hyungi_document_server/docker-compose.yml

services:
  postgres:
    image: pgvector/pgvector:pg16
    volumes:
      - pgdata:/var/lib/postgresql/data
      - ./migrations:/docker-entrypoint-initdb.d
    environment:
      POSTGRES_DB: pkm
      POSTGRES_USER: pkm
      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
    ports:
      - "100.110.63.63:15432:5432"
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U pkm"]
      interval: 5s
      timeout: 5s
      retries: 5
    restart: unless-stopped

  kordoc-service:
    build: ./services/kordoc
    ports:
      - "127.0.0.1:3100:3100"
    volumes:
      - ${NAS_NFS_PATH:-/mnt/nas/Document_Server}:/documents:ro
    mem_limit: 4g
    memswap_limit: 4g
    healthcheck:
      test: ["CMD", "node", "-e", "fetch('http://localhost:3100/health').then(r=>{process.exit(r.ok?0:1)}).catch(()=>process.exit(1))"]
      interval: 10s
      timeout: 5s
      retries: 3
    restart: unless-stopped

  ocr-service:
    build: ./services/ocr
    expose:
      - "3200"
    volumes:
      - ${NAS_NFS_PATH:-/mnt/nas/Document_Server}:/documents:ro
      - ocr_models:/root/.cache
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:3200/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 180s
    restart: unless-stopped

  # Phase 1B (2026-05-01): PDF → markdown 변환. ocr-service 와 별도 컨테이너 (deps 충돌 회피).
  marker-service:
    build: ./services/marker
    ports:
      - "127.0.0.1:3300:3300"
    expose:
      - "3300"
    environment:
      - HF_HOME=/models/huggingface
      - TORCH_HOME=/models/torch
      # D-1 (crawl-24x7): idle-unload 전환 — 영구 점유(~3.5GB) 해제가 90% 봉투의 전제.
      # /ready 는 idle 에서도 200 (fastapi depends_on service_healthy 유지).
      # 롤백 = MARKER_PRELOAD=1 + MARKER_IDLE_UNLOAD_MINUTES=0.
      - MARKER_PRELOAD=0
      - MARKER_IDLE_UNLOAD_MINUTES=${MARKER_IDLE_UNLOAD_MINUTES:-30}
    volumes:
      - ${NAS_NFS_PATH:-/mnt/nas/Document_Server}:/documents:ro
      - marker_models:/models
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:3300/ready"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 300s
    restart: unless-stopped

  stt-service:
    # 2026-05-08 (D9 Track B revised): GPU is canonical STT owner.
    # 정책: Mac mini = Gemma 26B 전용 우선이므로 STT/Whisper 는 호출량 무관 GPU 서버 소유.
    # 이전 "Mac mini 이전본" 주석은 trace 오인 기반이었고 본 revised 결정으로 폐기.
    # fastapi 의 STT_ENDPOINT 는 `http://stt-service:3300` (compose 내부 DNS) 사용.
    build: ./services/stt
    expose:
      - "3300"
    volumes:
      - ${NAS_NFS_PATH:-/mnt/nas/Document_Server}:/documents:ro
      - stt_models:/root/.cache
    environment:
      - WHISPER_MODEL=${WHISPER_MODEL:-large-v3}
      - WHISPER_DEVICE=${WHISPER_DEVICE:-cuda}
      - WHISPER_COMPUTE_TYPE=${WHISPER_COMPUTE_TYPE:-float16}
      # D-1 (crawl-24x7): idle-unload 전환 — 영구 점유(~4GB) 해제가 90% 봉투의 전제.
      # 콜드로드 수초~수십 초는 배치 작업이라 무방 (stt_worker read=1800s 가 흡수).
      # 롤백 = STT_PRELOAD=1 + STT_IDLE_UNLOAD_MINUTES=0.
      - STT_PRELOAD=0
      - STT_IDLE_UNLOAD_MINUTES=${STT_IDLE_UNLOAD_MINUTES:-30}
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    healthcheck:
      # D-1: idle-unload 도입으로 '모델 적재' 는 더 이상 상시 상태가 아님 — cuda 가용성만
      # healthy 기준. 모델 적재 여부는 /ready 의 models_loaded 필드로 관측(정보성).
      test: ["CMD", "python3", "-c", "import json,urllib.request,sys; r=urllib.request.urlopen('http://localhost:3300/ready'); sys.exit(0 if json.load(r).get('cuda') else 1)"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 300s
    restart: unless-stopped

