Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
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fix: handling of long sequence of unescaped underscore chars in markdown (#173)
* Fix for md hanging when encountering long sequence of unescaped underscore chars

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Added comment explaining reason for fix

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* Fixed trailing inline text handling (at the end of a file), and corrected underscore sequence shortening

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* making fix more rare

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

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Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>
Co-authored-by: Maksym Lysak <mly@zurich.ibm.com>
2024-10-28 16:34:48 +01:00
.github chore: make auto-release on request (#179) 2024-10-25 10:47:25 +02:00
docling fix: handling of long sequence of unescaped underscore chars in markdown (#173) 2024-10-28 16:34:48 +01:00
docs docs: update LlamaIndex docs for Docling v2 (#182) 2024-10-28 14:28:26 +01:00
tests fix: MD Backend, fixes to properly handle trailing inline text and emphasis in headers (#178) 2024-10-25 18:02:20 +02:00
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pyproject.toml fix: MD Backend, fixes to properly handle trailing inline text and emphasis in headers (#178) 2024-10-25 18:02:20 +02:00
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Docling

Docling

arXiv Docs PyPI version Python Poetry Code style: black Imports: isort Pydantic v2 pre-commit License MIT

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

Features

  • 🗂️ Multi-format support for input (PDF, DOCX etc.) & output (Markdown, JSON etc.)
  • 📑 Advanced PDF document understanding incl. page layout, reading order & table structures
  • 📝 Metadata extraction, including title, authors, references & language
  • 🤖 Seamless LlamaIndex 🦙 & LangChain 🦜🔗 integration for powerful RAG / QA applications
  • 🔍 OCR support for scanned PDFs
  • 💻 Simple and convenient CLI

Explore the documentation to discover plenty examples and unlock the full power of Docling!

Installation

To use Docling, simply install docling from your package manager, e.g. pip:

pip install docling

Works on macOS, Linux and Windows environments. Both x86_64 and arm64 architectures.

More detailed installation instructions are available in the docs.

Getting started

To convert individual documents, use convert(), for example:

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[...]"

Check out Getting started. You will find lots of tuning options to leverage all the advanced capabilities.

Get help and support

Please feel free to connect with us using the discussion section.

Technical report

For more details on Docling's inner workings, check out the Docling Technical Report.

Contributing

Please read Contributing to Docling for details.

References

If you use Docling in your projects, please consider citing the following:

@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.