<p align="center">
<a href="https://github.com/ds4sd/docling">
<img loading="lazy" alt="Docling" src="https://github.com/DS4SD/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://ds4sd.github.io/docling/)
[](https://pypi.org/project/docling/)
[](https://python-poetry.org/)
[](https://github.com/psf/black)
[](https://pycqa.github.io/isort/)
[](https://pydantic.dev)
[](https://github.com/pre-commit/pre-commit)
[](https://opensource.org/licenses/MIT)
Docling parses documents and exports them to the desired format with ease and speed.
## Features
* 🗂️ Reads popular document formats (PDF, DOCX, PPTX, Images, HTML, AsciiDoc, Markdown) and exports to Markdown and JSON
* 📑 Advanced PDF document understanding including page layout, reading order & table structures
* 🧩 Unified, expressive [DoclingDocument](https://ds4sd.github.io/docling/concepts/docling_document/) representation format
* 🤖 Easy integration with LlamaIndex 🦙 & LangChain 🦜🔗 for powerful RAG / QA applications
* 🔍 OCR support for scanned PDFs
* 💻 Simple and convenient CLI
Explore the [documentation](https://ds4sd.github.io/docling/) to discover plenty examples and unlock the full power of Docling!
### Coming soon
* ♾️ Equation & code extraction
* 📝 Metadata extraction, including title, authors, references & language
* 🦜🔗 Native LangChain extension
## 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://ds4sd.github.io/docling/installation/) are available in the docs.
## Getting started
To convert individual documents, 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[...]"
```
Check out [Getting started](https://ds4sd.github.io/docling/).
You will find lots of tuning options to leverage all the advanced capabilities.
## Get help and support
Please feel free to connect with us using the [discussion section](https://github.com/DS4SD/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/DS4SD/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.
## IBM ❤️ Open Source AI
Docling has been brought to you by IBM.
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