Add architecture documentation and CI/CD best practices#7
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Comprehensive guide covering what AI Quickstarts are, the OpenShift AI platform features they leverage, the shared AI Architecture Helm Charts library, how to create a new quickstart, and a catalog of existing quickstarts across industries. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Incorporate patterns from the it-self-service-agent repository: - Testing strategy: unit tests, integration tests, LLM evaluation framework - Code quality: flake8, black, isort, mypy strict mode, custom logging rules - Git workflow: dev/main branch promotion, version management, dependency management - GitHub Actions: PR checks, e2e tests, nightly evaluation, build/push, promotion - Container build patterns: multi-stage UBI9 builds, dual uv/pip support - Restore README.md to original template (used for publication) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace placeholder values.yaml and helm install examples with the actual patterns used by the RAG quickstart: global.models config, Chart.yaml with conditional dependencies, Makefile-driven installation with command-line overrides, and the url-based local vs remote model distinction. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Starter CI/CD pipeline that ships with the template so new quickstarts get working workflows out of the box: Workflows: - pr-checks: linting, formatting, type checks, helm validation, unit tests - pr-e2e-tests: integration + short LLM evaluation on Kind cluster - pr-evaluation-check: known-bad conversation regression check - pr-build-test: container build + Helm deploy on Kind - pr-branch-check: enforce dev-to-main promotion path - build-and-push: image build/push to Quay on merge - nightly-e2e: full evaluation with 20+ conversations - create-dev-to-main-pr: automated promotion with version validation - version-bump: auto-increment version across Makefile + Chart.yaml Custom Actions: - kind: create Kind cluster, build/load images, deploy with Helm - prepare-runner: free disk space, report system resources Also makes evaluation framework docs generic (not specific to any existing quickstart). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Explain the GitHub "Use this template" flow for creating a new repository with the full scaffold already in place. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Show React/TypeScript project structure (src/, package.json, tsconfig.json) instead of Python for the frontend directory. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Update architecture doc to use Tailwind CSS instead of PatternFly for frontend styling, and explicitly call out FastAPI as the backend framework with uvicorn. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add the AI QuickStart CLI as step 2 in the Getting Started guide and to the Key Technology table. The CLI generates a production-ready monorepo with React + Tailwind CSS, FastAPI, and PostgreSQL. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Following information is duplicate:
My thoughts:
Questions from me:
My recommendation is to align this PR with the existing contribution guide and user guide, removing duplicated requirements and updating the source documentation where additional guidance is needed. 🙂 For example, having a template architecture MD that they can start using is awesome. I will leave comments about the automated checks since that has been what others have been working on, as well as Adam and Yoni using agentic AI to run validation without requiring users or contributors to necessarily redo their existing work. |
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- So far first look my suggestion would be to break this ARCHITECTURE.md file into a few slices. Maybe split based on the
docs/index.mdfile.
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There is a lot of good content in here, main comment is that there might be a bit too much specific detail to start with. Maybe sections with less content, but then references to examples of how things were implemented. The maybe more specific requirements/details once we prove things out on 2 or more quickstarts ?
johnson2500
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I left a few comments on the PR, over all it looks good. I assume this is going to be out starting point and hopefully this will be cloned as a part of the quickstart factory?
I just want to ensure that we do not have a bunch of sprawl. |
| ├──────────────┬──────────────┬──────────────┬────────────────────────┤ | ||
| │ AI/Agent │ Integration │ Safety & │ Observability │ | ||
| │ Framework │ Layer │ Guardrails │ │ | ||
| │ (LlamaStack, │ (MCP Servers,│ (Llama Guard,│ (OpenTelemetry, │ |
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We probably want to pull out Llama Guard/Prompt Guard since those are no longer in OGX, but it is accurate for existing Quickstarts
| | **Helm Chart Validation** | Ensure Helm templates render valid Kubernetes manifests | Every PR | kubeconform | | ||
| | **LLM Evaluation** | Assess AI response quality, policy compliance, and conversation completeness | PR (short), nightly (full) | DeepEval, LLM-as-Judge | | ||
| | **Known-Bad Regression** | Verify that evaluation metrics correctly flag known failures | Every PR | DeepEval | | ||
| | **E2E (End-to-End)** | Full deployment on a real or Kind cluster with real LLM interactions | Nightly, pre-promotion | Kind cluster, Helm | |
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This does reflect what we do in the it-self-service-agent (using Kind for a number of the runs), but I'm not sure we want that in the doc here as any quickstart that a usable subset cannot be deployed without OpenShift AI would not be able to do this.
Summary
ARCHITECTURE.mdcovering what Red Hat AI Quickstarts are, the OpenShift AI platform features they leverage, the shared AI Architecture Helm Charts library, and how to create a new quickstartdocs/index.mdwith navigable links to all architecture sectionsTest plan
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