40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
import os
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from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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DB_USER: str = os.getenv("DB_USER", "postgres")
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DB_PASS: str = os.getenv("DB_PASS", "postgres")
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DB_NAME: str = os.getenv("DB_NAME", "test_db")
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DB_HOST: str = os.getenv("DB_HOST", "db")
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DB_PORT: str = os.getenv("DB_PORT", "5432")
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DEFAULT_VOSK_MODEL: str = os.getenv(
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"DEFAULT_VOSK_MODEL", "models/vosk-model-small-ru-0.22"
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)
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SUBTITLE_LANGS: str = os.getenv("SUBTITLE_LANGS", "ru,en")
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# External LLM (OpenAI-compatible). The LLM itself is NOT hosted in this container —
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# point at any provider/proxy/local server that speaks /v1/chat/completions.
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LLM_BASE_URL: str = os.getenv("LLM_BASE_URL", "")
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LLM_API_KEY: str = os.getenv("LLM_API_KEY", "")
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LLM_MODEL: str = os.getenv("LLM_MODEL", "")
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LLM_TIMEOUT: int = int(os.getenv("LLM_TIMEOUT", "120"))
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# reasoning-модели тратят max_tokens и на размышления — 1000 им мало
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LLM_MAX_TOKENS: int = int(os.getenv("LLM_MAX_TOKENS", "8000"))
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LLM_TEMPERATURE: float = float(os.getenv("LLM_TEMPERATURE", "0.3"))
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@property
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def database_url(self) -> str:
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return (
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f"postgresql+asyncpg://{self.DB_USER}:{self.DB_PASS}"
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f"@{self.DB_HOST}:{self.DB_PORT}/{self.DB_NAME}"
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)
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@property
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def subtitle_langs_list(self) -> list[str]:
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return [x.strip() for x in self.SUBTITLE_LANGS.split(",") if x.strip()]
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settings = Settings()
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