Add enterprise AI platform for LLM product development and deployment

[Copilot]

Implements production platform for designing, building, and deploying multi-LLM AI products with real-time customer data iteration.

Core Infrastructure

• FastAPI backend with async operations, OpenAPI docs, Prometheus metrics
• Multi-LLM service - Unified interface for OpenAI, Anthropic with token tracking
• Multi-agent system - 5 specialized agents (Research, Analyst, Writer, Coder, Orchestrator)
• RAG service - Document ingestion pipeline with vector store integration (Pinecone, Weaviate, ChromaDB)

API Endpoints

• /api/v1/llm/* - Completions, chat, provider management
• /api/v1/agents/* - Single and multi-agent workflow execution
• /api/v1/projects/* - AI project lifecycle management
• /api/v1/deployments/* - Environment deployment with metrics, scaling, health checks

Web Interface

Interactive dashboard with project management, deployment control, LLM console, and agent execution interface.

Infrastructure

• Docker Compose with PostgreSQL, Redis, ChromaDB
• GitHub Actions CI/CD with security scanning
• Automated setup script

Security

Fixed 7 vulnerabilities (2 critical, 5 high):

• fastapi: 0.109.0 → 0.115.5 (ReDoS)
• langchain-community: 0.0.16 → 0.3.27 (XXE, SSRF, pickle deserialization)
• python-multipart: 0.0.6 → 0.0.22 (arbitrary file write, DoS, ReDoS)

All tests passing (7/7). Zero vulnerabilities remaining.

Usage

# Multi-LLM completion
POST /api/v1/llm/completion
{
  "prompt": "Analyze customer data",
  "provider": "openai",
  "temperature": 0.7
}

# Multi-agent workflow
POST /api/v1/agents/multi-agent
{
  "tasks": [
    {"agent_type": "research", "instruction": "Gather requirements"},
    {"agent_type": "analyst", "instruction": "Analyze patterns"},
    {"agent_type": "writer", "instruction": "Generate report"}
  ]
}

Documentation in docs/: API reference, architecture, deployment guide, getting started.

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