hyungi
5125f82d4a
GPU = RAG context provider, Mac mini = LLM 가공 공장.
GPU 측 변경:
- app/api/internal_study.py: GET /internal/study/explanation-context/{qid}
Bearer auth, gather_explanation_context + _render_envelope_prompt 재호출.
204=evidence missing, 410=deleted/ready.
- app/workers/study_queue_consumer.py: settings.study_explanation_enabled
false 시 explanation 분기 skip (status/attempts 미변경, pending 유지 → Mac mini 흡수).
- app/core/config.py: study_explanation_enabled + internal_worker_token 2 setting.
- app/main.py: internal_study_router include (prefix /internal/study).
- docker-compose.yml: fastapi ports → 100.110.63.63:8000 Tailscale bind,
STUDY_EXPLANATION_ENABLED + INTERNAL_WORKER_TOKEN env 추가.
Mac mini 측: ~/derived-worker/ (별도 push 0, 어제 작성).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
241 lines
7.3 KiB
YAML
241 lines
7.3 KiB
YAML
services:
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postgres:
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image: pgvector/pgvector:pg16
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volumes:
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- pgdata:/var/lib/postgresql/data
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- ./migrations:/docker-entrypoint-initdb.d
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environment:
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POSTGRES_DB: pkm
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POSTGRES_USER: pkm
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POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
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ports:
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- "100.110.63.63:15432:5432"
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healthcheck:
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test: ["CMD-SHELL", "pg_isready -U pkm"]
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interval: 5s
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timeout: 5s
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retries: 5
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restart: unless-stopped
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kordoc-service:
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build: ./services/kordoc
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ports:
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- "127.0.0.1:3100:3100"
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volumes:
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- ${NAS_NFS_PATH:-/mnt/nas/Document_Server}:/documents:ro
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mem_limit: 4g
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memswap_limit: 4g
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healthcheck:
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test: ["CMD", "node", "-e", "fetch('http://localhost:3100/health').then(r=>{process.exit(r.ok?0:1)}).catch(()=>process.exit(1))"]
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interval: 10s
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timeout: 5s
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retries: 3
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restart: unless-stopped
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ocr-service:
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build: ./services/ocr
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expose:
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- "3200"
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volumes:
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- ${NAS_NFS_PATH:-/mnt/nas/Document_Server}:/documents:ro
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- ocr_models:/root/.cache
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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healthcheck:
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test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:3200/health')"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 180s
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restart: unless-stopped
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# Phase 1B (2026-05-01): PDF → markdown 변환. ocr-service 와 별도 컨테이너 (deps 충돌 회피).
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marker-service:
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build: ./services/marker
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ports:
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- "127.0.0.1:3300:3300"
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expose:
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- "3300"
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environment:
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- HF_HOME=/models/huggingface
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- TORCH_HOME=/models/torch
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volumes:
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- ${NAS_NFS_PATH:-/mnt/nas/Document_Server}:/documents:ro
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- marker_models:/models
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:3300/ready"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 300s
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restart: unless-stopped
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stt-service:
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# 2026-05-08 (D9 Track B revised): GPU is canonical STT owner.
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# 정책: Mac mini = Gemma 26B 전용 우선이므로 STT/Whisper 는 호출량 무관 GPU 서버 소유.
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# 이전 "Mac mini 이전본" 주석은 trace 오인 기반이었고 본 revised 결정으로 폐기.
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# fastapi 의 STT_ENDPOINT 는 `http://stt-service:3300` (compose 내부 DNS) 사용.
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build: ./services/stt
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expose:
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- "3300"
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volumes:
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- ${NAS_NFS_PATH:-/mnt/nas/Document_Server}:/documents:ro
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- stt_models:/root/.cache
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environment:
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- WHISPER_MODEL=${WHISPER_MODEL:-large-v3}
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- WHISPER_DEVICE=${WHISPER_DEVICE:-cuda}
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- WHISPER_COMPUTE_TYPE=${WHISPER_COMPUTE_TYPE:-float16}
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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healthcheck:
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# /ready: CUDA 디바이스 + 모델 적재 둘 다 확인. ready=true 만 healthy 처리.
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# /health 는 단순 liveness 라 모델 미적재 상태도 healthy 로 잡혀 운영 신호로 부적합.
