# docker-compose.yml
# DC Hub × NVIDIA: MCP Data Layer + NIM Inference Layer
#
# Usage:
#   export DC_HUB_API_KEY=YOUR_DCHUB_KEY
#   docker-compose up -d
#
# Architecture:
#   mcp-client → DC Hub API (data retrieval)
#   nim-inference → NVIDIA NIM (optional AI summarization/scoring)

version: "3.8"

services:
  # MCP Client: Retrieves data from DC Hub
  mcp-client:
    image: python:3.11-slim
    container_name: dchub-mcp-client
    environment:
      - DC_HUB_API_KEY=${DC_HUB_API_KEY:-YOUR_DCHUB_KEY}
      - DC_HUB_MCP_URL=https://dchub.cloud/mcp
      - DC_HUB_BASE_URL=https://dchub.cloud/api
    volumes:
      - ./scripts:/app
    working_dir: /app
    command: >
      bash -c "pip install requests && python health_check.py"
    healthcheck:
      test: ["CMD", "python", "-c", "import requests; r=requests.get('https://dchub.cloud/api/agent/stats', headers={'X-API-Key':'${DC_HUB_API_KEY}'}); assert r.status_code==200"]
      interval: 300s
      timeout: 15s
      retries: 3

  # NIM Inference: Optional GPU-accelerated AI layer
  # Uncomment to enable NIM-powered summarization and risk scoring
  #
  # nim-inference:
  #   image: nvcr.io/nim/nvidia/llama-3.1-nemotron-70b:latest
  #   container_name: dchub-nim-inference
  #   runtime: nvidia
  #   environment:
  #     - NVIDIA_VISIBLE_DEVICES=all
  #   ports:
  #     - "8000:8000"
  #   deploy:
  #     resources:
  #       reservations:
  #         devices:
  #           - driver: nvidia
  #             count: 1
  #             capabilities: [gpu]