  # ── ollama 서비스 제거 (2026-06-08) ──
  # 정본 ollama = standalone `~/ollama/docker-compose.yml`(container_name: ollama).
  # 그 컨테이너가 hyungi_document_server_default 망(external) + 동일 볼륨
  # hyungi_document_server_ollama_data(external, bge-m3) 부착으로 fastapi 의 `ollama:11434`
  # 임베딩을 이미 서빙(재부팅에도 durable). 본 중복 서비스는 같은 host 127.0.0.1:11434 를
  # 점유 다퉈, 재부팅 후 `docker compose up` 을 'port already allocated' 로 abort →
  # 뒤 의존서비스(caddy·frontend) 미기동 = 웹 outage 유발 → 제거. (ollama_data 볼륨 def 는
  # standalone 이 external 로 참조하므로 아래 volumes: 에 보존.)

  # Phase 1.3: bge-reranker-v2-m3 (TEI) — internal only, fastapi에서 reranker:80으로 호출
  # fastapi가 depends_on 안 함 → 단독 시작 가능, 없어도 fastapi 동작 (rerank=false fallback)
  reranker:
    image: ghcr.io/huggingface/text-embeddings-inference:1.7
    container_name: hyungi_document_server-reranker-1
    expose:
      - "80"
    environment:
      - MODEL_ID=BAAI/bge-reranker-v2-m3
      # PR-2Q-Rerank-Payload-Fix (2026-05-24): 2 env 변경 — 413 root cause 분리.
      #   (a) MAX_BATCH_TOKENS 8192 → 16384: 각 batch 의 token sum 한도
      #   (b) MAX_CLIENT_BATCH_SIZE 32 → 64: 1 request 안 entries 수 한도 (default 32).
      #       multi-query cap 60 chunks (×2 chunks_per_doc dedup) 가 batch entries 54~60
      #       → 32 한도 초과 → 413. 64 로 늘림.
      # GPU VRAM free 6199MiB 충분. baseline path (MAX_RERANK_INPUT=200) 영향 0.
      - MAX_BATCH_TOKENS=16384
      - MAX_CLIENT_BATCH_SIZE=256  # 2026-06-18 fix: 64→256, MAX_RERANK_INPUT=200 커버 (batch>64 ERROR=RRF silent fallback 해소; MAX_BATCH_TOKENS가 VRAM 상한이라 entries 증가는 VRAM 무관)
      - MAX_CONCURRENT_REQUESTS=4
    volumes:
      - reranker_cache:/data
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    healthcheck:
      test: ["CMD", "curl", "-fsS", "http://localhost/health"]
      interval: 30s
      timeout: 5s
      retries: 3
      start_period: 120s
    restart: unless-stopped

