from functools import lru_cache
from pathlib import Path

from pydantic_settings import BaseSettings, SettingsConfigDict


class Settings(BaseSettings):
    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")

    # 서버 바인딩
    host: str = "0.0.0.0"
    port: int = 8000
    log_level: str = "info"        # debug / info / warning / error
    reload: bool = False           # 개발용 자동 재시작

    engine_version: str = "o2o-plagiarism-2.0.0-pdf-v1.2"
    reference_corpus_dir: str = "./data/reference"
    taxonomy_dir: str = "./data/taxonomy"
    autobiography_patterns_path: str = "./data/autobiography/common_patterns.txt"

    # PDF VII-4 권장: 정밀도 우선 보수적 임계값
    similarity_threshold: float = 0.85

    # KoSimCSE / KoSBERT (PDF VII-3 권장) - 한국어 오픈소스 임베딩
    use_kosimcse: bool = True
    kosimcse_model: str = "BM-K/KoSimCSE-roberta-multitask"
    kosimcse_max_length: int = 512

    # OpenAI (옵션 - 자체 모델 없을 때 폴백)
    openai_api_key: str = ""
    openai_extraction_model: str = "gpt-4o-mini"
    openai_embedding_model: str = "text-embedding-3-small"
    use_llm_extractor: bool = False
    use_embedding_similarity: bool = False

    # 삼중 유사도 가중치 (실측 기반)
    weight_text_sim: float = 0.30
    weight_lemma_sim: float = 0.45
    weight_char_sim: float = 0.15
    weight_motif_sim: float = 0.10

    # PDF VII-3 캐스케이딩
    use_lsh_filter: bool = True
    lsh_threshold: float = 0.3      # 1차 필터는 느슨하게 (재현율 우선)
    lsh_top_k: int = 50

    # PDF VII-4 자서전 모드
    autobiography_mode: bool = True
    enable_entity_masking: bool = True

    @property
    def corpus_path(self) -> Path:
        return Path(self.reference_corpus_dir).resolve()

    @property
    def taxonomy_path(self) -> Path:
        return Path(self.taxonomy_dir).resolve()

    @property
    def has_openai(self) -> bool:
        return bool(self.openai_api_key.strip())


@lru_cache
def get_settings() -> Settings:
    return Settings()