docling/README.md

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

For general usage, you can simply install docling through pip from the pypi package index.

pip install docling

Notes:

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

Development setup

To develop for docling, you need Python 3.11 and poetry. Install poetry from here.

Once you have poetry installed and cloned this repo, create an environment and install docling from the repo root:

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

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.

Adjust pipeline features

Control pipeline options

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)
)

Control table extraction options

You can control if table structure recognition should map the recognized structure back to PDF cells (default) or use text cells from the structure prediction itself. This can improve output quality if you find that multiple columns in extracted tables are erroneously merged into one.

pipeline_options = PipelineOptions(do_table_structure=True)
pipeline_options.table_structure_options.do_cell_matching = True

doc_converter = DocumentConverter(
    artifacts_path=artifacts_path,
    pipeline_options=pipeline_options, # 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.