Metadata-Version: 2.4
Name: docling
Version: 2.43.0
Summary: SDK and CLI for parsing PDF, DOCX, HTML, and more, to a unified document representation for powering downstream workflows such as gen AI applications.
Author-email: Christoph Auer <cau@zurich.ibm.com>, Michele Dolfi <dol@zurich.ibm.com>, Maxim Lysak <mly@zurich.ibm.com>, Nikos Livathinos <nli@zurich.ibm.com>, Ahmed Nassar <ahn@zurich.ibm.com>, Panos Vagenas <pva@zurich.ibm.com>, Peter Staar <taa@zurich.ibm.com>
License-Expression: MIT
Project-URL: homepage, https://github.com/docling-project/docling
Project-URL: repository, https://github.com/docling-project/docling
Project-URL: issues, https://github.com/docling-project/docling/issues
Project-URL: changelog, https://github.com/docling-project/docling/blob/main/CHANGELOG.md
Keywords: docling,convert,document,pdf,docx,html,markdown,layout model,segmentation,table structure,table former
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: <4.0,>=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pydantic<3.0.0,>=2.0.0
Requires-Dist: docling-core[chunking]<3.0.0,>=2.42.0
Requires-Dist: docling-parse<5.0.0,>=4.0.0
Requires-Dist: docling-ibm-models<4,>=3.9.0
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Provides-Extra: asr
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Dynamic: license-file

<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>

[![arXiv](https://img.shields.io/badge/arXiv-2408.09869-b31b1b.svg)](https://arxiv.org/abs/2408.09869)
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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, PPTX, XLSX, HTML, WAV, MP3, 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 ([SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview))
* 🎙️  Support for Audio with Automatic Speech Recognition (ASR) models
* 💻 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/
