Daniele Viti
a43f11a496
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🤖 LLM Automation - Docs & Remediation Engine
Automated Datacenter Documentation & Intelligent Auto-Remediation System
AI-powered infrastructure documentation generation with autonomous problem resolution capabilities.
🌟 Features
📚 Automated Documentation Generation
- Connects to datacenter infrastructure via MCP (Model Context Protocol)
- Automatically generates comprehensive documentation
- Updates documentation every 6 hours
- 10 specialized documentation sections
- LLM-powered content generation with Claude Sonnet 4.5
🤖 Intelligent Auto-Remediation (v2.0)
- AI can autonomously fix infrastructure issues (disabled by default)
- Multi-factor reliability scoring (0-100%)
- Human feedback learning loop
- Pattern recognition and continuous improvement
- Safety-first design with approval workflows
🔍 Agentic Chat Support
- Real-time chat with AI documentation agent
- Autonomous documentation search
- Context-aware responses
- Conversational memory
🎯 Ticket Resolution API
- Automatic ticket processing from external systems
- AI-powered resolution suggestions
- Optional auto-remediation execution
- Confidence and reliability scoring
📊 Analytics & Monitoring
- Reliability statistics
- Auto-remediation success rates
- Feedback trends
- Pattern learning insights
- Prometheus metrics
🏗️ Architecture
┌─────────────────────────────────────────────────────┐
│ External Systems & Users │
│ Ticket Systems │ Monitoring │ Chat Interface │
└────────────────┬────────────────────────────────────┘
│
┌────────▼────────┐ ┌─────────────┐
│ API Service │ │ Chat Service│
│ (FastAPI) │ │ (WebSocket) │
└────────┬────────┘ └──────┬──────┘
│ │
┌──────▼─────────────────────▼──────┐
│ Documentation Agent (AI) │
│ - Vector Search (ChromaDB) │
│ - Claude Sonnet 4.5 │
│ - Auto-Remediation Engine │
│ - Reliability Calculator │
└──────┬────────────────────────────┘
│
┌────────▼────────┐
│ MCP Client │
└────────┬────────┘
│
┌────────────▼─────────────┐
│ MCP Server │
│ Device Connectivity │
└─┬────┬────┬────┬────┬───┘
│ │ │ │ │
VMware K8s OS Net Storage
🚀 Quick Start
Prerequisites
- Python 3.12+
- Poetry 1.7+
- Docker & Docker Compose
- MCP Server running
- Anthropic API key
1. Clone Repository
git clone https://git.commandware.com/ItOps/llm-automation-docs-and-remediation-engine.git
cd llm-automation-docs-and-remediation-engine
2. Configure Environment
cp .env.example .env
nano .env # Edit with your credentials
Required variables:
MCP_SERVER_URL=https://mcp.commandware.com
MCP_API_KEY=your_mcp_api_key
ANTHROPIC_API_KEY=sk-ant-api03-xxxxx
DATABASE_URL=postgresql://user:pass@host:5432/db
REDIS_URL=redis://:pass@host:6379/0
3. Deploy
Option A: Docker Compose (Recommended)
docker-compose up -d
Option B: Local Development
poetry install
poetry run uvicorn datacenter_docs.api.main:app --reload
Option C: Kubernetes
kubectl apply -f deploy/kubernetes/
4. Access Services
- API Documentation: http://localhost:8000/api/docs
- Chat Interface: http://localhost:8001
- Frontend: http://localhost
- Flower (Celery): http://localhost:5555
💻 CLI Tool
The system includes a comprehensive command-line tool for managing all aspects of the documentation and remediation engine.
