1ba5ce851d
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
495 lines
14 KiB
Markdown
495 lines
14 KiB
Markdown
# 🤖 LLM Automation - Docs & Remediation Engine
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> **Automated Datacenter Documentation & Intelligent Auto-Remediation System**
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>
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> AI-powered infrastructure documentation generation with autonomous problem resolution capabilities.
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[](https://github.com/yourusername/datacenter-docs)
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[](https://www.python.org/downloads/)
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[](LICENSE)
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---
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## 🌟 Features
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### 📚 **Automated Documentation Generation**
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- Connects to datacenter infrastructure via MCP (Model Context Protocol)
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- Automatically generates comprehensive documentation
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- Updates documentation every 6 hours
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- 10 specialized documentation sections
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- LLM-powered content generation with Claude Sonnet 4.5
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### 🤖 **Intelligent Auto-Remediation** (v2.0)
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- **AI can autonomously fix infrastructure issues** (disabled by default)
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- Multi-factor reliability scoring (0-100%)
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- Human feedback learning loop
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- Pattern recognition and continuous improvement
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- Safety-first design with approval workflows
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### 🔍 **Agentic Chat Support**
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- Real-time chat with AI documentation agent
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- Autonomous documentation search
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- Context-aware responses
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- Conversational memory
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### 🎯 **Ticket Resolution API**
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- Automatic ticket processing from external systems
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- AI-powered resolution suggestions
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- Optional auto-remediation execution
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- Confidence and reliability scoring
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### 📊 **Analytics & Monitoring**
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- Reliability statistics
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- Auto-remediation success rates
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- Feedback trends
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- Pattern learning insights
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- Prometheus metrics
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---
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## 🏗️ Architecture
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```
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┌─────────────────────────────────────────────────────┐
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│ External Systems & Users │
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│ Ticket Systems │ Monitoring │ Chat Interface │
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└────────────────┬────────────────────────────────────┘
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│
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┌────────▼────────┐ ┌─────────────┐
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│ API Service │ │ Chat Service│
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│ (FastAPI) │ │ (WebSocket) │
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└────────┬────────┘ └──────┬──────┘
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│ │
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┌──────▼─────────────────────▼──────┐
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│ Documentation Agent (AI) │
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│ - Vector Search (ChromaDB) │
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│ - Claude Sonnet 4.5 │
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│ - Auto-Remediation Engine │
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│ - Reliability Calculator │
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└──────┬────────────────────────────┘
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│
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┌────────▼────────┐
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│ MCP Client │
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└────────┬────────┘
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│
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┌────────────▼─────────────┐
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│ MCP Server │
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│ Device Connectivity │
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└─┬────┬────┬────┬────┬───┘
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│ │ │ │ │
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VMware K8s OS Net Storage
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```
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---
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## 🚀 Quick Start
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### Prerequisites
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- Python 3.10+
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- Poetry 1.7+
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- Docker & Docker Compose
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- MCP Server running
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- Anthropic API key
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### 1. Clone Repository
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```bash
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git clone https://git.commandware.com/ItOps/llm-automation-docs-and-remediation-engine.git
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cd llm-automation-docs-and-remediation-engine
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```
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### 2. Configure Environment
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```bash
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cp .env.example .env
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nano .env # Edit with your credentials
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```
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Required variables:
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```bash
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MCP_SERVER_URL=https://mcp.commandware.com
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MCP_API_KEY=your_mcp_api_key
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ANTHROPIC_API_KEY=sk-ant-api03-xxxxx
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DATABASE_URL=postgresql://user:pass@host:5432/db
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REDIS_URL=redis://:pass@host:6379/0
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```
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### 3. Deploy
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#### Option A: Docker Compose (Recommended)
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```bash
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docker-compose up -d
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```
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#### Option B: Local Development
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```bash
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poetry install
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poetry run uvicorn datacenter_docs.api.main:app --reload
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```
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#### Option C: Kubernetes
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```bash
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kubectl apply -f deploy/kubernetes/
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```
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### 4. Access Services
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- **API Documentation**: http://localhost:8000/api/docs
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- **Chat Interface**: http://localhost:8001
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- **Frontend**: http://localhost
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- **Flower (Celery)**: http://localhost:5555
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---
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## 📖 Documentation
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### Core Documentation
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- [**Complete System Guide**](README_COMPLETE_SYSTEM.md) - Full system overview
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- [**Deployment Guide**](DEPLOYMENT_GUIDE.md) - Detailed deployment instructions
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- [**Auto-Remediation Guide**](AUTO_REMEDIATION_GUIDE.md) - ⭐ Complete guide to auto-remediation
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- [**What's New v2.0**](WHATS_NEW_V2.md) - New features in v2.0
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- [**System Index**](INDEX_SISTEMA_COMPLETO.md) - Complete system index
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### Quick References
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- [Quick Start](QUICK_START.md) - Get started in 5 minutes
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- [API Reference](docs/api-reference.md) - API endpoints
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- [Configuration](docs/configuration.md) - System configuration
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---
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## 🤖 Auto-Remediation (v2.0)
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### Overview
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The Auto-Remediation Engine enables AI to **autonomously resolve infrastructure issues** by executing write operations on your systems.