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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('ready') else 1)"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 300s
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restart: unless-stopped
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ollama:
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image: ollama/ollama
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volumes:
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- ollama_data:/root/.ollama
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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ports:
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- "127.0.0.1:11434:11434"
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restart: unless-stopped
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# Phase 1.3: bge-reranker-v2-m3 (TEI) — internal only, fastapi에서 reranker:80으로 호출
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# fastapi가 depends_on 안 함 → 단독 시작 가능, 없어도 fastapi 동작 (rerank=false fallback)
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reranker:
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image: ghcr.io/huggingface/text-embeddings-inference:1.7
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container_name: hyungi_document_server-reranker-1
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expose:
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- "80"
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environment:
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- MODEL_ID=BAAI/bge-reranker-v2-m3
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- MAX_BATCH_TOKENS=8192
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- MAX_CONCURRENT_REQUESTS=4
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volumes:
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- reranker_cache:/data
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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healthcheck:
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test: ["CMD", "curl", "-fsS", "http://localhost/health"]
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interval: 30s
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timeout: 5s
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retries: 3
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start_period: 120s
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restart: unless-stopped
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ai-gateway:
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build: ./gpu-server/services/ai-gateway
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ports:
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- "127.0.0.1:8081:8080"
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environment:
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- PRIMARY_ENDPOINT=http://100.76.254.116:8801/v1/chat/completions
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- FALLBACK_ENDPOINT=http://ollama:11434/v1/chat/completions
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- CLAUDE_API_KEY=${CLAUDE_API_KEY:-}
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- DAILY_BUDGET_USD=${DAILY_BUDGET_USD:-5.00}
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depends_on:
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- ollama
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restart: unless-stopped
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fastapi:
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build: ./app
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ports:
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- "100.110.63.63:8000:8000"
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volumes:
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- ${NAS_NFS_PATH:-/mnt/nas/Document_Server}:/documents
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- ./config.yaml:/app/config.yaml:ro
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- ./domain_policy.yaml:/app/domain_policy.yaml:ro
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- ./scripts:/app/scripts:ro
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- ./logs:/app/logs
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- ./migrations:/app/migrations:ro
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depends_on:
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postgres:
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condition: service_healthy
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kordoc-service:
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condition: service_healthy
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marker-service:
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condition: service_healthy
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env_file:
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- credentials.env
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environment:
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- DATABASE_URL=postgresql+asyncpg://pkm:${POSTGRES_PASSWORD}@postgres:5432/pkm
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- KORDOC_ENDPOINT=http://kordoc-service:3100
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- OCR_ENDPOINT=http://ocr-service:3200
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- MARKER_ENDPOINT=http://marker-service:3300
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- MARKER_CONTAINER_PATH_PREFIX=/documents
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# 2026-05-08 (D9 Track B revised): GPU stt-service 정식 승격, 내부 DNS 사용.
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- STT_ENDPOINT=http://stt-service:3300
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# KGS Code 등 외부 학습 자료 추가 스캔 경로 (host .env 에서 주입). 빈 값이면 비활성.
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- ADDITIONAL_WATCH_TARGETS=${ADDITIONAL_WATCH_TARGETS:-}
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# PR-MacMini-Derived-Worker-1
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- STUDY_EXPLANATION_ENABLED=${STUDY_EXPLANATION_ENABLED:-true}
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- INTERNAL_WORKER_TOKEN=${INTERNAL_WORKER_TOKEN}
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# Voice Memo PoC v1 — bot 계정 한정 long-expiry access token. default false → 일반 운영 영향 0.
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# 활성화: host .env 에 VOICE_MEMO_BOT_TOKEN_ENABLED=true. plan: rosy-launching-otter.md
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- VOICE_MEMO_BOT_TOKEN_ENABLED=${VOICE_MEMO_BOT_TOKEN_ENABLED:-false}
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- VOICE_MEMO_BOT_USERNAME=${VOICE_MEMO_BOT_USERNAME:-voice-memo-bot}
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- VOICE_MEMO_BOT_TOKEN_EXPIRE_DAYS=${VOICE_MEMO_BOT_TOKEN_EXPIRE_DAYS:-365}
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restart: unless-stopped
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frontend:
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build: ./frontend
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ports:
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- "127.0.0.1:3000:3000"
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depends_on:
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- fastapi
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restart: unless-stopped
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caddy:
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image: caddy:2
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ports:
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- "8080:80"
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volumes:
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- ./Caddyfile:/etc/caddy/Caddyfile
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- caddy_data:/data
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depends_on:
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- fastapi
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- frontend
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restart: unless-stopped
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volumes:
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pgdata:
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caddy_data:
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ollama_data:
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reranker_cache:
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ocr_models:
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stt_models:
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marker_models:
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