  fastapi:
    build: ./app
    ports:
      - "100.110.63.63:8000:8000"
    volumes:
      - ${NAS_NFS_PATH:-/mnt/nas/Document_Server}:/documents
      - ./config.yaml:/app/config.yaml:ro
      - ./domain_policy.yaml:/app/domain_policy.yaml:ro
      - ./scripts:/app/scripts:ro
      - ./logs:/app/logs
      - ./migrations:/app/migrations:ro
    depends_on:
      postgres:
        condition: service_healthy
      kordoc-service:
        condition: service_healthy
      marker-service:
        condition: service_healthy
    env_file:
      - credentials.env
    environment:
      - DATABASE_URL=postgresql+asyncpg://pkm:${POSTGRES_PASSWORD}@postgres:5432/pkm
      - KORDOC_ENDPOINT=http://kordoc-service:3100
      - OCR_ENDPOINT=http://ocr-service:3200
      - MARKER_ENDPOINT=http://marker-service:3300
      - MARKER_CONTAINER_PATH_PREFIX=/documents
      # 2026-05-08 (D9 Track B revised): GPU stt-service 정식 승격, 내부 DNS 사용.
      - STT_ENDPOINT=http://stt-service:3300
      # KGS Code 등 외부 학습 자료 추가 스캔 경로 (host .env 에서 주입). 빈 값이면 비활성.
      - ADDITIONAL_WATCH_TARGETS=${ADDITIONAL_WATCH_TARGETS:-}
      # PR-MacMini-Derived-Worker-1
      - STUDY_EXPLANATION_ENABLED=${STUDY_EXPLANATION_ENABLED:-true}
      - INTERNAL_WORKER_TOKEN=${INTERNAL_WORKER_TOKEN}
      # Voice Memo PoC v1 — bot 계정 한정 long-expiry access token. default false → 일반 운영 영향 0.
      # 활성화: host .env 에 VOICE_MEMO_BOT_TOKEN_ENABLED=true. plan: rosy-launching-otter.md
      - VOICE_MEMO_BOT_TOKEN_ENABLED=${VOICE_MEMO_BOT_TOKEN_ENABLED:-false}
      - VOICE_MEMO_BOT_USERNAME=${VOICE_MEMO_BOT_USERNAME:-voice-memo-bot}
      - VOICE_MEMO_BOT_TOKEN_EXPIRE_DAYS=${VOICE_MEMO_BOT_TOKEN_EXPIRE_DAYS:-365}
      # PR-Notebook-Client-1 — notebook-client-bot long-expiry token (laptop-worker-bot wrapper 재활용,
      # 1B Pilot dormant 후 username rename). default false → 운영 영향 0.
      - LAPTOP_WORKER_BOT_TOKEN_ENABLED=${LAPTOP_WORKER_BOT_TOKEN_ENABLED:-false}
      - LAPTOP_WORKER_BOT_USERNAME=${LAPTOP_WORKER_BOT_USERNAME:-laptop-worker-bot}
      - LAPTOP_WORKER_BOT_TOKEN_EXPIRE_DAYS=${LAPTOP_WORKER_BOT_TOKEN_EXPIRE_DAYS:-365}
      # PR-2 of DS AI routing policy (2026-05-23) — backends dispatcher via llm-router.
      # router_url default points at Mac mini Tailscale interface :8890 (PR-1).
      # DS_BACKENDS_VIA_ROUTER=false 로 legacy 직접 호출 path 즉시 복귀.
      - LLM_ROUTER_URL=${LLM_ROUTER_URL:-http://100.76.254.116:8890}
      - DS_BACKENDS_VIA_ROUTER=${DS_BACKENDS_VIA_ROUTER:-true}
    restart: unless-stopped

  frontend:
    build: ./frontend
    ports:
      - "127.0.0.1:3000:3000"
    depends_on:
      - fastapi
    restart: unless-stopped

  # crawl-24x7 A-8 1차: 전 소스 헬스 패널 — 내부 전용 (읽기 전용 SELECT 만).
  # '내부 전용' 성립 구현 = 별도 바인딩뿐 (r4 결정): Tailscale 인터페이스에만 publish.
  # 기존 SvelteKit 라우트(vhost=Host 헤더 검사=앱 가드 환원)나 프록시 경로 차단(경로 가드
  # 회귀)으로 옮기지 말 것. caddy/home-caddy 라우트 추가 금지. fastapi/postgres 바인딩 선례.
  crawl-health:
    build: ./services/crawl-health
    ports:
      - "100.110.63.63:8765:8765"
    environment:
      - CRAWL_HEALTH_DSN=postgresql://pkm:${POSTGRES_PASSWORD}@postgres:5432/pkm
    depends_on:
      postgres:
        condition: service_healthy
    restart: unless-stopped

  # crawl-24x7 B-3: 구독 세션 Playwright fetch 격리 — internal-only (host 포트·caddy 라우트 금지).
  # 브라우저 hang/크래시가 fastapi APScheduler 를 잠식하지 않게 별도 컨테이너 + mem cap.
  # 세션 파일(쿠키=credential 등가물)은 repo 밖 호스트 경로 ro mount (600, gitignore 무관 영역).
  playwright-fetcher:
    build: ./services/playwright-fetcher
    volumes:
      - /home/hyungi/.local/share/crawl-auth:/auth:ro
    mem_limit: 2g
    restart: unless-stopped

  caddy:
    image: caddy:2
    ports:
      - "8080:80"
    volumes:
      - ./Caddyfile:/etc/caddy/Caddyfile
      - caddy_data:/data
    depends_on:
      - fastapi
      - frontend
    restart: unless-stopped

volumes:
  pgdata:
  caddy_data:
  ollama_data:
  reranker_cache:
  ocr_models:
  stt_models:
  marker_models: