OneRAG/docker-compose.yml at main · notadev-iamaura/OneRAG · GitHub

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# OneRAG 통합 Docker Compose
# Start: make start 명령어로 실행
#
# 포함 서비스:
# - weaviate: 벡터 데이터베이스 (하이브리드 검색 지원)
# - api: RAG API 서버 (FastAPI)

services:
  # Weaviate 벡터 데이터베이스
  weaviate:
    image: cr.weaviate.io/semitechnologies/weaviate:1.27.8
    container_name: rag-weaviate
    restart: unless-stopped
    ports:
      - "8080:8080"   # HTTP API
      - "50051:50051" # gRPC (Python client v4)
    environment:
      # 단일 노드 클러스터 설정 (필수)
      CLUSTER_HOSTNAME: 'node1'
      RAFT_BOOTSTRAP_EXPECT: 1
      # 한국어 토크나이저 활성화
      ENABLE_TOKENIZER_KAGOME_KR: 'true'
      # 데이터 영속화
      PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
      # 인증 (로컬 개발 - 비활성화)
      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
      # 기본 설정
      QUERY_DEFAULTS_LIMIT: 25
      LOG_LEVEL: 'info'
    volumes:
      - weaviate_data:/var/lib/weaviate
    healthcheck:
      test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://localhost:8080/v1/.well-known/ready"]
      interval: 10s
      timeout: 5s
      retries: 5
      start_period: 30s

  # RAG API 서버
  api:
    build:
      context: .
      dockerfile: Dockerfile
      args:
        INSTALL_LOCAL_EMBEDDING_DEPS: "${INSTALL_LOCAL_EMBEDDING_DEPS:-false}"
        PRELOAD_LOCAL_EMBEDDING_MODEL: "${PRELOAD_LOCAL_EMBEDDING_MODEL:-false}"
        INSTALL_PLAYWRIGHT_BROWSERS: "${INSTALL_PLAYWRIGHT_BROWSERS:-false}"
    container_name: rag-api
    restart: unless-stopped
    ports:
      - "8000:8000"
    env_file:
      - path: .env
        required: false
    environment:
      # Weaviate 연결 (컨테이너 네트워크 사용)
      WEAVIATE_URL: "http://weaviate:8080"
      WEAVIATE_GRPC_HOST: "weaviate"
      WEAVIATE_GRPC_PORT: "50051"
      # 개발 환경
      ENVIRONMENT: "development"
      RETRIEVAL_STARTUP_POLICY: "${RETRIEVAL_STARTUP_POLICY:-degraded}"
      # 기본은 가벼운 원격 임베딩 provider. 로컬 임베딩은 INSTALL_LOCAL_EMBEDDING_DEPS=true와 함께 opt-in.
      EMBEDDINGS_PROVIDER: "${EMBEDDINGS_PROVIDER:-openrouter}"
      # LLM 설정 (Quickstart용 - Google Gemini 무료 티어)
      LLM_PROVIDER: "${LLM_PROVIDER:-google}"
      LLM_MODEL: "${LLM_MODEL:-gemini-2.0-flash}"
      GENERATION_PROVIDER: "${GENERATION_PROVIDER:-google}"
    depends_on:
      weaviate:
        condition: service_healthy
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/ready"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 60s

  # React 프론트엔드 (프로필 선택 실행)
  # 실행: docker compose --profile fullstack up
  frontend:
    build:
      context: ./frontend
      dockerfile: Dockerfile
    container_name: rag-frontend
    restart: unless-stopped
    ports:
      - "5173:80"
    environment:
      # Browser runtime config for local fullstack quickstart
      PORT: "80"
      VITE_API_BASE_URL: "http://localhost:8000"
      VITE_WS_BASE_URL: "ws://localhost:8000"
      VITE_ACCESS_CODE: "${VITE_ACCESS_CODE:-1127}"
    depends_on:
      api:
        condition: service_healthy
    profiles:
      - fullstack
    healthcheck:
      test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://127.0.0.1:80/health"]
      interval: 30s
      timeout: 5s
      retries: 3
      start_period: 10s

volumes:
  weaviate_data:
    name: onerag_weaviate_data