760b56bfd6
Covers: overview, setup, API reference, configuration, testing, deployment, and known quirks.
73 lines
2.5 KiB
Markdown
73 lines
2.5 KiB
Markdown
# Deployment
|
|
|
|
## Docker
|
|
|
|
The service ships with a `Dockerfile` based on `python:3.12-slim-bookworm` using [uv](https://astral.sh/uv/) for fast dependency installation.
|
|
|
|
### Building
|
|
|
|
```bash
|
|
docker build -t email-classifier .
|
|
```
|
|
|
|
### Running
|
|
|
|
```bash
|
|
docker run -d --name email-classifier \
|
|
-p 7999:7999 \
|
|
-e LLM_PROVIDER=openai \
|
|
-e LLM_BASE_URL=http://your-ollama:11434/v1 \
|
|
-e LLM_API_KEY=none \
|
|
-e LLM_MODEL=qwen2.5-7b-instruct.q4_k_m \
|
|
-e LLM_TEMPERATURE=0.1 \
|
|
-e EMAIL_CLASSIFIER_DB_PATH=/data/email_classifier.db \
|
|
-v /path/to/local/data:/data \
|
|
email-classifier
|
|
```
|
|
|
|
Mount a persistent volume for `/data` (or wherever `EMAIL_CLASSIFIER_DB_PATH` points) to preserve the dedupe database across container restarts.
|
|
|
|
### Building for a Remote Registry
|
|
|
|
```bash
|
|
docker build -t \
|
|
your-registry.example.com/your-org/email-classifier:latest \
|
|
.
|
|
|
|
docker push your-registry.example.com/your-org/email-classifier:latest
|
|
```
|
|
|
|
## GitHub Actions CI/CD
|
|
|
|
The repository includes a workflow at `.github/workflows/build-publish.yaml` that builds and pushes a Docker image on every push to `main`.
|
|
|
|
### Required Secrets
|
|
|
|
Configure these in your GitHub/Gitea Actions secrets:
|
|
|
|
| Secret | Description |
|
|
|---|---|
|
|
| `DOCKER_REGISTRY` | Registry hostname (e.g., `ghcr.io` or your custom registry) |
|
|
| `DOCKER_USERNAME` | Registry username |
|
|
| `DOCKER_PASSWORD` | Registry password or access token |
|
|
|
|
The workflow tags the image as:
|
|
- `:latest` — always points to the latest commit on `main`
|
|
- `:<sha>` — the full git SHA of the triggering commit (useful for rollbacks)
|
|
|
|
### Deployment Considerations
|
|
|
|
- **Network access** — The container needs to reach your LLM backend. If using Ollama on the host, use `host.docker.internal` (Linux) or `docker.for.mac.localhost` (macOS) as the base URL.
|
|
- **Dedupe persistence** — Mount a volume for the SQLite database to persist dedupe state across deploys.
|
|
- **Port** — The container exposes port `7999`. Map it to any host port you prefer.
|
|
- **Health check** — The service does not currently expose a dedicated `/health` endpoint. Use `GET /docs` as a liveness probe.
|
|
|
|
## Production Checklist
|
|
|
|
- [ ] Set `LLM_API_KEY` to a real key (not `none`) in production
|
|
- [ ] Use HTTPS for `LLM_BASE_URL` in production
|
|
- [ ] Mount a persistent volume for `EMAIL_CLASSIFIER_DB_PATH`
|
|
- [ ] Set appropriate resource limits (CPU/memory) on the container
|
|
- [ ] Configure `LLM_MAX_RETRIES` and `LLM_TIMEOUT_SECONDS` to suit your LLM backend's reliability
|
|
- [ ] Set `LLM_TEMPERATURE=0.1` (or similar low value) for consistent classification results
|