agent
88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
"""
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集中读取 .env / 环境变量。
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使用方式：
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from config import settings
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print(settings.llm_model)
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print(settings.has_openai_key)
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"""
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import os
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from dataclasses import dataclass
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from pathlib import Path
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def _load_dotenv(env_path: Path) -> None:
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"""极简 .env 解析，避免引入 python-dotenv 依赖。"""
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if not env_path.is_file():
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return
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for raw_line in env_path.read_text(encoding="utf-8").splitlines():
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line = raw_line.strip()
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if not line or line.startswith("#"):
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continue
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if "=" not in line:
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continue
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key, _, value = line.partition("=")
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key = key.strip()
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value = value.strip().strip('"').strip("'")
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# 已存在则不覆盖（让真实环境变量优先）
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os.environ.setdefault(key, value)
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_ROOT = Path(__file__).resolve().parent
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_load_dotenv(_ROOT / ".env")
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@dataclass(frozen=True)
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class Settings:
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openai_api_key: str
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anthropic_api_key: str
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llm_provider: str
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llm_model: str
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llm_max_tokens: int
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rag_chroma_path: str
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rag_collection_name: str
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rag_embed_model: str
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validation_service_url: str
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log_level: str
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@property
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def has_openai_key(self) -> bool:
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return bool(self.openai_api_key) and self.openai_api_key != "your_openai_api_key_here"
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@property
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def has_anthropic_key(self) -> bool:
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return bool(self.anthropic_api_key) and self.anthropic_api_key != "your_anthropic_api_key_here"
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def _int(name: str, default: int) -> int:
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raw = os.environ.get(name)
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try:
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return int(raw) if raw else default
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except ValueError:
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return default
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settings = Settings(
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openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
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anthropic_api_key=os.environ.get("ANTHROPIC_API_KEY", ""),
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llm_provider=os.environ.get("LLM_PROVIDER", "openai"),
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llm_model=os.environ.get("LLM_MODEL", "gpt-4o-mini"),
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llm_max_tokens=_int("LLM_MAX_TOKENS", 4096),
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rag_chroma_path=os.environ.get("RAG_CHROMA_PATH", "./db/chroma"),
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rag_collection_name=os.environ.get("RAG_COLLECTION_NAME", "jrxml_chunks"),
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rag_embed_model=os.environ.get("RAG_EMBED_MODEL", "sentence-transformers/all-MiniLM-L6-v2"),
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validation_service_url=os.environ.get("VALIDATION_SERVICE_URL", "http://localhost:8001"),
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log_level=os.environ.get("LOG_LEVEL", "INFO"),
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)
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if __name__ == "__main__":
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s = settings
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print(f"provider = {s.llm_provider}")
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print(f"model = {s.llm_model}")
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print(f"max_tok = {s.llm_max_tokens}")
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print(f"openai? = {s.has_openai_key}")
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print(f"anthro? = {s.has_anthropic_key}")
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print(f"rag_path = {s.rag_chroma_path}")
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