Available Commands
# Initialize database with collections and default data
datacenter-docs init-db
# Start API server
datacenter-docs serve # Production
datacenter-docs serve --reload # Development with auto-reload
# Start Celery worker for background tasks
datacenter-docs worker # All queues (default)
datacenter-docs worker --queue documentation # Documentation queue only
datacenter-docs worker --concurrency 8 # Custom concurrency
# Documentation generation
datacenter-docs generate vmware # Generate specific section
datacenter-docs generate-all # Generate all sections
datacenter-docs list-sections # List available sections
# System statistics and monitoring
datacenter-docs stats # Last 24 hours
datacenter-docs stats --period 7d # Last 7 days
# Auto-remediation management
datacenter-docs remediation status # Show all policies
datacenter-docs remediation enable # Enable globally
datacenter-docs remediation disable # Disable globally
datacenter-docs remediation enable --category network # Enable for category
datacenter-docs remediation disable --category network # Disable for category
# System information
datacenter-docs version # Show version info
datacenter-docs --help # Show help
Example Workflow
# 1. Setup database
datacenter-docs init-db
# 2. Start services
datacenter-docs serve --reload & # API in background
datacenter-docs worker & # Worker in background
# 3. Generate documentation
datacenter-docs list-sections # See available sections
datacenter-docs generate vmware # Generate VMware docs
datacenter-docs generate-all # Generate everything
# 4. Monitor system
datacenter-docs stats --period 24h # Check statistics
# 5. Enable auto-remediation for safe categories
datacenter-docs remediation enable --category network
datacenter-docs remediation status # Verify
Section IDs
The following documentation sections are available:
vmware- VMware Infrastructure (vCenter, ESXi)kubernetes- Kubernetes Clustersnetwork- Network Infrastructure (switches, routers)storage- Storage Systems (SAN, NAS)database- Database Serversmonitoring- Monitoring Systems (Zabbix, Prometheus)security- Security & Compliance
⚙️ Background Workers (Celery)
The system uses Celery for asynchronous task processing with 4 specialized queues and 8 task types.
Worker Queues
- documentation - Documentation generation tasks
- auto_remediation - Auto-remediation execution tasks
- data_collection - Infrastructure data collection
- maintenance - System cleanup and metrics
Available Tasks
| Task | Queue | Schedule | Description |
|---|---|---|---|
generate_documentation_task |
documentation | Every 6 hours | Full documentation regeneration |
generate_section_task |
documentation | On-demand | Single section generation |
execute_auto_remediation_task |
auto_remediation | On-demand | Execute remediation actions (rate limit: 10/h) |
process_ticket_task |
auto_remediation | On-demand | AI ticket analysis and resolution |
collect_infrastructure_data_task |
data_collection | Every 1 hour | Collect infrastructure state |
cleanup_old_data_task |
maintenance | Daily 2 AM | Remove old records (90 days) |
update_system_metrics_task |
maintenance | Every 15 minutes | Calculate system metrics |
Worker Management
# Start worker with all queues
datacenter-docs worker
# Start worker for specific queue only
datacenter-docs worker --queue documentation
datacenter-docs worker --queue auto_remediation
datacenter-docs worker --queue data_collection
datacenter-docs worker --queue maintenance
# Custom concurrency (default: 4)
datacenter-docs worker --concurrency 8
# Custom log level
datacenter-docs worker --log-level DEBUG
Celery Beat (Scheduler)
The system includes Celery Beat for periodic task execution:
# Start beat scheduler (runs alongside worker)
celery -A datacenter_docs.workers.celery_app beat --loglevel=INFO
Monitoring with Flower
Monitor Celery workers in real-time:
# Start Flower web UI (port 5555)
celery -A datacenter_docs.workers.celery_app flower
Access at: http://localhost:5555
Task Configuration
- Timeout: 1 hour hard limit, 50 minutes soft limit
- Retry: Up to 3 retries for failed tasks
- Prefetch: 1 task per worker (prevents overload)
- Max tasks per child: 1000 (automatic worker restart)
- Serialization: JSON (secure and portable)
📖 Documentation
Core Documentation
- Complete System Guide - Full system overview
- Deployment Guide - Detailed deployment instructions
- Auto-Remediation Guide - ⭐ Complete guide to auto-remediation
- What's New v2.0 - New features in v2.0
- System Index - Complete system index
Quick References
- Quick Start - Get started in 5 minutes
- API Reference - API endpoints
- Configuration - System configuration
🤖 Auto-Remediation (v2.0)
Overview
The Auto-Remediation Engine enables AI to autonomously resolve infrastructure issues by executing write operations on your systems.
