4 Commits

Author	SHA1	Message	Date
dnviti	c8aebaaee3	feat: implement automatic ChromaDB indexing and loop-based documentation generation ... - Add automatic ChromaDB indexing after documentation generation - Implement loop-based section generation for individual VM and container documentation - Fix Celery anti-pattern in generate_proxmox_docs task (removed blocking .get() call) - Share ChromaDB vector store volume between worker and chat services - Add documentation management UI to frontend with manual job triggering and log viewing - Fix frontend Socket.IO connection URL to point to correct chat service port - Enhance DocumentationAgent.index_documentation() with automatic cleanup of old documents - Update Proxmox template to generate individual files for each VM and container This enables the RAG system to properly respond with infrastructure-specific information from the generated documentation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-10-21 03:00:50 +02:00
dnviti	6f5deb0879	feat: add multilingual chat support with markdown rendering ... - Fix Socket.IO proxy configuration in nginx for chat connectivity - Add Socket.IO path routing (/socket.io/) with WebSocket upgrade support - Fix frontend healthcheck to use curl instead of wget - Add react-markdown and remark-gfm for proper markdown rendering - Implement language selector in chat interface (8 languages supported) - Add language parameter to chat agent and LLM prompts - Support English, Italian, Spanish, French, German, Portuguese, Chinese, Japanese This resolves the chat connection issues and enables users to receive AI responses in their preferred language with properly formatted markdown. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-10-20 19:14:38 +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
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