Docling PDF conversion package
Project description
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
.
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.
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