llm-automation-docs-and-remediation-engine/.gitea/workflows

CI/CD Workflows

This directory contains separated CI/CD pipeline workflows for the Datacenter Documentation System.

Workflow Organization

Each workflow is designed for a specific purpose and can run independently:

1. Code Quality (lint.yml)

Triggers: Push/PR to main/develop

Runs code quality checks:

• Black: Code formatting verification
• Ruff: Linting and code quality
• MyPy: Static type checking

Purpose: Ensure code quality standards before merge.

---

2. Testing (test.yml)

Triggers: Push/PR to main/develop

Runs test suite with coverage:

• Unit tests with pytest
• Integration tests (when available)
• Coverage reporting to CodeCov
• MongoDB service container for database tests

Purpose: Verify functionality and prevent regressions.

---

3. Security (security.yml)

Triggers: Push/PR to main/develop, Weekly schedule (Monday)

Security scanning:

• Bandit: Python code security analysis
• Safety: Dependency vulnerability scanning (future)

Purpose: Identify security vulnerabilities early.

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4. Build (build.yml)

Triggers: Push to main, Version tags

Builds and pushes Docker images:

• Components: API, Chat, Worker, Frontend
• Registry: Gitea Container Registry
• Caching: BuildKit cache for faster builds
• Tagging: Branch names, SHAs, semantic versions

Purpose: Create deployable container images.

---

5. Deploy - Staging (deploy-staging.yml)

Triggers: Push to main (after successful build)

Deploys to staging environment:

• Platform: Kubernetes
• Namespace: datacenter-docs
• Components: API, Chat, Worker
• Validation: Rollout status checks

Purpose: Test changes in staging before production.

---

6. Deploy - Production (deploy-production.yml)

Triggers: Version tags (v*)

Deploys to production environment:

• Platform: Kubernetes
• Namespace: datacenter-docs
• Components: API, Chat, Worker
• Validation: Rollout status + smoke tests

Purpose: Deploy stable versions to production.

---

7. Documentation Generation (docs-generation.yml)

Triggers: Schedule (every 6 hours), Manual

Automated documentation generation:

• Collects infrastructure data
• Generates documentation via LLM
• Uploads artifacts
• Commits changes (if any)

Purpose: Keep documentation up-to-date automatically.

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Workflow Dependencies

┌─────────────┐
│   lint.yml  │ ─┐
└─────────────┘  │
                 │
┌─────────────┐  │
│   test.yml  │ ─┤
└─────────────┘  │
                 ├──> ┌──────────────┐     ┌────────────────────┐
┌─────────────┐  │    │  build.yml   │ ──> │ deploy-staging.yml │
│security.yml │ ─┘    └──────────────┘     └────────────────────┘
└─────────────┘            │
                           │ (on tags)
                           ▼
                    ┌──────────────────────┐
                    │deploy-production.yml │
                    └──────────────────────┘

┌──────────────────────┐
│docs-generation.yml   │ (independent, scheduled)
└──────────────────────┘

Environment Variables

Required Secrets

• USERNAME: Registry username
• TOKEN: Registry access token
• LLM_API_KEY: LLM provider API key
• LLM_BASE_URL: LLM provider base URL
• MCP_SERVER_URL: Infrastructure API endpoint
• KUBE_CONFIG_STAGING: Staging Kubernetes config
• KUBE_CONFIG_PRODUCTION: Production Kubernetes config

Required Variables

• PACKAGES_REGISTRY: Container registry URL (e.g., git.commandware.com)

Running Workflows

Automatic Triggers

All workflows run automatically based on their configured triggers.

Manual Triggers

Some workflows support manual triggering via Gitea Actions UI:

• docs-generation.yml (workflow_dispatch)

Best Practices

1. Always pass lint and test before merging to main
2. Review security reports weekly
3. Test in staging before creating version tags
4. Use semantic versioning for production releases (v1.2.3)
5. Monitor deployment rollouts in Kubernetes

Troubleshooting

Build Failures

• Check Docker build logs
• Verify poetry.lock is committed
• Ensure all dependencies are available

Test Failures

• Review test output in job logs
• Check MongoDB service health
• Verify environment variables

Deployment Failures

• Check Kubernetes cluster status
• Verify secrets are configured
• Review deployment rollout logs

Maintenance

• Review and update workflow configurations quarterly
• Update action versions when new releases are available
• Monitor workflow execution times and optimize as needed