# 🦆 Docling

[](https://arxiv.org/abs/2408.09869) [](https://ds4sd.github.io/docling/) [](https://pypi.org/project/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) [](https://pepy.tech/projects/docling) 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 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[...]" ``` 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.