docling/README.md

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# Docling

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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, XLSX, HTML, images, 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, 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 Visual Language Models ([SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview)) 🆕
* 💻 Simple and convenient CLI

### 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/) 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 🥚[SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview) and other VLMs via Docling CLI:
```bash
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](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/