docker-compose.yml

services:
  resume-matcher:
    image: ghcr.io/srbhr/resume-matcher
    build: .
    container_name: resume-matcher
    ports:
      - "${PORT:-3000}:${FRONTEND_PORT:-3000}"
    volumes:
      - resume-data:/app/backend/data
    #secrets:
    #  - llm_api_key
    environment:
      # Internal container ports (default: 3000 frontend, 8000 backend)
      # Note: changing BACKEND_PORT requires rebuilding the image with
      # matching BACKEND_ORIGIN for the Next.js proxy rewrites.
      - FRONTEND_PORT=${FRONTEND_PORT:-3000}
      - BACKEND_PORT=${BACKEND_PORT:-8000}
      # domain in reverse proxy
      - FRONTEND_BASE_URL=${FRONTEND_BASE_URL:-http://localhost:3000}
      # Logging configuration: DEBUG, INFO, WARNING, ERROR
      - LOG_LEVEL=${LOG_LEVEL:-INFO}
      #- LOG_LEVEL_FILE=${LOG_LEVEL_FILE:-}
      # Debug level for LiteLLM: DEBUG, INFO, WARNING, ERROR
      - LOG_LLM=${LOG_LLM:-WARNING}
      #- LOG_LLM_FILE=${LOG_LLM_FILE:-}
      # LLM Configuration - configure via Settings UI or set env vars for explicit overrides
      # Supported providers: openai, anthropic, openrouter, gemini, deepseek, ollama
      # Defaults are defined in apps/backend/app/config.py
      - LLM_PROVIDER=${LLM_PROVIDER:-openai}
      - LLM_MODEL=${LLM_MODEL:-}
      # Optional Docker Secret file path (same behavior as Postgres-style *_FILE)
      #- LLM_API_KEY_FILE=${LLM_API_KEY_FILE:-/run/secrets/llm_api_key}
      - LLM_API_KEY=${LLM_API_KEY:-}
      # For Ollama running on host machine, use host.docker.internal:
      # - LLM_API_BASE=http://host.docker.internal:11434
      - LLM_API_BASE=${LLM_API_BASE:-}
    restart: unless-stopped

#secrets:
#  llm_api_key:
#    file: ./secrets/llm_api_key

volumes:
  resume-data:
    driver: local