Docling Jobkit

Running a distributed job processing documents with Docling.

Note

This is an unstable draft implementation which will quickly evolve.

How to use it

Make sure your Ray cluster has docling-jobkit installed, then submit the job.

ray job submit --no-wait --working-dir . --runtime-env runtime_env.yml -- docling-ray-job

Ray runtime with Docling Jobkit

Custom runtime environment

1. Create a file runtime_env.yml:

   # Expected environment if clean ray image is used. Take into account that ray worker can timeout before it finishes installing modules.
   pip:
   - docling-jobkit

2. Submit the job using the custom runtime env:

   ray job submit --no-wait --runtime-env runtime_env.yml -- docling-ray-job

More examples and customization are provided in docs/ray-job/.

Custom image with all dependencies

Coming soon. Initial instruction from OpenShift AI docs.

Get help and support

Please feel free to connect with us using the discussion section of the main Docling repository.

Contributing

Please read Contributing to Docling Serve for details.

References

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

@techreport{Docling,
  author = {Deep Search Team},
  month = {1},
  title = {Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion},
  url = {https://arxiv.org/abs/2501.17887},
  eprint = {2501.17887},
  doi = {10.48550/arXiv.2501.17887},
  version = {2.0.0},
  year = {2025}
}

License

The Docling Serve codebase is under MIT license.

LF AI & Data

Docling is hosted as a project in the LF AI & Data Foundation.

IBM ❤️ Open Source AI

The project was started by the AI for Knowledge team at IBM Research Zurich.