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<p align="center">
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# Docowling
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<a href="https://github.com/ds4sd/docling">
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<img loading="lazy" alt="Docling" src="https://github.com/DS4SD/docling/raw/main/docs/assets/docling_processing.png" width="100%"/>
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</a>
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</p>
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# Docling
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**Docowling** é um fork do projeto [Docling](https://github.com/DS4SD/docling), desenvolvido para aprimorar funcionalidades e adicionar novas capacidades de processamento de documentos.
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<p align="center">
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<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>
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</p>
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[](https://arxiv.org/abs/2408.09869)
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[](https://ds4sd.github.io/docling/)
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[](https://pypi.org/project/docling/)
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[](https://pypi.org/project/docling/)
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[](https://python-poetry.org/)
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[](https://github.com/psf/black)
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[](https://pycqa.github.io/isort/)
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[](https://pydantic.dev)
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[](https://github.com/pre-commit/pre-commit)
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[](https://opensource.org/licenses/MIT)
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[](https://pepy.tech/projects/docling)
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Docling parses documents and exports them to the desired format with ease and speed.
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## Features
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* 🗂️ Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to HTML, Markdown and JSON (with embedded and referenced images)
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* 📑 Advanced PDF document understanding including page layout, reading order & table structures
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* 🧩 Unified, expressive [DoclingDocument](https://ds4sd.github.io/docling/concepts/docling_document/) representation format
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* 🤖 Easy integration with 🦙 LlamaIndex & 🦜🔗 LangChain for powerful RAG / QA applications
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* 🔍 OCR support for scanned PDFs
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* 💻 Simple and convenient CLI
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Explore the [documentation](https://ds4sd.github.io/docling/) to discover plenty examples and unlock the full power of Docling!
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### Coming soon
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* ♾️ Equation & code extraction
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* 📝 Metadata extraction, including title, authors, references & language
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* 🦜🔗 Native LangChain extension
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## Installation
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To use Docling, simply install `docling` from your package manager, e.g. pip:
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```bash
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pip install docling
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```
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Works on macOS, Linux and Windows environments. Both x86_64 and arm64 architectures.
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More [detailed installation instructions](https://ds4sd.github.io/docling/installation/) are available in the docs.
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## Getting started
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To convert individual documents, use `convert()`, for example:
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```python
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from docling.document_converter import DocumentConverter
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source = "https://arxiv.org/pdf/2408.09869" # document per local path or URL
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converter = DocumentConverter()
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result = converter.convert(source)
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print(result.document.export_to_markdown()) # output: "## Docling Technical Report[...]"
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```
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More [advanced usage options](https://ds4sd.github.io/docling/usage/) are available in
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the docs.
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## Documentation
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Check out Docling's [documentation](https://ds4sd.github.io/docling/), for details on
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installation, usage, concepts, recipes, extensions, and more.
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## Examples
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Go hands-on with our [examples](https://ds4sd.github.io/docling/examples/),
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demonstrating how to address different application use cases with Docling.
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## Integrations
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To further accelerate your AI application development, check out Docling's native
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[integrations](https://ds4sd.github.io/docling/integrations/) with popular frameworks
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and tools.
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## Get help and support
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Please feel free to connect with us using the [discussion section](https://github.com/DS4SD/docling/discussions).
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## Technical report
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For more details on Docling's inner workings, check out the [Docling Technical Report](https://arxiv.org/abs/2408.09869).
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## Contributing
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Please read [Contributing to Docling](https://github.com/DS4SD/docling/blob/main/CONTRIBUTING.md) for details.
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## References
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If you use Docling in your projects, please consider citing the following:
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```bib
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@techreport{Docling,
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author = {Deep Search Team},
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month = {8},
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title = {Docling Technical Report},
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url = {https://arxiv.org/abs/2408.09869},
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eprint = {2408.09869},
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doi = {10.48550/arXiv.2408.09869},
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version = {1.0.0},
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year = {2024}
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}
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```
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## License
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The Docling codebase is under MIT license.
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For individual model usage, please refer to the model licenses found in the original packages.
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## IBM ❤️ Open Source AI
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Docling has been brought to you by IBM.
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