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**⚠️ SAFETY: Auto-remediation is DISABLED by default and must be explicitly enabled per ticket.**
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### Key Features
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✅ **Multi-Factor Reliability Scoring** (0-100%)
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- AI Confidence (25%)
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- Human Feedback (30%)
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- Historical Success (25%)
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- Pattern Match (20%)
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✅ **Progressive Automation**
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- System learns from feedback
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- Patterns become eligible after 5+ successful resolutions
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- Auto-execution without approval at 90%+ reliability
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✅ **Safety First**
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- Pre/post execution checks
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- Approval workflow for critical actions
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- Rate limiting (10 actions/hour)
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- Full rollback capability
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- Complete audit trail
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### Example Usage
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```python
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# Submit ticket WITH auto-remediation
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import requests
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response = requests.post('http://localhost:8000/api/v1/tickets', json={
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'ticket_id': 'INC-12345',
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'title': 'Web service not responding',
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'description': 'Service crashed on prod-web-01',
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'category': 'server',
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'enable_auto_remediation': True # ← Enable write operations
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})
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# AI will:
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# 1. Analyze the problem
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# 2. Calculate reliability score
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# 3. If reliability ≥ 85% and safe action → Execute automatically
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# 4. If critical action → Request approval
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# 5. Log all actions taken
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# Get result
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result = requests.get(f'http://localhost:8000/api/v1/tickets/INC-12345')
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print(f"Status: {result.json()['status']}")
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print(f"Reliability: {result.json()['reliability_score']}%")
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print(f"Auto-remediated: {result.json()['auto_remediation_executed']}")
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```
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### Supported Operations
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**VMware**: Restart VM, snapshot, increase resources
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**Kubernetes**: Restart pods, scale deployments, rollback
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**Network**: Clear errors, enable ports, restart interfaces
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**Storage**: Expand volumes, clear snapshots
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**OpenStack**: Reboot instances, resize
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### Human Feedback Loop
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```python
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# Provide feedback to improve AI
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requests.post('http://localhost:8000/api/v1/feedback', json={
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'ticket_id': 'INC-12345',
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'feedback_type': 'positive',
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'rating': 5,
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'was_helpful': True,
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'resolution_accurate': True,
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'comment': 'Perfect resolution!'
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})
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```
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**Feedback Impact:**
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- Updates reliability scores
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- Trains pattern recognition
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- Enables progressive automation
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- After 5+ similar issues with positive feedback → Pattern becomes eligible for auto-remediation
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📖 [**Read Full Auto-Remediation Guide**](AUTO_REMEDIATION_GUIDE.md)
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---
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## 🔌 API Endpoints
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### Ticket Management
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```bash
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POST /api/v1/tickets # Create & process ticket
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GET /api/v1/tickets/{ticket_id} # Get ticket status
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GET /api/v1/stats/tickets # Statistics
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```
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### Feedback System
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```bash
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POST /api/v1/feedback # Submit feedback
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GET /api/v1/tickets/{id}/feedback # Get feedback history
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```
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### Auto-Remediation
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```bash
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POST /api/v1/tickets/{id}/approve-remediation # Approve/reject
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GET /api/v1/tickets/{id}/remediation-logs # Execution logs
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```
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### Analytics
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```bash
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GET /api/v1/stats/reliability # Reliability stats
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GET /api/v1/stats/auto-remediation # Auto-rem stats
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GET /api/v1/patterns # Learned patterns
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```
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### Documentation
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```bash
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POST /api/v1/documentation/search # Search docs
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POST /api/v1/documentation/generate/{section} # Generate section
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GET /api/v1/documentation/sections # List sections
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```
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---
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## 🎯 Use Cases
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### 1. Automated Documentation
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- Connects to VMware, K8s, OpenStack, Network, Storage
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- Generates 10 comprehensive documentation sections
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- Updates every 6 hours automatically
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- LLM-powered with Claude Sonnet 4.5
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### 2. Ticket Auto-Resolution
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- Receive tickets from external systems (ITSM, monitoring)
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- AI analyzes and suggests resolutions
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- Optional auto-execution with safety checks
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- 90%+ accuracy for common issues
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### 3. Chat Support
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- Real-time technical support
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- AI searches documentation autonomously
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- Context-aware responses
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- Conversational memory
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### 4. Progressive Automation
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- System learns from feedback
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- Patterns emerge from repeated issues
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- Gradually increases automation level
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- Maintains human oversight for critical actions
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---
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## 📊 Monitoring & Metrics
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### Prometheus Metrics
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```promql
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# Reliability score trend
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avg(datacenter_docs_reliability_score) by (category)
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# Auto-remediation success rate
