# Docling
[](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://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, 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 several 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/