Hyungi Ahn
088966bf78
스캔 PDF/이미지 자동 OCR 트리거 + 결과 품질 검증 + 1회 제한. - extract_meta JSONB 컬럼 추가 (migration 134) ocr_attempted, ocr_reason, ocr_skip_reason, ocr_terminal, ocr_chars - PDF OCR 트리거: total_chars < 300 또는 avg < 80 && total < 3000 - 이미지 자동 OCR: jpg/png/tiff/webp 등 - 품질 차등: 이미지 50자, PDF 200자 또는 페이지당 30자 - 상한: pages > 200 또는 file_size > 150MB → 스킵 - OCR 1회 제한: extract_meta.ocr_attempted로 재시도 방지 - extractor_version은 도구명만 (surya_ocr/pymupdf/kordoc) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
124 lines
3.8 KiB
Python
124 lines
3.8 KiB
Python
"""설정 로딩 — config.yaml + credentials.env"""
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import os
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from pathlib import Path
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import yaml
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from pydantic import BaseModel
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class AIModelConfig(BaseModel):
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endpoint: str
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model: str
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max_tokens: int = 4096
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timeout: int = 60
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daily_budget_usd: float | None = None
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require_explicit_trigger: bool = False
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class AIConfig(BaseModel):
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gateway_endpoint: str
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primary: AIModelConfig
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fallback: AIModelConfig
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premium: AIModelConfig
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embedding: AIModelConfig
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vision: AIModelConfig
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rerank: AIModelConfig
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# Phase 3.5a: exaone classifier (optional — 없으면 score-only gate)
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classifier: AIModelConfig | None = None
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# Phase 3.5b: exaone verifier (optional — 없으면 grounding-only)
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verifier: AIModelConfig | None = None
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class Settings(BaseModel):
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# DB
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database_url: str = ""
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# AI
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ai: AIConfig | None = None
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# NAS
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nas_mount_path: str = "/documents"
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nas_pkm_root: str = "/documents/PKM"
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# 인증
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jwt_secret: str = ""
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totp_secret: str = ""
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# kordoc
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kordoc_endpoint: str = "http://kordoc-service:3100"
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# OCR (Surya)
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ocr_endpoint: str = "http://ocr-service:3200"
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# 분류 체계
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taxonomy: dict = {}
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document_types: list[str] = []
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def load_settings() -> Settings:
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"""config.yaml + 환경변수에서 설정 로딩"""
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# 환경변수 (docker-compose에서 주입)
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database_url = os.getenv("DATABASE_URL", "")
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jwt_secret = os.getenv("JWT_SECRET", "")
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totp_secret = os.getenv("TOTP_SECRET", "")
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kordoc_endpoint = os.getenv("KORDOC_ENDPOINT", "http://kordoc-service:3100")
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ocr_endpoint = os.getenv("OCR_ENDPOINT", "http://ocr-service:3200")
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# config.yaml — Docker 컨테이너 내부(/app/config.yaml) 또는 프로젝트 루트
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config_path = Path("/app/config.yaml")
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if not config_path.exists():
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config_path = Path(__file__).parent.parent.parent / "config.yaml"
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ai_config = None
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nas_mount = "/documents"
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nas_pkm = "/documents/PKM"
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if config_path.exists():
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with open(config_path) as f:
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raw = yaml.safe_load(f)
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if "ai" in raw:
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ai_raw = raw["ai"]
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ai_config = AIConfig(
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gateway_endpoint=ai_raw.get("gateway", {}).get("endpoint", ""),
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primary=AIModelConfig(**ai_raw["models"]["primary"]),
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fallback=AIModelConfig(**ai_raw["models"]["fallback"]),
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premium=AIModelConfig(**ai_raw["models"]["premium"]),
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embedding=AIModelConfig(**ai_raw["models"]["embedding"]),
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vision=AIModelConfig(**ai_raw["models"]["vision"]),
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rerank=AIModelConfig(**ai_raw["models"]["rerank"]),
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classifier=(
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AIModelConfig(**ai_raw["models"]["classifier"])
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if "classifier" in ai_raw.get("models", {})
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else None
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),
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verifier=(
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AIModelConfig(**ai_raw["models"]["verifier"])
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if "verifier" in ai_raw.get("models", {})
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else None
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),
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)
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if "nas" in raw:
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nas_mount = raw["nas"].get("mount_path", nas_mount)
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nas_pkm = raw["nas"].get("pkm_root", nas_pkm)
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taxonomy = raw.get("taxonomy", {}) if config_path.exists() and raw else {}
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document_types = raw.get("document_types", []) if config_path.exists() and raw else []
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return Settings(
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database_url=database_url,
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ai=ai_config,
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nas_mount_path=nas_mount,
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nas_pkm_root=nas_pkm,
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jwt_secret=jwt_secret,
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totp_secret=totp_secret,
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kordoc_endpoint=kordoc_endpoint,
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ocr_endpoint=ocr_endpoint,
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taxonomy=taxonomy,
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document_types=document_types,
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)
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settings = load_settings()
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