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feat: deploy the AI service with Docker and document the AI features - #8
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ShehanSuraweera
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Oct 2, 2026
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- Dockerfile: locked deps, embedding model baked in, non-root user
- deploy/compose.prod.yml: 127.0.0.1 only, 400 MiB limit (293 MiB peak measured), read-only filesystem, no capabilities, healthcheck
- deploy-ai-service.yml: tests, image built once in CI and pushed to GHCR by commit SHA, migrate, start and wait for health, automatic rollback to the previous image; no stored registry credentials
- CI: reusable AI service workflow, image build check on PRs; backend and AI deployments never run at the same time
- Fix ai-db bootstrap for non-superuser admins such as the RDS master user, with a regression test
- docs/ai.md: architecture, design decisions, safety, reliability, acceptance-rate queries and eval results
- README: architecture diagram, AI features and design decisions; deployment guide: one-time AI setup, memory budget, runbook
- Dockerfile: locked deps, embedding model baked in, non-root user - deploy/compose.prod.yml: 127.0.0.1 only, 400 MiB limit (293 MiB peak measured), read-only filesystem, no capabilities, healthcheck - deploy-ai-service.yml: tests, image built once in CI and pushed to GHCR by commit SHA, migrate, start and wait for health, automatic rollback to the previous image; no stored registry credentials - CI: reusable AI service workflow, image build check on PRs; backend and AI deployments never run at the same time - Fix ai-db bootstrap for non-superuser admins such as the RDS master user, with a regression test - docs/ai.md: architecture, design decisions, safety, reliability, acceptance-rate queries and eval results - README: architecture diagram, AI features and design decisions; deployment guide: one-time AI setup, memory budget, runbook
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