68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
from functools import lru_cache
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from pathlib import Path
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
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# 서버 바인딩
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host: str = "0.0.0.0"
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port: int = 8000
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log_level: str = "info" # debug / info / warning / error
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reload: bool = False # 개발용 자동 재시작
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root_path: str = "" # 리버스 프록시 sub-path (예: /plagiarism)
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engine_version: str = "o2o-plagiarism-2.0.0-pdf-v1.2"
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reference_corpus_dir: str = "./data/reference"
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taxonomy_dir: str = "./data/taxonomy"
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autobiography_patterns_path: str = "./data/autobiography/common_patterns.txt"
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# PDF VII-4 권장: 정밀도 우선 보수적 임계값
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similarity_threshold: float = 0.85
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# KoSimCSE / KoSBERT (PDF VII-3 권장) - 한국어 오픈소스 임베딩
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use_kosimcse: bool = True
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kosimcse_model: str = "BM-K/KoSimCSE-roberta-multitask"
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kosimcse_max_length: int = 512
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# OpenAI (옵션 - 자체 모델 없을 때 폴백)
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openai_api_key: str = ""
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openai_extraction_model: str = "gpt-4o-mini"
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openai_embedding_model: str = "text-embedding-3-small"
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use_llm_extractor: bool = False
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use_embedding_similarity: bool = False
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# 삼중 유사도 가중치 (실측 기반)
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weight_text_sim: float = 0.30
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weight_lemma_sim: float = 0.45
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weight_char_sim: float = 0.15
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weight_motif_sim: float = 0.10
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# PDF VII-3 캐스케이딩
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use_lsh_filter: bool = True
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lsh_threshold: float = 0.3 # 1차 필터는 느슨하게 (재현율 우선)
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lsh_top_k: int = 50
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# PDF VII-4 자서전 모드
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autobiography_mode: bool = True
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enable_entity_masking: bool = True
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@property
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def corpus_path(self) -> Path:
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return Path(self.reference_corpus_dir).resolve()
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@property
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def taxonomy_path(self) -> Path:
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return Path(self.taxonomy_dir).resolve()
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@property
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def has_openai(self) -> bool:
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return bool(self.openai_api_key.strip())
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@lru_cache
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def get_settings() -> Settings:
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return Settings()
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