Docling

Docling bundles PDF document conversion to JSON and Markdown in an easy, self-contained package.

Features

• ⚡ Converts any PDF document to JSON or Markdown format, stable and lightning fast
• 📑 Understands detailed page layout, reading order and recovers table structures
• 📝 Extracts metadata from the document, such as title, authors, references and language
• 🔍 Optionally applies OCR (use with scanned PDFs)

Setup

You need Python 3.11 and poetry. Install poetry from here.

Once you have poetry installed, create an environment and install the package:

poetry env use $(which python3.11)
poetry shell
poetry install

Notes:

• Works on macOS and Linux environments. Windows platforms are currently not tested.

Usage

For basic usage, see the convert.py example module. Run with:

python examples/convert.py

The output of the above command will be written to ./scratch.

Enable or disable pipeline features

You can control if table structure recognition or OCR should be performed by arguments passed to DocumentConverter

doc_converter = DocumentConverter(
    artifacts_path=artifacts_path,
    pipeline_options=PipelineOptions(do_table_structure=False, # Controls if table structure is recovered. 
                                     do_ocr=True), # Controls if OCR is applied (ignores programmatic content)
)

Impose limits on the document size

You can limit the file size and number of pages which should be allowed to process per document.

paths = [Path("./test/data/2206.01062.pdf")]

input = DocumentConversionInput.from_paths(
    paths, limits=DocumentLimits(max_num_pages=100, max_file_size=20971520)
)

Convert from binary PDF streams

You can convert PDFs from a binary stream instead of from the filesystem as follows:

buf = BytesIO(your_binary_stream)
docs = [DocumentStream(filename="my_doc.pdf", stream=buf)]
input = DocumentConversionInput.from_streams(docs)
converted_docs = doc_converter.convert(input)

Limit resource usage

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.

Contributing

Please read Contributing to Docling for details.

References

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

@software{Docling,
author = {Deep Search Team},
month = {7},
title = {{Docling}},
url = {https://github.com/DS4SD/docling},
version = {main},
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.