8 Commits

Author	SHA1	Message	Date
dnviti	4a8372f0d1	fix: upgrade Poetry to 2.2.1 for poetry.lock compatibility ... Resolve Docker build failure caused by poetry.lock incompatibility: **Root Cause:** - Local Poetry version: 2.2.1 - Dockerfile Poetry version: 1.8.0 - poetry.lock generated with 2.2.1 not compatible with 1.8.0 - Build failed: "Dependency walk failed at triton (==3.5.0)" **Solution:** - Upgrade Poetry to 2.2.1 in all Dockerfiles (api, chat, worker) - Update CI/CD pipeline to match (POETRY_VERSION: 2.2.1) - Successfully tested Docker build with new version **Files Modified:** - deploy/docker/Dockerfile.api - deploy/docker/Dockerfile.chat - deploy/docker/Dockerfile.worker - .gitea/workflows/build.yml 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-10-21 15:55:15 +02:00
dnviti	4d2bf99d12	feat: add comprehensive caching to CI/CD pipeline ... Optimize Gitea Actions pipeline with multi-layer caching strategy: **Lint Job:** - Cache Poetry installation (~/.local) - avoids reinstalling Poetry - Cache Poetry dependencies (.venv + ~/.cache/pypoetry) - reuses installed packages - Cache key based on poetry.lock hash for automatic invalidation on dependency changes **Build Job:** - Cache Docker Buildx layers (/tmp/.buildx-cache) - speeds up incremental builds - Dual cache strategy: local filesystem + container registry - Cache rotation to prevent unlimited growth - Per-component cache keys for optimal reuse **Expected Performance:** - Lint job: ~2-3x faster after first run (skip Poetry + deps installation) - Build job: ~3-5x faster on incremental builds (reuse Docker layers) - First run unchanged, subsequent runs significantly faster 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-10-21 15:13:48 +02:00
dnviti	2db606ea00	refactor: simplify CI/CD to single build pipeline ... Remove all unnecessary pipelines and consolidate into a single simple build pipeline that runs on every push to main. ## Pipeline Flow 1. **Lint Job**: Code quality checks - Black (formatting) - Ruff (linting) - MyPy (type checking) 2. **Build & Push Job**: Docker images (needs: lint) - Matrix build: [api, chat, worker, frontend] - Push to container registry - BuildKit caching ## Removed Pipelines - ❌ test.yml (no tests yet) - ❌ security.yml (not needed for now) - ❌ deploy-staging.yml (manual deployment) - ❌ deploy-production.yml (manual deployment) - ❌ docs-generation.yml (not needed) - ❌ lint.yml (merged into build) - ❌ ci.yml.old (old backup) - ❌ README.md (unnecessary complexity) ## Benefits - ✅ Simple and focused - ✅ Fast feedback on main pushes - ✅ Quality checks before build - ✅ Easy to understand and maintain 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-10-21 13:42:41 +02:00
dnviti	d6d44270ee	refactor: reorganize CI/CD pipelines into separate workflow files ... BREAKING CHANGE: Monolithic ci.yml split into focused pipeline files ## Pipeline Reorganization Split single CI/CD pipeline into 7 specialized workflows: 1. **lint.yml** - Code quality checks (Black, Ruff, MyPy) 2. **test.yml** - Test suite with coverage reporting 3. **security.yml** - Security scanning (Bandit) 4. **build.yml** - Docker image builds and registry push 5. **deploy-staging.yml** - Staging environment deployment 6. **deploy-production.yml** - Production deployment (tags only) 7. **docs-generation.yml** - Scheduled documentation generation ## Benefits - **Modularity**: Each pipeline has single responsibility - **Performance**: Workflows run independently, faster feedback - **Clarity**: Easier to understand and maintain - **Flexibility**: Trigger pipelines independently - **Debugging**: Isolated failures easier to diagnose ## Dockerfile Improvements - Fix FROM AS casing (was 'as', now 'AS') in all Dockerfiles - Resolves Docker build warnings - Improves consistency across build files ## Documentation - Added .gitea/workflows/README.md with: - Workflow descriptions and triggers - Dependency diagram - Environment variables reference - Troubleshooting guide - Best practices ## Migration Notes - Old monolithic pipeline backed up as ci.yml.old - All triggers preserved from original pipeline - No changes to build or deploy logic - Same environment variables and secrets required 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-10-21 13:37:21 +02:00
