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git.stella-ops.org/ops/deployment/advisory-ai/README.md
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# Advisory AI Deployment Runbook
## Scope
- Helm and Compose packaging for `advisory-ai-web` (API/plan cache) and `advisory-ai-worker` (inference/queue).
- GPU toggle (NVIDIA) for on-prem inference; defaults remain CPU-safe.
- Offline kit pickup instructions for including advisory AI artefacts.
## Helm
Values already ship in `deploy/helm/stellaops/values-*.yaml` under `services.advisory-ai-web` and `advisory-ai-worker`.
GPU enablement (example):
```yaml
services:
advisory-ai-worker:
runtimeClassName: nvidia
nodeSelector:
nvidia.com/gpu.present: "true"
tolerations:
- key: nvidia.com/gpu
operator: Exists
effect: NoSchedule
resources:
limits:
nvidia.com/gpu: 1
advisory-ai-web:
runtimeClassName: nvidia
resources:
limits:
nvidia.com/gpu: 1
```
Apply:
```bash
helm upgrade --install stellaops ./deploy/helm/stellaops \
-f deploy/helm/stellaops/values-prod.yaml \
-f deploy/helm/stellaops/values-mirror.yaml \
--set services.advisory-ai-worker.resources.limits.nvidia\.com/gpu=1 \
--set services.advisory-ai-worker.runtimeClassName=nvidia
```
## Compose
- Base profiles: `docker-compose.dev.yaml`, `stage`, `prod`, `airgap` already include advisory AI services and shared volumes.
- GPU overlay: `docker-compose.gpu.yaml` (adds NVIDIA device reservations and `ADVISORY_AI_INFERENCE_GPU=true`). Use:
```bash
docker compose --env-file prod.env \
-f docker-compose.prod.yaml \
-f docker-compose.gpu.yaml up -d
```
## Offline kit pickup
- Ensure advisory AI images are mirrored to your registry (or baked into airgap tar) before running the offline kit build.
- Copy the following into `out/offline-kit/metadata/` before invoking the offline kit script:
- `advisory-ai-web` image tar
- `advisory-ai-worker` image tar
- SBOM/provenance generated by the release pipeline
- Verify `docs/24_OFFLINE_KIT.md` includes the advisory AI entries and rerun `tests/offline/test_build_offline_kit.py` if it changes.
## Runbook (prod quickstart)
1) Prepare secrets in ExternalSecret or Kubernetes secret named `stellaops-prod-core` (see helm values).
2) Run Helm install with prod values and GPU overrides as needed.
3) For Compose, use `prod.env` and optionally `docker-compose.gpu.yaml` overlay.
4) Validate health:
- `GET /healthz` on `advisory-ai-web`
- Check queue directories under `advisory-ai-*` volumes remain writable
- Confirm inference path logs when GPU is detected (log key `advisory.ai.inference.gpu=true`).
## Advisory Feed Packaging (DEVOPS-AIAI-31-002)
Package advisory feeds (SBOM pointers + provenance) for release/offline kit:
```bash
# Production (CI with COSIGN_PRIVATE_KEY_B64 secret)
./ops/deployment/advisory-ai/package-advisory-feeds.sh
# Development (uses tools/cosign/cosign.dev.key)
COSIGN_ALLOW_DEV_KEY=1 COSIGN_PASSWORD=stellaops-dev \
./ops/deployment/advisory-ai/package-advisory-feeds.sh
```
Outputs:
- `out/advisory-ai/feeds/advisory-feeds.tar.gz` - Feed bundle
- `out/advisory-ai/feeds/advisory-feeds.manifest.json` - Manifest with SBOM pointers
- `out/advisory-ai/feeds/advisory-feeds.manifest.dsse.json` - DSSE signed manifest
- `out/advisory-ai/feeds/provenance.json` - Build provenance
CI workflow: `.gitea/workflows/advisory-ai-release.yml`
## Evidence to attach (sprint)
- Helm release output (rendered templates for advisory AI)
- `docker-compose config` with/without GPU overlay
- Offline kit metadata listing advisory AI images + SBOMs
- Advisory feed package manifest with SBOM pointers