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141 lines
5.5 KiB
Markdown
141 lines
5.5 KiB
Markdown
<p align="center">
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<a href="https://github.com/docling-project/docling-serve">
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<img loading="lazy" alt="Docling" src="https://github.com/docling-project/docling-serve/raw/main/docs/assets/docling-serve-pic.png" width="30%"/>
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</a>
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</p>
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# Docling Serve
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Running [Docling](https://github.com/docling-project/docling) as an API service.
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📚 [Docling Serve documentation](./docs/README.md)
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- Learning how to [configure the webserver](./docs/configuration.md)
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- Get to know all [runtime options](./docs/usage.md) of the API
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- Explore useful [deployment examples](./docs/deployment.md)
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- And more
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> [!NOTE]
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> **Migration to the `v1` API.** Docling Serve now has a stable v1 API. Read more on the [migration to v1](./docs/v1_migration.md).
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## Getting started
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Install the `docling-serve` package and run the server.
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```bash
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# Using the python package
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pip install "docling-serve[ui]"
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docling-serve run --enable-ui
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# Using container images, e.g. with Podman
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podman run -p 5001:5001 -e DOCLING_SERVE_ENABLE_UI=1 quay.io/docling-project/docling-serve
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```
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The server is available at
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- API <http://127.0.0.1:5001>
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- API documentation <http://127.0.0.1:5001/docs>
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- UI playground <http://127.0.0.1:5001/ui>
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Try it out with a simple conversion:
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```bash
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curl -X 'POST' \
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'http://localhost:5001/v1/convert/source' \
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-H 'accept: application/json' \
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-H 'Content-Type: application/json' \
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-d '{
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"sources": [{"kind": "http", "url": "https://arxiv.org/pdf/2501.17887"}]
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}'
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```
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### Container Images
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The following container images are available for running **Docling Serve** with different hardware and PyTorch configurations:
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#### 📦 Distributed Images
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| Image | Description | Architectures | Size |
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|-------|-------------|----------------|------|
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| [`ghcr.io/docling-project/docling-serve`](https://github.com/docling-project/docling-serve/pkgs/container/docling-serve) <br> [`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) <br> 8.7 GB (amd64) |
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| [`ghcr.io/docling-project/docling-serve-cpu`](https://github.com/docling-project/docling-serve/pkgs/container/docling-serve-cpu) <br> [`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 |
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| [`ghcr.io/docling-project/docling-serve-cu128`](https://github.com/docling-project/docling-serve/pkgs/container/docling-serve-cu128) <br> [`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 |
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| [`ghcr.io/docling-project/docling-serve-cu130`](https://github.com/docling-project/docling-serve/pkgs/container/docling-serve-cu130) <br> [`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 |
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> [!IMPORTANT]
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> **CUDA Image Tagging Policy**
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>
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> CUDA-specific images (`-cu128`, `-cu130`) follow PyTorch's CUDA version support lifecycle and are tagged differently from base images:
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>
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> - **Base images** (`docling-serve`, `docling-serve-cpu`): Tagged with `latest` and `main` for convenience
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> - **CUDA images** (`docling-serve-cu*`): **Only tagged with explicit versions** (e.g., `1.12.0`) and `main`
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>
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> **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:
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>
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> ```bash
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> # ✅ Recommended: Explicit version
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> docker pull quay.io/docling-project/docling-serve-cu130:1.12.0
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>
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> # ❌ Not available for CUDA images
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> docker pull quay.io/docling-project/docling-serve-cu130:latest
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> ```
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#### 🚫 Not Distributed
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An image for AMD ROCm 6.3 (`docling-serve-rocm`) is supported but **not published** due to its large size.
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To build it locally:
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```bash
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git clone --branch main git@github.com:docling-project/docling-serve.git
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cd docling-serve/
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make docling-serve-rocm-image
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```
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For deployment using Docker Compose, see [docs/deployment.md](docs/deployment.md).
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Coming soon: `docling-serve-slim` images will reduce the size by skipping the model weights download.
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### Demonstration UI
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An easy to use UI is available at the `/ui` endpoint.
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## Get help and support
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Please feel free to connect with us using the [discussion section](https://github.com/docling-project/docling/discussions).
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## Contributing
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Please read [Contributing to Docling Serve](https://github.com/docling-project/docling-serve/blob/main/CONTRIBUTING.md) for details.
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## References
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If you use Docling in your projects, please consider citing the following:
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```bib
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@techreport{Docling,
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author = {Docling Contributors},
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month = {1},
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title = {Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion},
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url = {https://arxiv.org/abs/2501.17887},
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eprint = {2501.17887},
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doi = {10.48550/arXiv.2501.17887},
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version = {2.0.0},
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year = {2025}
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}
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```
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## License
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The Docling Serve codebase is under MIT license.
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## IBM ❤️ Open Source AI
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Docling has been brought to you by IBM.
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