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build(docker): slim backend image, static frontend image, -docling variant
Backend (arc53/docsgpt): 4.5 GB compressed -> 0.9 GB with both embedding models and tiktoken baked in. - torch/transformers gone from the default install (docling extra only). - Ubuntu 24.04 ships python3.12: no deadsnakes PPA, no software-properties- common; every pin is a wheel, so no gcc/g++/rust in the builder. - COPY --chown and a prefetch that runs as the process user replace the trailing chown -R, which duplicated the 600 MB model layer. - .dockerignore keeps __pycache__, .coverage, local indexes and .env out. - EXTRAS build arg (INSTALL_DOCLING kept as an alias); the docling variant also bakes docling's layout/table/RapidOCR models (DOCLING_ARTIFACTS_PATH) and tesseract, and drops only the discovery documents of Google APIs the app never builds. - FLASK_DEBUG env removed (unused); OCI labels added. Frontend (arc53/docsgpt-fe): 302 MB Vite dev server -> 25 MB static build behind nginx. VITE_* variables are injected at container start into /config.js and read through src/env.ts, so the image no longer needs a rebuild per deployment; docker-compose.yaml keeps hot reload via the dev target. Publishing: every release and develop build now pushes a slim tag and a -docling tag (docling engine + models + tesseract). docker-compose-hub.yaml takes DOCSGPT_IMAGE_TAG / DOCSGPT_IMAGE_VARIANT; docker-compose-standalone.yaml runs the stack from pre-built images without a checkout and is attached to each release. setup.sh selects the -docling variant for OCR instead of requiring a local build. A new workflow builds the image on PRs that touch it and runs verify_offline under --network none; lint checks the exported requirements match uv.lock.
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@@ -17,9 +17,57 @@ Docker is the recommended method for deploying DocsGPT, providing a consistent a
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**Important Note for Windows Users:** Docker Desktop on Windows generally requires the WSL 2 backend to function correctly, especially when using features like host networking which are utilized in DocsGPT's Docker Compose setup. Ensure WSL 2 is enabled and configured in Docker Desktop settings.
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## Quickest Setup: Using DocsGPT Public API
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## Quickest Setup: Pre-built Images, No Checkout
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The fastest way to try out DocsGPT is by using the public API endpoint. This requires minimal configuration and no local LLM setup.
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Every release publishes ready-to-run images to Docker Hub (`arc53/docsgpt`,
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`arc53/docsgpt-fe`) and GitHub Container Registry (`ghcr.io/arc53/docsgpt`,
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`ghcr.io/arc53/docsgpt-fe`) for `linux/amd64` and `linux/arm64`. The images
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contain everything the default configuration needs (embedding models,
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tokenizers, tiktoken's encoding), so a fresh container makes no downloads on
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first use. You do not need the source tree to run them:
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1. **Download the standalone Compose file** (also attached to every
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[release](https://github.com/arc53/DocsGPT/releases)):
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```bash
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mkdir docsgpt && cd docsgpt
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curl -fsSLO https://raw.githubusercontent.com/arc53/DocsGPT/main/deployment/docker-compose-standalone.yaml
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```
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2. **Create a `.env` next to it** with your settings, for example the public API:
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```
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LLM_PROVIDER=docsgpt
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VITE_API_STREAMING=true
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```
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3. **Start it:**
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```bash
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docker compose -f docker-compose-standalone.yaml up -d
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```
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Then open [http://localhost:5173/](http://localhost:5173/). Data lives in
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named Docker volumes; `docker compose -f docker-compose-standalone.yaml down`
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keeps it and `down -v` removes it.
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**Tags and variants.** `DOCSGPT_IMAGE_TAG` picks the version: a release such
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as `0.20.0`, `latest` (the newest release, the default) or `develop` (follows
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the `main` branch). `DOCSGPT_IMAGE_VARIANT` picks the flavour: empty for the
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slim default image, or `-docling` for the image with the docling parser
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engine, its models and tesseract baked in (needed for OCR of scanned
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documents, see the [OCR guide](/Guides/ocr)). Both are read from `.env` or
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the shell, e.g. `DOCSGPT_IMAGE_TAG=0.20.0 DOCSGPT_IMAGE_VARIANT=-docling`.
