from pydantic_settings import BaseSettings, SettingsConfigDict


class Settings(BaseSettings):
    app_name: str = "Labor Arbitration Backend"
    # 开发期：使用本机 MySQL，避免依赖 Docker/PostgreSQL
    # 示例：mysql+pymysql://root:password@127.0.0.1:3306/graduation?charset=utf8mb4
    database_url: str = "mysql+pymysql://root:password@127.0.0.1:3306/graduation?charset=utf8mb4"

    # 下面这些在你以后接入向量库和独立 NLP 服务时再启用
    # 无 Docker / 无 Qdrant 时保持 False，相似案件走 MySQL 内简易文本相似度
    use_vector_store: bool = False
    qdrant_url: str = "http://localhost:6333"
    qdrant_collection: str = "labor_case_vectors"
    embedding_model_name: str = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
    nlp_service_url: str = "http://localhost:8001/extract"
    use_remote_nlp_service: bool = False

    # Ollama（可选）：启用后使用 app/anj.py 中同一 Ollama 模型增强「仲裁请求 claims」与「案由模板扁平要素」
    # （extract_dispute_template_fields），规则抽取仍先执行，LLM 结果覆盖非空字段。
    use_ollama: bool = False
    ollama_base_url: str = "http://127.0.0.1:11434"
    ollama_model_name: str = "qwen2.5:3b"

    # Extractor mode: "rules" | "bert" | "ollama" | "hybrid"
    extractor_mode: str = "rules"
    # BERT model service URL (nlp-service)
    bert_model_service_url: str = "http://localhost:8001/extract"

    model_config = SettingsConfigDict(env_file=".env", extra="ignore")


settings = Settings()