aibridze/document_intelligence

1from pydantic_settings import BaseSettings2from pathlib import Path3from functools import lru_cache4 5 6class Settings(BaseSettings):7    # API Keys8    GOOGLE_API_KEY: str = ""9 10    # Models11    VISION_MODEL: str = "gemini-2.5-flash"12    EMBEDDING_MODEL: str = "models/gemini-embedding-001"13 14    # Paths15    DATA_DIR: str = "data"16    UPLOAD_DIR: str = "uploads"17    PROCESSED_DIR: str = "processed"18    VECTORSTORE_DIR: str = "vectorstore"19 20    # Server21    HOST: str = "0.0.0.0"22    PORT: int = 800023 24    class Config:25        env_file = ".env"26        extra = "ignore"27 28    def get_data_path(self) -> Path:29        p = Path(self.DATA_DIR)30        p.mkdir(parents=True, exist_ok=True)31        return p32 33    def get_upload_path(self) -> Path:34        p = self.get_data_path() / self.UPLOAD_DIR35        p.mkdir(parents=True, exist_ok=True)36        return p37 38    def get_processed_path(self) -> Path:39        p = self.get_data_path() / self.PROCESSED_DIR40        p.mkdir(parents=True, exist_ok=True)41        return p42 43    def get_vectorstore_path(self) -> Path:44        p = self.get_data_path() / self.VECTORSTORE_DIR45        p.mkdir(parents=True, exist_ok=True)46        return p47 48 49@lru_cache()50def get_settings() -> Settings:51    return Settings()