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The same two variables drive `deployment/docker-compose-hub.yaml` in a
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checkout.
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## Using the Source Checkout
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With a clone of the repository, `deployment/docker-compose-hub.yaml` runs the
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same pre-built images while keeping your data in `application/indexes`,
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`application/inputs` and `application/vectors`, and `deployment/docker-compose.yaml`
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builds the images from your working tree (for local changes, or a build with
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extra packages: `EXTRAS=docling` in `.env`).
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1. **Clone the DocsGPT Repository (if you haven't already):**
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@@ -48,10 +96,12 @@ The fastest way to try out DocsGPT is by using the public API endpoint. This req
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Navigate to the root directory of the DocsGPT repository in your terminal and run:
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```bash
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docker compose --env-file .env -f deployment/docker-compose.yaml up -d
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docker compose --env-file .env -f deployment/docker-compose-hub.yaml up -d
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```
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The `-d` flag runs Docker Compose in detached mode (in the background).
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To build the images from your working tree instead of pulling them, use
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`deployment/docker-compose.yaml` with `up --build -d`.
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5. **Access DocsGPT in your browser:**
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@@ -62,7 +112,7 @@ The fastest way to try out DocsGPT is by using the public API endpoint. This req
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To stop the application, navigate to the same directory in your terminal and run:
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```bash
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docker compose -f deployment/docker-compose.yaml down
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docker compose -f deployment/docker-compose-hub.yaml down
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```
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## Optional Ollama Setup (Local Models)
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@@ -84,11 +134,11 @@ There are two Ollama optional files:
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**CPU:**
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```bash
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docker compose --env-file .env -f deployment/docker-compose.yaml -f deployment/optional/docker-compose.optional.ollama-cpu.yaml up -d
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docker compose --env-file .env -f deployment/docker-compose-hub.yaml -f deployment/optional/docker-compose.optional.ollama-cpu.yaml up -d
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```
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**GPU:**
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```bash
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docker compose --env-file .env -f deployment/docker-compose.yaml -f deployment/optional/docker-compose.optional.ollama-gpu.yaml up -d
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docker compose --env-file .env -f deployment/docker-compose-hub.yaml -f deployment/optional/docker-compose.optional.ollama-gpu.yaml up -d
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```
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3. **Pull the Ollama Model:**
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@@ -96,11 +146,11 @@ There are two Ollama optional files:
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**Crucially, after launching with Ollama, you need to pull the desired model into the Ollama container.** Find the `LLM_NAME` you configured in your `.env` file (e.g., `llama3.2:1b`). Then execute the following command to pull the model *inside* the running Ollama container:
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```bash
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docker compose -f deployment/docker-compose.yaml -f deployment/optional/docker-compose.optional.ollama-cpu.yaml exec -it ollama ollama pull <LLM_NAME>
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docker compose -f deployment/docker-compose-hub.yaml -f deployment/optional/docker-compose.optional.ollama-cpu.yaml exec -it ollama ollama pull <LLM_NAME>
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```
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or (for GPU):
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```bash
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docker compose -f deployment/docker-compose.yaml -f deployment/optional/docker-compose.optional.ollama-gpu.yaml exec -it ollama ollama pull <LLM_NAME>
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docker compose -f deployment/docker-compose-hub.yaml -f deployment/optional/docker-compose.optional.ollama-gpu.yaml exec -it ollama ollama pull <LLM_NAME>
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```
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Replace `<LLM_NAME>` with the actual model name from your `.env` file.
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@@ -113,12 +163,12 @@ There are two Ollama optional files:
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To stop a DocsGPT setup launched with Ollama optional files, use `docker compose down` and include all the compose files used during the `up` command:
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```bash
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docker compose -f deployment/docker-compose.yaml -f deployment/optional/docker-compose.optional.ollama-cpu.yaml down
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docker compose -f deployment/docker-compose-hub.yaml -f deployment/optional/docker-compose.optional.ollama-cpu.yaml down
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```
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or
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```bash
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docker compose -f deployment/docker-compose.yaml -f deployment/optional/docker-compose.optional.ollama-gpu.yaml down
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docker compose -f deployment/docker-compose-hub.yaml -f deployment/optional/docker-compose.optional.ollama-gpu.yaml down
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```
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**Important for GPU Usage:**
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