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111 lines
3.9 KiB
Markdown
111 lines
3.9 KiB
Markdown
<p align="center">
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<a href="https://github.com/ds4sd/docling"> <img loading="lazy" alt="Docling" src="https://github.com/DS4SD/docling/raw/main/logo.png" width="150" />
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</p>
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# 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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Docling bundles PDF document conversion to JSON and Markdown in an easy, self-contained package.
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## Features
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* ⚡ Converts any PDF document to JSON or Markdown format, stable and lightning fast
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* 📑 Understands detailed page layout, reading order and recovers table structures
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* 📝 Extracts metadata from the document, such as title, authors, references and language
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* 🔍 Optionally applies OCR (use with scanned PDFs)
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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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> [!NOTE]
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> Works on macOS and Linux environments. Windows platforms are currently not tested.
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### Development setup
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To develop for Docling, you need Python 3.11 / 3.12 and Poetry. You can then install from your local clone's root dir:
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```bash
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poetry install
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```
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## Usage
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For basic usage, see the [convert.py](https://github.com/DS4SD/docling/blob/main/examples/convert.py) example module. Run with:
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```
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python examples/convert.py
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```
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The output of the above command will be written to `./scratch`.
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### Enable or disable pipeline features
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You can control if table structure recognition or OCR should be performed by arguments passed to `DocumentConverter`:
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```python
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doc_converter = DocumentConverter(
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artifacts_path=artifacts_path,
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pipeline_options=PipelineOptions(
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do_table_structure=False, # controls if table structure is recovered
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do_ocr=True, # controls if OCR is applied (ignores programmatic content)
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),
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)
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```
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### Impose limits on the document size
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You can limit the file size and number of pages which should be allowed to process per document:
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```python
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conv_input = DocumentConversionInput.from_paths(
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paths=[Path("./test/data/2206.01062.pdf")],
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limits=DocumentLimits(max_num_pages=100, max_file_size=20971520)
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)
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```
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### Convert from binary PDF streams
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You can convert PDFs from a binary stream instead of from the filesystem as follows:
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```python
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buf = BytesIO(your_binary_stream)
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docs = [DocumentStream(filename="my_doc.pdf", stream=buf)]
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conv_input = DocumentConversionInput.from_streams(docs)
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converted_docs = doc_converter.convert(conv_input)
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```
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### Limit resource usage
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You can limit the CPU threads used by Docling by setting the environment variable `OMP_NUM_THREADS` accordingly. The default setting is using 4 CPU threads.
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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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@software{Docling,
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author = {Deep Search Team},
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month = {7},
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title = {{Docling}},
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url = {https://github.com/DS4SD/docling},
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version = {main},
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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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