dnviti	52655e9eee	feat: Implement CLI tool, Celery workers, and VMware collector ... Complete implementation of core MVP components: CLI Tool (src/datacenter_docs/cli.py): - 11 commands for system management (serve, worker, init-db, generate, etc.) - Auto-remediation policy management (enable/disable/status) - System statistics and monitoring - Rich formatted output with tables and panels Celery Workers (src/datacenter_docs/workers/): - celery_app.py with 4 specialized queues (documentation, auto_remediation, data_collection, maintenance) - tasks.py with 8 async tasks integrated with MongoDB/Beanie - Celery Beat scheduling (6h docs, 1h data collection, 15m metrics, 2am cleanup) - Rate limiting (10 auto-remediation/h) and timeout configuration - Task lifecycle signals and comprehensive logging VMware Collector (src/datacenter_docs/collectors/): - BaseCollector abstract class with full workflow (connect/collect/validate/store/disconnect) - VMwareCollector for vSphere infrastructure data collection - Collects VMs, ESXi hosts, clusters, datastores, networks with statistics - MCP client integration with mock data fallback for development - MongoDB storage via AuditLog and data validation Documentation & Configuration: - Updated README.md with CLI commands and Workers sections - Updated TODO.md with project status (55% completion) - Added CLAUDE.md with comprehensive project instructions - Added Docker compose setup for development environment Project Status: - Completion: 50% -> 55% - MVP Milestone: 80% complete (only Infrastructure Generator remaining) - Estimated time to MVP: 1-2 days 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-10-19 22:29:59 +02:00
dnviti	cebd69c780	fix: Update all pipelines to Python 3.14 ... Python 3.13 is causing installation errors in CI/CD runners. Upgrading to Python 3.14 which is the latest stable version. Changes: - pyproject.toml: Update python requirement to ^3.14 - pyproject.toml: Update MyPy python_version to 3.14 - pyproject.toml: Update Black target-version to py314 - .gitlab-ci.yml: Update PYTHON_VERSION to 3.14 - .github/workflows/build-deploy.yml: Update PYTHON_VERSION to 3.14 - .gitea/workflows/ci.yml: Update PYTHON_VERSION to 3.14 This fixes the CI/CD error: "rm: cannot remove '/opt/hostedtoolcache/Python/3.13.9/x64/lib/python3.13/__pycache__': Directory not empty" 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-10-19 14:22:17 +02:00
dnviti	ac90dff43a	fix: Update GitHub and Gitea workflows for Python 3.13 and MongoDB ... Changes to GitHub Actions (.github/workflows/build-deploy.yml): - Update PYTHON_VERSION from 3.11 to 3.13 Changes to Gitea Actions (.gitea/workflows/ci.yml): - Update PYTHON_VERSION from 3.10 to 3.13 - Update POETRY_VERSION from 1.7.1 to 1.8.0 - Replace PostgreSQL and Redis services with MongoDB - Update service health check for MongoDB (mongosh) - Update test environment variables for MongoDB - Remove DATABASE_URL and REDIS_URL - Add MONGODB_URL and MONGODB_DATABASE This ensures all CI/CD pipelines are aligned with: - Python 3.13 requirement in pyproject.toml - MongoDB migration from PostgreSQL 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-10-19 14:14:57 +02:00
LLM Automation System	1ba5ce851d	Initial commit: LLM Automation Docs & Remediation Engine v2.0 ... Features: - Automated datacenter documentation generation - MCP integration for device connectivity - Auto-remediation engine with safety checks - Multi-factor reliability scoring (0-100%) - Human feedback learning loop - Pattern recognition and continuous improvement - Agentic chat support with AI - API for ticket resolution - Frontend React with Material-UI - CI/CD pipelines (GitLab + Gitea) - Docker & Kubernetes deployment - Complete documentation and guides v2.0 Highlights: - Auto-remediation with write operations (disabled by default) - Reliability calculator with 4-factor scoring - Human feedback system for continuous learning - Pattern-based progressive automation - Approval workflow for critical actions - Full audit trail and rollback capability	2025-10-17 23:47:28 +00:00