# AI Integration Architecture\n\n## Overview\n\nThe provisioning platform's AI system provides intelligent capabilities for configuration generation, troubleshooting, and automation. The\narchitecture consists of multiple layers designed for reliability, security, and performance.\n\n## Core Components - Production-Ready\n\n### 1. AI Service (`provisioning/platform/ai-service`)\n\n**Status**: ✅ Production-Ready (2,500+ lines Rust code)\n\nThe core AI service provides:\n- Multi-provider LLM support (Anthropic Claude, OpenAI GPT-4, local models)\n- Streaming response support for real-time feedback\n- Request caching with LRU and semantic similarity\n- Rate limiting and cost control\n- Comprehensive error handling\n- HTTP REST API on port 8083\n\n**Supported Models**:\n- Claude Sonnet 4, Claude Opus 4 (Anthropic)\n- GPT-4 Turbo, GPT-4 (OpenAI)\n- Llama 3, Mistral (local/on-premise)\n\n### 2. RAG System (Retrieval-Augmented Generation)\n\n**Status**: ✅ Production-Ready (22/22 tests passing)\n\nThe RAG system enables AI to access and reason over platform documentation:\n- Vector embeddings via SurrealDB vector store\n- Hybrid search: vector similarity + BM25 keyword search\n- Document chunking (code and markdown aware)\n- Relevance ranking and context selection\n- Semantic caching for repeated queries\n\n**Capabilities**:\n```\nprovisioning ai query "How do I set up Kubernetes?"\nprovisioning ai template "Describe my infrastructure"\n```\n\n### 3. MCP Server (Model Context Protocol)\n\n**Status**: ✅ Production-Ready\n\nProvides Model Context Protocol integration:\n- Standardized tool interface for LLMs\n- Complex workflow composition\n- Integration with external AI systems (Claude, other LLMs)\n- Tool calling for provisioning operations\n\n### 4. CLI Integration\n\n**Status**: ✅ Production-Ready\n\nInteractive commands:\n```\nprovisioning ai template --prompt "Describe infrastructure"\nprovisioning ai query --prompt "Configuration question"\nprovisioning ai chat # Interactive mode\n```\n\n**Configuration**:\n```\n[ai]\nenabled = true\nprovider = "anthropic" # or "openai" or "local"\nmodel = "claude-sonnet-4"\n\n[ai.cache]\nenabled = true\nsemantic_similarity = true\nttl_seconds = 3600\n\n[ai.limits]\nmax_tokens = 4096\ntemperature = 0.7\n```\n\n## Planned Components - Q2 2025\n\n### Autonomous Agents (typdialog-ag)\n\n**Status**: 🔴 Planned\n\nSelf-directed agents for complex tasks:\n- Multi-step workflow execution\n- Decision making and adaptation\n- Monitoring and self-healing recommendations\n\n### AI-Assisted Forms (typdialog-ai)\n\n**Status**: 🔴 Planned\n\nReal-time AI suggestions in configuration forms:\n- Context-aware field recommendations\n- Validation error explanations\n- Auto-completion for infrastructure patterns\n\n### Advanced Features\n\n- Fine-tuning capabilities for custom models\n- Autonomous workflow execution with human approval\n- Cedar authorization policies for AI actions\n- Custom knowledge bases per workspace\n\n## Architecture Diagram\n\n```\n┌─────────────────────────────────────────────────┐\n│ User Interface │\n│ ├── CLI (provisioning ai ...) │\n│ ├── Web UI (typdialog) │\n│ └── MCP Client (Claude, etc.) │\n└──────────────┬──────────────────────────────────┘\n ↓\n┌──────────────────────────────────────────────────┐\n│ AI Service (Port 8083) │\n│ ├── Request Router │\n│ ├── Cache Layer (LRU + Semantic) │\n│ ├── Prompt Engineering │\n│ └── Response Streaming │\n└────<E29480>