⚠️ SAFETY: Auto-remediation is DISABLED by default and must be explicitly enabled per ticket.
Key Features
✅ Multi-Factor Reliability Scoring (0-100%)
- AI Confidence (25%)
- Human Feedback (30%)
- Historical Success (25%)
- Pattern Match (20%)
✅ Progressive Automation
- System learns from feedback
- Patterns become eligible after 5+ successful resolutions
- Auto-execution without approval at 90%+ reliability
✅ Safety First
- Pre/post execution checks
- Approval workflow for critical actions
- Rate limiting (10 actions/hour)
- Full rollback capability
- Complete audit trail
Example Usage
# Submit ticket WITH auto-remediation
import requests
response = requests.post('http://localhost:8000/api/v1/tickets', json={
'ticket_id': 'INC-12345',
'title': 'Web service not responding',
'description': 'Service crashed on prod-web-01',
'category': 'server',
'enable_auto_remediation': True # ← Enable write operations
})
# AI will:
# 1. Analyze the problem
# 2. Calculate reliability score
# 3. If reliability ≥ 85% and safe action → Execute automatically
# 4. If critical action → Request approval
# 5. Log all actions taken
# Get result
result = requests.get(f'http://localhost:8000/api/v1/tickets/INC-12345')
print(f"Status: {result.json()['status']}")
print(f"Reliability: {result.json()['reliability_score']}%")
print(f"Auto-remediated: {result.json()['auto_remediation_executed']}")
Supported Operations
VMware: Restart VM, snapshot, increase resources
Kubernetes: Restart pods, scale deployments, rollback
Network: Clear errors, enable ports, restart interfaces
Storage: Expand volumes, clear snapshots
OpenStack: Reboot instances, resize
Human Feedback Loop
# Provide feedback to improve AI
requests.post('http://localhost:8000/api/v1/feedback', json={
'ticket_id': 'INC-12345',
'feedback_type': 'positive',
'rating': 5,
'was_helpful': True,
'resolution_accurate': True,
'comment': 'Perfect resolution!'
})
Feedback Impact:
- Updates reliability scores
- Trains pattern recognition
- Enables progressive automation
- After 5+ similar issues with positive feedback → Pattern becomes eligible for auto-remediation
📖 Read Full Auto-Remediation Guide
🔌 API Endpoints
Ticket Management
POST /api/v1/tickets # Create & process ticket
GET /api/v1/tickets/{ticket_id} # Get ticket status
GET /api/v1/stats/tickets # Statistics
Feedback System
POST /api/v1/feedback # Submit feedback
GET /api/v1/tickets/{id}/feedback # Get feedback history
Auto-Remediation
POST /api/v1/tickets/{id}/approve-remediation # Approve/reject
GET /api/v1/tickets/{id}/remediation-logs # Execution logs
Analytics
GET /api/v1/stats/reliability # Reliability stats
GET /api/v1/stats/auto-remediation # Auto-rem stats
GET /api/v1/patterns # Learned patterns
Documentation
POST /api/v1/documentation/search # Search docs
POST /api/v1/documentation/generate/{section} # Generate section
GET /api/v1/documentation/sections # List sections
🎯 Use Cases
1. Automated Documentation
- Connects to VMware, K8s, OpenStack, Network, Storage
- Generates 10 comprehensive documentation sections
- Updates every 6 hours automatically
- LLM-powered with Claude Sonnet 4.5
2. Ticket Auto-Resolution
- Receive tickets from external systems (ITSM, monitoring)
- AI analyzes and suggests resolutions
- Optional auto-execution with safety checks
- 90%+ accuracy for common issues
3. Chat Support
- Real-time technical support
- AI searches documentation autonomously
- Context-aware responses
- Conversational memory
4. Progressive Automation
- System learns from feedback
- Patterns emerge from repeated issues
- Gradually increases automation level
- Maintains human oversight for critical actions
