Docling Core

PyPI version Python uv Code style: black Imports: isort Checked with mypy Pydantic v2 pre-commit License MIT

Docling Core is a library that defines core data types and transformations in Docling.

Installation

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

pip install docling-core

Development setup

To develop for Docling Core, you need Python 3.10 through 3.14 and the uv package. You can then install it from your local clone's root directory:

uv sync --all-extras

To run the pytest suite, execute:

uv run pytest -s test

Main features

Docling Core provides the foundational DoclingDocument data model and API, as well as additional APIs for tasks like serialization and chunking, which are key to developing generative AI applications using Docling.

DoclingDocument

Docling Core defines the DoclingDocument as a Pydantic model, allowing for advanced data model control, customizability, and interoperability.

In addition to specifying the schema, it provides a handy API for building documents, as well as for basic operations, e.g. exporting to various formats, like Markdown, HTML, and others.

👉 More details:

Serialization

Different users can have varying requirements when it comes to serialization. To address this, the Serialization API introduces a design that allows easy extension, while providing feature-rich built-in implementations (on which the respective DoclingDocument helpers are actually based).

👉 More details:

Chunking

Similarly to above, the Chunking API provides built-in chunking capabilities as well as a design that enables easy extension, this way tackling customization requirements of different use cases.

👉 More details:

Profiling

The Profiling API enables extraction of comprehensive statistics from DoclingDocument objects, both for individual documents and collections. It provides metrics on document structure (pages, tables, pictures, text items) along with statistical distributions (deciles, histograms) and visualization capabilities for analyzing document collections at scale.

👉 More details:

Contributing

Please read Contributing to Docling Core for details.

References

If you use Docling Core 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 Core codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.

Project links

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Owner

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Credits

Author: Cesar Berrospi Ramis

Maintainer: Panos Vagenas

GitHub Statistics

Data verified by PyPI on Aug 6, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python <4.0, >=3.10

Provides Extra

dclq chunking chunking-openai examples

Tags

docling discovery etl information retrieval analytics database database schema schema JSON

Classifiers

Report project as malware

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