https://github.com/docling-project/docling-serve
Running Docling as an API service
https://github.com/docling-project/docling-serve
Last synced: 4 months ago
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Running Docling as an API service
- Host: GitHub
- URL: https://github.com/docling-project/docling-serve
- Owner: docling-project
- License: mit
- Created: 2024-09-06T07:43:00.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2026-02-16T16:26:31.000Z (4 months ago)
- Last Synced: 2026-02-16T20:24:07.936Z (4 months ago)
- Language: Python
- Homepage:
- Size: 18.1 MB
- Stars: 1,244
- Watchers: 12
- Forks: 256
- Open Issues: 100
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: .github/SECURITY.md
- Maintainers: MAINTAINERS.md
Awesome Lists containing this project
README
# Docling Serve
Running [Docling](https://github.com/docling-project/docling) as an API service.
📚 [Docling Serve documentation](./docs/README.md)
- Learning how to [configure the webserver](./docs/configuration.md)
- Get to know all [runtime options](./docs/usage.md) of the API
- Explore useful [deployment examples](./docs/deployment.md)
- And more
> [!NOTE]
> **Migration to the `v1` API.** Docling Serve now has a stable v1 API. Read more on the [migration to v1](./docs/v1_migration.md).
## Getting started
Install the `docling-serve` package and run the server.
```bash
# 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
- API documentation
- UI playground
Try it out with a simple conversion:
```bash
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`](https://github.com/docling-project/docling-serve/pkgs/container/docling-serve)
[`quay.io/docling-project/docling-serve`](https://quay.io/repository/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`](https://github.com/docling-project/docling-serve/pkgs/container/docling-serve-cpu)
[`quay.io/docling-project/docling-serve-cpu`](https://quay.io/repository/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`](https://github.com/docling-project/docling-serve/pkgs/container/docling-serve-cu128)
[`quay.io/docling-project/docling-serve-cu128`](https://quay.io/repository/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`](https://github.com/docling-project/docling-serve/pkgs/container/docling-serve-cu130)
[`quay.io/docling-project/docling-serve-cu130`](https://quay.io/repository/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:
>
> ```bash
> # ✅ Recommended: Explicit version
> docker pull quay.io/docling-project/docling-serve-cu130:1.12.0
>
> # ❌ 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:
```bash
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](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.
## Get help and support
Please feel free to connect with us using the [discussion section](https://github.com/docling-project/docling/discussions).
## Contributing
Please read [Contributing to Docling Serve](https://github.com/docling-project/docling-serve/blob/main/CONTRIBUTING.md) for details.
## References
If you use Docling in your projects, please consider citing the following:
```bib
@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.