.github.io/docling/), for details on
installation, usage, concepts, recipes, extensions, and more.
## Examples
Go hands-on with our [examples](https://docling-project.github.io/docling/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](https://docling-project.github.io/docling/integrations/) with popular frameworks
and tools.
## Get help and support
Please feel free to connect with us using the [discussion section](https://github.com/docling-project/docling/discussions).
## Technical report
For more details on Docling's inner workings, check out the [Docling Technical Report](https://arxiv.org/abs/2408.09869).
## Contributing
Please read [Contributing to Docling](https://github.com/docling-project/docling/blob/main/CONTRIBUTING.md) for details.
## References
If you use Docling in your projects, please consider citing the following:
```bib
@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](https://lfaidata.foundation/projects/).
### IBM ❤️ Open Source AI
The project was started by the AI for knowledge team at IBM Research Zurich.
[supported_formats]: https://docling-project.github.io/docling/usage/supported_formats/
[docling_document]: https://docling-project.github.io/docling/concepts/docling_document/
[integrations]: https://docling-project.github.io/docling/integrations/
[extraction]: https://docling-project.github.io/docling/examples/extraction/
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PDF, DOCX, PPTX, XLSX, HTML, WAV, MP3, VTT, images (PNG, TIFF, JPEG, ...), and more\n* \ud83d\udcd1 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more\n* \ud83e\uddec Unified, expressive [DoclingDocument][docling_document] representation format\n* \u21aa\ufe0f Various [export formats][supported_formats] and options, including Markdown, HTML, [DocTags](https://arxiv.org/abs/2503.11576) and lossless JSON\n* \ud83d\udd12 Local execution capabilities for sensitive data and air-gapped environments\n* \ud83e\udd16 Plug-and-play [integrations][integrations] incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI\n* \ud83d\udd0d Extensive OCR support for scanned PDFs and images\n* \ud83d\udc53 Support of several Visual Language Models ([GraniteDocling](https://huggingface.co/ibm-granite/granite-docling-258M))\n* \ud83c\udf99\ufe0f Audio support with Automatic Speech Recognition (ASR) models\n* \ud83d\udd0c Connect to any agent using the [MCP server](https://docling-project.github.io/docling/usage/mcp/)\n* \ud83d\udcbb Simple and convenient CLI\n\n### What's new\n* \ud83d\udce4 Structured [information extraction][extraction] \\[\ud83e\uddea beta\\]\n* \ud83d\udcd1 New layout model (**Heron**) by default, for faster PDF parsing\n* \ud83d\udd0c [MCP server](https://docling-project.github.io/docling/usage/mcp/) for agentic applications\n* \ud83d\udcac Parsing of Web Video Text Tracks (WebVTT) files\n\n### Coming soon\n\n* \ud83d\udcdd Metadata extraction, including title, authors, references & language\n* \ud83d\udcdd Chart understanding (Barchart, Piechart, LinePlot, etc)\n* \ud83d\udcdd Complex chemistry understanding (Molecular structures)\n\n## Installation\n\nTo use Docling, simply install `docling` from your package manager, e.g. pip:\n```bash\npip install docling\n```\n\nWorks on macOS, Linux and Windows environments. 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