AI-Code-Executor/docker-compose.yml at main · Ark0N/AI-Code-Executor · GitHub

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# =============================================================================
# AI Code Executor - Docker Compose Configuration
# =============================================================================
# Usage:
#   docker compose up -d          # Start in background
#   docker compose logs -f        # View logs
#   docker compose down           # Stop
#   docker compose down -v        # Stop and remove volumes
# =============================================================================

services:
  ai-code-executor:
    build:
      context: .
      dockerfile: Dockerfile.app
    container_name: ai-code-executor
    restart: unless-stopped

    ports:
      - "${PORT:-8000}:8000"

    volumes:
      # CRITICAL: Mount Docker socket for container management
      # This allows the app to create code execution containers
      - /var/run/docker.sock:/var/run/docker.sock

      # Persist database and exported images
      - ai-executor-data:/app/data
      - ai-executor-exports:/app/docker_images_exported

      # Mount the sandbox Dockerfile so the app can build the execution image
      - ./Dockerfile:/app/Dockerfile:ro

    environment:
      # Server
      - HOST=0.0.0.0
      - PORT=8000

      # Database (persisted in volume)
      - DATABASE_URL=sqlite+aiosqlite:///./data/conversations.db

      # Docker settings
      - DOCKER_EXPORT_PATH=/app/docker_images_exported
      - DOCKER_CPU_CORES=${DOCKER_CPU_CORES:-2}
      - DOCKER_MEMORY_LIMIT=${DOCKER_MEMORY_LIMIT:-8g}
      - DOCKER_STORAGE_LIMIT=${DOCKER_STORAGE_LIMIT:-10g}
      - DOCKER_EXECUTION_TIMEOUT=${DOCKER_EXECUTION_TIMEOUT:-30}

      # API Keys (set in .env file or pass directly)
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
      - OPENAI_API_KEY=${OPENAI_API_KEY:-}
      - GEMINI_API_KEY=${GEMINI_API_KEY:-}

      # Ollama (optional - for local AI models)
      # Use host.docker.internal to reach Ollama running on host
      - OLLAMA_HOST=${OLLAMA_HOST:-http://host.docker.internal:11434}

      # LM Studio (optional - for local AI models)
      # Use host.docker.internal to reach LM Studio running on host
      - LMSTUDIO_HOST=${LMSTUDIO_HOST:-http://host.docker.internal:1234}

      # Whisper (optional - for voice input)
      - WHISPER_SERVER_URL=${WHISPER_SERVER_URL:-}

    # Add host.docker.internal for Linux (already works on Mac/Windows)
    extra_hosts:
      - "host.docker.internal:host-gateway"

    # Ensure proper permissions for Docker socket
    # You may need to adjust this based on your Docker group ID
    # Run: getent group docker | cut -d: -f3
    # group_add:
    #   - "docker"

    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/api/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s

# =============================================================================
# Volumes for Data Persistence
# =============================================================================
volumes:
  ai-executor-data:
    name: ai-executor-data
  ai-executor-exports:
    name: ai-executor-exports

# =============================================================================
# Networks (optional - if you need custom networking)
# =============================================================================
# networks:
#   ai-executor-network:
#     driver: bridge