1. 统一为使用基于pydantic的.env环境变量管理配置
2. 全项目基于loguru进行日志管理
This commit is contained in:
Doiiars
@@ -12,7 +12,7 @@ from .text_processing import (
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format_search_results_for_prompt
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)
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from .config import Config, load_config
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from .config import Settings
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__all__ = [
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"clean_json_tags",
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@@ -21,6 +21,5 @@ __all__ = [
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"extract_clean_response",
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"update_state_with_search_results",
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"format_search_results_for_prompt",
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"Config",
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"load_config"
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"Settings",
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]
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+68
-140
@@ -1,151 +1,79 @@
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"""
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Configuration management module for the Query Engine.
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Query Engine 配置管理模块
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此模块使用 pydantic-settings 管理 Query Engine 的配置，支持从环境变量和 .env 文件自动加载。
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数据模型定义位置：
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- 本文件 - 配置模型定义
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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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from pydantic_settings import BaseSettings
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from pydantic import Field
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from typing import Optional
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from loguru import logger
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def _get_value(source, key: str, default=None, *fallback_keys: str):
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candidates = (key,) + fallback_keys
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value = None
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for candidate in candidates:
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if isinstance(source, dict):
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value = source.get(candidate)
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else:
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value = getattr(source, candidate, None)
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if value not in (None, ""):
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break
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if value in (None, ""):
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for candidate in candidates:
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env_val = os.getenv(candidate)
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if env_val not in (None, ""):
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value = env_val
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break
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return value if value not in (None, "") else default
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# 计算 .env 优先级：优先当前工作目录，其次项目根目录
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PROJECT_ROOT: Path = Path(__file__).resolve().parents[2]
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CWD_ENV: Path = Path.cwd() / ".env"
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ENV_FILE: str = str(CWD_ENV if CWD_ENV.exists() else (PROJECT_ROOT / ".env"))
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@dataclass
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class Config:
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"""Query Engine configuration."""
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llm_api_key: Optional[str] = None
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llm_base_url: Optional[str] = None
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llm_model_name: Optional[str] = None
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llm_provider: Optional[str] = None # compatibility
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tavily_api_key: Optional[str] = None
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search_timeout: int = 240
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max_content_length: int = 20000
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max_reflections: int = 2
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max_paragraphs: int = 5
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max_search_results: int = 20
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output_dir: str = "reports"
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save_intermediate_states: bool = True
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def __post_init__(self):
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if not self.llm_provider and self.llm_model_name:
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self.llm_provider = self.llm_model_name
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def validate(self) -> bool:
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if not self.llm_api_key:
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print("错误: Query Engine LLM API Key 未设置 (QUERY_ENGINE_API_KEY)。")
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return False
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if not self.llm_model_name:
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print("错误: Query Engine 模型名称未设置 (QUERY_ENGINE_MODEL_NAME)。")
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return False
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if not self.tavily_api_key:
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print("错误: Tavily API Key 未设置 (TAVILY_API_KEY)。")
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return False
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return True
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@classmethod
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def from_file(cls, config_file: str) -> "Config":
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if config_file.endswith(".py"):
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import importlib.util
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spec = importlib.util.spec_from_file_location("config", config_file)
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config_module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(config_module)
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return cls(
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llm_api_key=_get_value(config_module, "QUERY_ENGINE_API_KEY"),
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llm_base_url=_get_value(config_module, "QUERY_ENGINE_BASE_URL"),
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llm_model_name=_get_value(config_module, "QUERY_ENGINE_MODEL_NAME"),
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tavily_api_key=_get_value(config_module, "TAVILY_API_KEY"),
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search_timeout=int(_get_value(config_module, "SEARCH_TIMEOUT", 240)),
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max_content_length=int(_get_value(config_module, "SEARCH_CONTENT_MAX_LENGTH", 20000)),
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max_reflections=int(_get_value(config_module, "MAX_REFLECTIONS", 2)),
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max_paragraphs=int(_get_value(config_module, "MAX_PARAGRAPHS", 5)),
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max_search_results=int(_get_value(config_module, "MAX_SEARCH_RESULTS", 20)),
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output_dir=_get_value(config_module, "OUTPUT_DIR", "reports"),
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save_intermediate_states=str(
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_get_value(config_module, "SAVE_INTERMEDIATE_STATES", "true")
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).lower()
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in ("true", "1", "yes"),
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)
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config_dict = {}
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if os.path.exists(config_file):
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with open(config_file, "r", encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if line and not line.startswith("#") and "=" in line:
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key, value = line.split("=", 1)
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config_dict[key.strip()] = value.strip()
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return cls(
