docker-compose.yml

version: "3.9"
services:
  # Optional: run Ollama inside Docker (disabled by default).
  # Enable with: docker compose --profile with-ollama up
  ollama:
    profiles: ["with-ollama"]
    image: ollama/ollama:latest
    container_name: tree-evaluator-ollama
    ports:
      - "11434:11434"
    volumes:
      - ollama:/root/.ollama
    healthcheck:
      test: ["CMD", "ollama", "list"]
      interval: 10s
      timeout: 5s
      retries: 10

  api:
    build: .
    container_name: tree-evaluator-api
    environment:
      - APP_ENV=development
      - APP_NAME=Tree Evaluator API
      - APP_VERSION=0.1.0
    ports:
      - "8000:8000"
    volumes:
      - ./app:/app/app:ro
    command: uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

  streamlit:
    build:
      context: .
      dockerfile: Dockerfile.streamlit
    container_name: tree-evaluator-streamlit
    ports:
      - "8501:8501"
    volumes:
      - ./streamlit_app:/app/streamlit_app:ro
      - ./app:/app/app:ro
      - ./dataset:/app/dataset:ro
      - chat_data:/app/data
    environment:
      - PYTHONPATH=/app
      - CHAT_DB_PATH=/app/data/chat_index.db
      # LLM provider selection: openai | ollama
      - LLM_PROVIDER=${LLM_PROVIDER:-openai}
      # Ollama settings (used when LLM_PROVIDER=ollama)
      # Default points to host Ollama (Docker Desktop): http://host.docker.internal:11434
      # If you enable the ollama service profile, set OLLAMA_BASE_URL=http://ollama:11434
      - OLLAMA_BASE_URL=${OLLAMA_BASE_URL:-http://host.docker.internal:11434}
      - OLLAMA_CHAT_MODEL=${OLLAMA_CHAT_MODEL:-qwen2.5:7b-instruct}
      - OLLAMA_EMBEDDING_MODEL=${OLLAMA_EMBEDDING_MODEL:-nomic-embed-text}
      # OpenAI API Key (opzionale, può essere inserita dall'UI)
      # - OPENAI_API_KEY=${OPENAI_API_KEY}
    env_file:
      - .env
    command: streamlit run streamlit_app/app.py --server.port 8501 --server.address 0.0.0.0

volumes:
  chat_data:
    driver: local
  ollama:
    driver: local