GroundCortex/docker-compose.yml at master · huss-mo/GroundCortex · GitHub

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services:
  # ---------------------------------------------------------------------------
  # groundcortex - memory consolidation engine
  #
  # Usage:
  #   cp .env.example .env      # configure model, ports, source paths, etc.
  #   docker compose up -d
  #
  # MCP server:       http://localhost:4343
  # Inference server: http://localhost:4344  (OpenAI-compatible)
  #
  # First run: the base model (~4 GB for Qwen3.5-2B) is downloaded
  # from Hugging Face into ./data/models/. Subsequent starts reuse the cached weights.
  # ---------------------------------------------------------------------------
  groundcortex:
    build:
      context: .
      # GPU build: uncomment to pull CUDA-enabled PyTorch wheels.
      # Requires NVIDIA Container Toolkit on the host (nvidia-container-toolkit).
      # After changing args, rebuild with: docker compose build
      #
      # args:
      #   TORCH_INDEX: https://download.pytorch.org/whl/cu124
    image: groundcortex:latest
    restart: unless-stopped
    ports:
      - "4343:4343"   # MCP server
      - "4344:4344"   # Inference server (OpenAI-compatible)
    volumes:
      # All runtime data (model weights, LoRA adapters, SQLite DB) under one
      # host directory. git-ignored and docker-ignored. Docker creates ./data/
      # on the host automatically if it does not exist.
      - ./data:/app/data

      # Source files to ingest - uncomment and adjust if your notes directory
      # is on the host and not accessible over HTTP. The path on the right must
      # match what you set in GROUNDCORTEX_SOURCE_PATHS.
      #
      # - /path/to/your/notes:/notes:ro
    env_file:
      # Copy .env.example to .env and fill in your values.
      # The env_file is optional - all settings have sensible defaults and
      # can also be passed as environment: entries below.
      - path: .env
        required: false
    environment:
      GROUNDCORTEX_BUFFER_DB: /app/data/groundcortex.db
      GROUNDCORTEX_OUTPUT_DIR: /app/data/adapters
      HF_HUB_CACHE: /app/data/models

      # All other settings (model name, ports, API keys, source paths, cron,
      # MCP exposed tools) can be set in .env or as additional entries here.

      # GPU: uncomment to reserve an NVIDIA GPU for this service.
      # Requires NVIDIA Container Toolkit on the host.
      #
      # deploy:
      #   resources:
      #     reservations:
      #       devices:
      #         - driver: nvidia
      #           count: 1
      #           capabilities: [gpu]