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llm_api_key=_get_value(config_dict, "QUERY_ENGINE_API_KEY"),
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llm_base_url=_get_value(config_dict, "QUERY_ENGINE_BASE_URL"),
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llm_model_name=_get_value(config_dict, "QUERY_ENGINE_MODEL_NAME"),
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tavily_api_key=_get_value(config_dict, "TAVILY_API_KEY"),
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search_timeout=int(_get_value(config_dict, "SEARCH_TIMEOUT", 240)),
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max_content_length=int(_get_value(config_dict, "SEARCH_CONTENT_MAX_LENGTH", 20000)),
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max_reflections=int(_get_value(config_dict, "MAX_REFLECTIONS", 2)),
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max_paragraphs=int(_get_value(config_dict, "MAX_PARAGRAPHS", 5)),
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max_search_results=int(_get_value(config_dict, "MAX_SEARCH_RESULTS", 20)),
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output_dir=_get_value(config_dict, "OUTPUT_DIR", "reports"),
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save_intermediate_states=str(
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_get_value(config_dict, "SAVE_INTERMEDIATE_STATES", "true")
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).lower()
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in ("true", "1", "yes"),
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)
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class Settings(BaseSettings):
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"""
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Query Engine 全局配置；支持 .env 和环境变量自动加载。
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变量名与原 config.py 大写一致，便于平滑过渡。
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"""
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# ======================= LLM 相关 =======================
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QUERY_ENGINE_API_KEY: str = Field(..., description="Query Engine LLM API密钥，用于主LLM。您可以更改每个部分LLM使用的API，🚩只要兼容OpenAI请求格式都可以，定义好KEY、BASE_URL与MODEL_NAME即可正常使用。")
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QUERY_ENGINE_BASE_URL: Optional[str] = Field(None, description="Query Engine LLM接口BaseUrl，可自定义厂商API")
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QUERY_ENGINE_MODEL_NAME: str = Field(..., description="Query Engine LLM模型名称")
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QUERY_ENGINE_PROVIDER: Optional[str] = Field(None, description="Query Engine LLM提供商（兼容字段）")
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# ================== 网络工具配置 ====================
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TAVILY_API_KEY: str = Field(..., description="Tavily API（申请地址：https://www.tavily.com/）API密钥，用于Tavily网络搜索")
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# ================== 搜索参数配置 ====================
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SEARCH_TIMEOUT: int = Field(240, description="搜索超时（秒）")
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SEARCH_CONTENT_MAX_LENGTH: int = Field(20000, description="用于提示的最长内容长度")
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MAX_REFLECTIONS: int = Field(2, description="最大反思轮数")
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MAX_PARAGRAPHS: int = Field(5, description="最大段落数")
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MAX_SEARCH_RESULTS: int = Field(20, description="最大搜索结果数")
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# ================== 输出配置 ====================
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OUTPUT_DIR: str = Field("reports", description="输出目录")
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SAVE_INTERMEDIATE_STATES: bool = Field(True, description="是否保存中间状态")
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class Config:
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env_file = ENV_FILE
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env_prefix = ""
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case_sensitive = False
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extra = "allow"
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def load_config(config_file: Optional[str] = None) -> Config:
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if config_file:
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if not os.path.exists(config_file):
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raise FileNotFoundError(f"配置文件不存在: {config_file}")
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file_to_load = config_file
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else:
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for candidate in ("config.py", "config.env", ".env"):
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if os.path.exists(candidate):
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file_to_load = candidate
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print(f"已找到配置文件: {candidate}")
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break
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else:
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raise FileNotFoundError("未找到配置文件，请创建 config.py。")
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# 创建全局配置实例
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settings = Settings()
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config = Config.from_file(file_to_load)
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if not config.validate():
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raise ValueError("配置校验失败，请检查 config.py 中的相关配置。")
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return config
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def print_config(config: Config):
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print("\n=== Query Engine 配置 ===")
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print(f"LLM 模型: {config.llm_model_name}")
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print(f"LLM Base URL: {config.llm_base_url or '(默认)'}")
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print(f"Tavily API Key: {'已配置' if config.tavily_api_key else '未配置'}")
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print(f"搜索超时: {config.search_timeout} 秒")
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print(f"最长内容长度: {config.max_content_length}")
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print(f"最大反思次数: {config.max_reflections}")
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print(f"最大段落数: {config.max_paragraphs}")
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print(f"最大搜索结果数: {config.max_search_results}")
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print(f"输出目录: {config.output_dir}")
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print(f"保存中间状态: {config.save_intermediate_states}")
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print(f"LLM API Key: {'已配置' if config.llm_api_key else '未配置'}")
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print("========================\n")
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def print_config(config: Settings):
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"""
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打印配置信息
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Args:
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config: Settings配置对象
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"""
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message = ""
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message += "=== Query Engine 配置 ===\n"
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message += f"LLM 模型: {config.QUERY_ENGINE_MODEL_NAME}\n"
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message += f"LLM Base URL: {config.QUERY_ENGINE_BASE_URL or '(默认)'}\n"
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message += f"Tavily API Key: {'已配置' if config.TAVILY_API_KEY else '未配置'}\n"
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message += f"搜索超时: {config.SEARCH_TIMEOUT} 秒\n"
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message += f"最长内容长度: {config.SEARCH_CONTENT_MAX_LENGTH}\n"
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message += f"最大反思次数: {config.MAX_REFLECTIONS}\n"
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message += f"最大段落数: {config.MAX_PARAGRAPHS}\n"
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message += f"最大搜索结果数: {config.MAX_SEARCH_RESULTS}\n"
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message += f"输出目录: {config.OUTPUT_DIR}\n"
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message += f"保存中间状态: {config.SAVE_INTERMEDIATE_STATES}\n"
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message += f"LLM API Key: {'已配置' if config.QUERY_ENGINE_API_KEY else '未配置'}\n"
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message += "========================\n"
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logger.info(message)
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