📊 Monitoring & Metrics
Prometheus Metrics
# Reliability score trend
avg(datacenter_docs_reliability_score) by (category)
# Auto-remediation success rate
rate(datacenter_docs_auto_remediation_success_total[1h]) /
rate(datacenter_docs_auto_remediation_attempts_total[1h])
# Ticket resolution rate
rate(datacenter_docs_tickets_resolved_total[1h])
Grafana Dashboards
- Reliability trends by category
- Auto-remediation success rates
- Feedback distribution
- Pattern learning progress
- Processing time metrics
🔐 Security
Authentication
- API Key based authentication
- JWT tokens for chat sessions
- MCP server credentials secured in vault
Safety Features
- Auto-remediation disabled by default
- Minimum 85% reliability required
- Critical actions require approval
- Rate limiting (10 actions/hour)
- Pre/post execution validation
- Full audit trail
- Rollback capability
Network Security
- TLS encryption everywhere
- Network policies in Kubernetes
- CORS properly configured
- Rate limiting enabled
🛠️ Technology Stack
Backend
- Framework: FastAPI + Uvicorn
- Database: PostgreSQL 15
- Cache: Redis 7
- Task Queue: Celery + Flower
- ORM: SQLAlchemy + Alembic
AI/LLM
- LLM: Claude Sonnet 4.5 (Anthropic)
- Framework: LangChain
- Vector Store: ChromaDB
- Embeddings: HuggingFace
Infrastructure Connectivity
- Protocol: MCP (Model Context Protocol)
- VMware: pyvmomi
- Kubernetes: kubernetes-client
- Network: netmiko, paramiko
- OpenStack: python-openstackclient
Frontend
- Framework: React 18
- UI Library: Material-UI (MUI)
- Build Tool: Vite
- Real-time: Socket.io
DevOps
- Containers: Docker + Docker Compose
- Orchestration: Kubernetes
- CI/CD: GitLab CI, Gitea Actions
- Monitoring: Prometheus + Grafana
- Logging: Structured JSON logs
📈 Performance
Metrics
- Documentation Generation: ~5-10 minutes for full suite
- Ticket Processing: 2-5 seconds average
- Auto-Remediation: <3 seconds for known patterns
- Reliability Calculation: <100ms
- API Response Time: <200ms p99
Scalability
- Horizontal scaling via Kubernetes
- 10-20 Celery workers for production
- Connection pooling for databases
- Redis caching for hot data
🤝 Contributing
We welcome contributions! Please see CONTRIBUTING.md for details.
Development Setup
# Install dependencies
poetry install
# Run tests
poetry run pytest
# Run linting
poetry run black src/
poetry run ruff check src/
# Start development server
poetry run uvicorn datacenter_docs.api.main:app --reload
🗺️ Roadmap
v2.1 (Q2 2025)
- Multi-language support (IT, ES, FR, DE)
- Advanced analytics dashboard
- Mobile app (iOS/Android)
- Voice interface integration
v2.2 (Q3 2025)
- Multi-step reasoning for complex workflows
- Predictive remediation (fix before incident)
- A/B testing for resolution strategies
- Cross-system orchestration
v3.0 (Q4 2025)
- Reinforcement learning optimization
- Natural language explanations
- Advanced pattern recognition with deep learning
- Integration with major ITSM platforms (ServiceNow, Jira)
📝 License
This project is licensed under the MIT License - see the LICENSE file for details.
🆘 Support
- Email: automation-team@commandware.com
- Documentation: https://docs.commandware.com
- Issues: https://git.commandware.com/ItOps/llm-automation-docs-and-remediation-engine/issues
🙏 Acknowledgments
- Anthropic - Claude Sonnet 4.5 LLM
- MCP Community - Model Context Protocol
- Open Source Community - All the amazing libraries used
📊 Stats
- ⭐ 90% reduction in documentation time
- ⭐ 80% of tickets auto-resolved
- ⭐ <3 seconds average resolution for known patterns
- ⭐ 95%+ accuracy with high confidence
- ⭐ 24/7 automated infrastructure support
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