43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
from pydantic_settings import BaseSettings
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from pathlib import Path
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BASE_DIR = Path(__file__).resolve().parent.parent.parent
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class Settings(BaseSettings):
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database_url: str = f"sqlite+aiosqlite:///{BASE_DIR / 'data' / 'bookmate.db'}"
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upload_dir: str = str(BASE_DIR / "uploads")
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data_dir: str = str(BASE_DIR / "data")
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api_host: str = "0.0.0.0"
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api_port: int = 8000
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# ===== 向量检索 & 重排（OpenAI 兼容 API，如硅基流动 SiliconFlow）=====
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# embedding_base_url: 支持 OpenAI 兼容 /embeddings 的端点前缀
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embedding_base_url: str = "https://api.siliconflow.cn/v1"
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embedding_api_key: str = "" # 留空则自动降级为纯关键词检索
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embedding_model: str = "BAAI/bge-m3" # 1024 维
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# rerank: 硅基流动使用 /v1/rerank，OpenAI 不提供；留空则跳过重排
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rerank_base_url: str = "https://api.siliconflow.cn/v1"
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rerank_api_key: str = ""
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rerank_model: str = "BAAI/bge-reranker-v2-m3"
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embedding_batch_size: int = 64
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@property
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def upload_path(self) -> Path:
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p = Path(self.upload_dir)
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p.mkdir(parents=True, exist_ok=True)
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return p
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@property
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def data_path(self) -> Path:
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p = Path(self.data_dir)
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p.mkdir(parents=True, exist_ok=True)
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return p
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class Config:
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env_file = ".env"
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env_file_encoding = "utf-8"
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extra = "ignore"
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settings = Settings() |