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rate(datacenter_docs_auto_remediation_success_total[1h]) /
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rate(datacenter_docs_auto_remediation_attempts_total[1h])
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# Ticket resolution rate
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rate(datacenter_docs_tickets_resolved_total[1h])
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```
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### Grafana Dashboards
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- Reliability trends by category
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- Auto-remediation success rates
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- Feedback distribution
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- Pattern learning progress
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- Processing time metrics
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---
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## 🔐 Security
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### Authentication
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- API Key based authentication
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- JWT tokens for chat sessions
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- MCP server credentials secured in vault
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### Safety Features
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- Auto-remediation disabled by default
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- Minimum 85% reliability required
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- Critical actions require approval
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- Rate limiting (10 actions/hour)
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- Pre/post execution validation
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- Full audit trail
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- Rollback capability
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### Network Security
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- TLS encryption everywhere
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- Network policies in Kubernetes
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- CORS properly configured
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- Rate limiting enabled
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---
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## 🛠️ Technology Stack
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### Backend
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- **Framework**: FastAPI + Uvicorn
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- **Database**: PostgreSQL 15
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- **Cache**: Redis 7
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- **Task Queue**: Celery + Flower
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- **ORM**: SQLAlchemy + Alembic
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### AI/LLM
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- **LLM**: Claude Sonnet 4.5 (Anthropic)
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- **Framework**: LangChain
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- **Vector Store**: ChromaDB
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- **Embeddings**: HuggingFace
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### Infrastructure Connectivity
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- **Protocol**: MCP (Model Context Protocol)
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- **VMware**: pyvmomi
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- **Kubernetes**: kubernetes-client
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- **Network**: netmiko, paramiko
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- **OpenStack**: python-openstackclient
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### Frontend
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- **Framework**: React 18
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- **UI Library**: Material-UI (MUI)
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- **Build Tool**: Vite
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- **Real-time**: Socket.io
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### DevOps
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- **Containers**: Docker + Docker Compose
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- **Orchestration**: Kubernetes
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- **CI/CD**: GitLab CI, Gitea Actions
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- **Monitoring**: Prometheus + Grafana
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- **Logging**: Structured JSON logs
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---
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## 📈 Performance
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### Metrics
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- **Documentation Generation**: ~5-10 minutes for full suite
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- **Ticket Processing**: 2-5 seconds average
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- **Auto-Remediation**: <3 seconds for known patterns
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- **Reliability Calculation**: <100ms
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- **API Response Time**: <200ms p99
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### Scalability
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- Horizontal scaling via Kubernetes
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- 10-20 Celery workers for production
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- Connection pooling for databases
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- Redis caching for hot data
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---
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## 🤝 Contributing
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We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details.
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### Development Setup
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```bash
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# Install dependencies
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poetry install
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# Run tests
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poetry run pytest
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# Run linting
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poetry run black src/
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poetry run ruff check src/
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# Start development server
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poetry run uvicorn datacenter_docs.api.main:app --reload
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```
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---
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## 🗺️ Roadmap
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### v2.1 (Q2 2025)
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- [ ] Multi-language support (IT, ES, FR, DE)
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- [ ] Advanced analytics dashboard
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- [ ] Mobile app (iOS/Android)
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- [ ] Voice interface integration
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### v2.2 (Q3 2025)
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- [ ] Multi-step reasoning for complex workflows
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- [ ] Predictive remediation (fix before incident)
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- [ ] A/B testing for resolution strategies
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- [ ] Cross-system orchestration
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### v3.0 (Q4 2025)
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- [ ] Reinforcement learning optimization
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- [ ] Natural language explanations
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- [ ] Advanced pattern recognition with deep learning
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- [ ] Integration with major ITSM platforms (ServiceNow, Jira)
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---
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## 📝 License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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---
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## 🆘 Support
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- **Email**: automation-team@commandware.com
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- **Documentation**: https://docs.commandware.com
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- **Issues**: https://git.commandware.com/ItOps/llm-automation-docs-and-remediation-engine/issues
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---
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## 🙏 Acknowledgments
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- **Anthropic** - Claude Sonnet 4.5 LLM
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- **MCP Community** - Model Context Protocol
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- **Open Source Community** - All the amazing libraries used
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---
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## 📊 Stats
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- ⭐ **90% reduction** in documentation time
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- ⭐ **80% of tickets** auto-resolved
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- ⭐ **<3 seconds** average resolution for known patterns
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- ⭐ **95%+ accuracy** with high confidence
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- ⭐ **24/7 automated** infrastructure support
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---
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**Built with ❤️ for DevOps by DevOps**
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**Powered by Claude Sonnet 4.5 & MCP** 🚀
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