Docling Serve

Running Docling as an API service.

Learning how to configure the webserver
Get to know all runtime options of the API
Explore useful deployment examples
And more

[!NOTE] Migration to the v1 API. Docling Serve now has a stable v1 API. Read more on the migration to v1.

Getting started

Install the docling-serve package and run the server.

# Using the python package
pip install "docling-serve[ui]"
docling-serve run --enable-ui

# Using container images, e.g. with Podman
podman run -p 5001:5001 -e DOCLING_SERVE_ENABLE_UI=1 quay.io/docling-project/docling-serve

The server is available at

API http://127.0.0.1:5001
API documentation http://127.0.0.1:5001/docs
UI playground http://127.0.0.1:5001/ui

API documentation

Try it out with a simple conversion:

curl -X 'POST' \
  'http://localhost:5001/v1/convert/source' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "sources": [{"kind": "http", "url": "https://arxiv.org/pdf/2501.17887"}]
  }'

Container Images

The following container images are available for running Docling Serve with different hardware and PyTorch configurations:

📦 Distributed Images

Image	Description	Architectures	Size
`ghcr.io/docling-project/docling-serve` `quay.io/docling-project/docling-serve`	Base image with all packages installed from the official PyPI index.	`linux/amd64`, `linux/arm64`	4.4 GB (arm64) 8.7 GB (amd64)
`ghcr.io/docling-project/docling-serve-cpu` `quay.io/docling-project/docling-serve-cpu`	CPU-only variant, using `torch` from the PyTorch CPU index.	`linux/amd64`, `linux/arm64`	4.4 GB
`ghcr.io/docling-project/docling-serve-cu128` `quay.io/docling-project/docling-serve-cu128`	CUDA 12.8 build with `torch` from the cu128 index.	`linux/amd64`	11.4 GB
`ghcr.io/docling-project/docling-serve-cu130` `quay.io/docling-project/docling-serve-cu130`	CUDA 13.0 build with `torch` from the cu130 index.	`linux/amd64`, `linux/arm64`	TBD

[!IMPORTANT] CUDA Image Tagging Policy

CUDA-specific images (-cu128, -cu130) follow PyTorch's CUDA version support lifecycle and are tagged differently from base images:

Base images (docling-serve, docling-serve-cpu): Tagged with latest and main for convenience

CUDA images (docling-serve-cu*): Only tagged with explicit versions (e.g., 1.12.0) and main

Why? CUDA versions are deprecated over time as PyTorch adds support for newer CUDA releases. To avoid accidentally pulling deprecated CUDA versions, CUDA images intentionally exclude the latest tag. Always use explicit version tags like:
# ✅ Recommended: Explicit version
docker pull quay.io/docling-project/docling-serve-cu130:v1.18.0

# ❌ Not available for CUDA images
docker pull quay.io/docling-project/docling-serve-cu130:latest

🚫 Not Distributed

An image for AMD ROCm 6.3 (docling-serve-rocm) is supported but not published due to its large size.

To build it locally:

git clone --branch main git@github.com:docling-project/docling-serve.git
cd docling-serve/
make docling-serve-rocm-image

For deployment using Docker Compose, see docs/deployment.md.

Coming soon: docling-serve-slim images will reduce the size by skipping the model weights download.

Demonstration UI

An easy to use UI is available at the /ui endpoint.

Input controllers in the UI

Output visualization in the UI

Get help and support

Please feel free to connect with us using the discussion section.

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 = {Docling Contributors},
  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.

IBM ❤️ Open Source AI

Docling has been brought to you by IBM.

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