https://github.com/ighina/docling

Get your documents ready for gen AI

https://github.com/ighina/docling

Repository

Get your documents ready for gen AI

https://github.com/Ighina/docling/blob/main/

  
    
  


# Docling


  


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Docling parses documents and exports them to the desired format with ease and speed.

## Features

*  Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to HTML, Markdown and JSON (with embedded and referenced images)
*  Advanced PDF document understanding including page layout, reading order & table structures
*  Unified, expressive [DoclingDocument](https://ds4sd.github.io/docling/concepts/docling_document/) representation format
*  Plug-and-play [integrations](https://ds4sd.github.io/docling/integrations/) incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
*  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

## 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[...]"
```

More [advanced usage options](https://ds4sd.github.io/docling/usage/) are available in
the docs.

## Documentation

Check out Docling's [documentation](https://ds4sd.github.io/docling/), for details on
installation, usage, concepts, recipes, extensions, and more.

## Examples

Go hands-on with our [examples](https://ds4sd.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://ds4sd.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/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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