🤖 LLM Automation - Docs & Remediation Engine

Automated Datacenter Documentation & Intelligent Auto-Remediation System

AI-powered infrastructure documentation generation with autonomous problem resolution capabilities.

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🌟 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

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🏗️ 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

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🚀 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

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💻 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 Clusters
• network - Network Infrastructure (switches, routers)
• storage - Storage Systems (SAN, NAS)
• database - Database Servers
• monitoring - Monitoring Systems (Zabbix, Prometheus)
• security - Security & Compliance

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⚙️ Background Workers (Celery)

The system uses Celery for asynchronous task processing with 4 specialized queues and 8 task types.

Worker Queues

1. documentation - Documentation generation tasks
2. auto_remediation - Auto-remediation execution tasks
3. data_collection - Infrastructure data collection
4. 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)

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📖 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

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🤖 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

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🔌 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

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🎯 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

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📊 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

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🔐 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

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🛠️ 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

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📈 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

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🤝 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

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🗺️ 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)

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🆘 Support

• Email: automation-team@commandware.com
• Documentation: https://docs.commandware.com
• Issues: https://git.commandware.com/ItOps/llm-automation-docs-and-remediation-engine/issues

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🙏 Acknowledgments

• Anthropic - Claude Sonnet 4.5 LLM
• MCP Community - Model Context Protocol
• Open Source Community - All the amazing libraries used

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📊 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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