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
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Doiiars
@@ -12,8 +12,6 @@ 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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__all__ = [
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"clean_json_tags",
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"clean_markdown_tags",
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@@ -21,6 +19,4 @@ __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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]
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+34
-212
@@ -6,218 +6,40 @@ Handles environment variables and config file parameters.
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import os
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from dataclasses import dataclass
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from typing import Optional
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from pydantic_settings import BaseSettings
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from pydantic import Field
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from loguru import logger
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class Settings(BaseSettings):
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INSIGHT_ENGINE_API_KEY: Optional[str] = Field(None, description="Insight Engine LLM API密钥")
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INSIGHT_ENGINE_BASE_URL: Optional[str] = Field(None, description="Insight Engine LLM base url，可选")
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INSIGHT_ENGINE_MODEL_NAME: Optional[str] = Field(None, description="Insight Engine LLM模型名称")
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INSIGHT_ENGINE_PROVIDER: Optional[str] = Field(None, description="Insight Engine模型提供者，不再建议使用")
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DB_HOST: Optional[str] = Field(None, description="数据库主机")
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DB_USER: Optional[str] = Field(None, description="数据库用户名")
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DB_PASSWORD: Optional[str] = Field(None, description="数据库密码")
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DB_NAME: Optional[str] = Field(None, description="数据库名称")
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DB_PORT: int = Field(3306, description="数据库端口")
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DB_CHARSET: str = Field("utf8mb4", description="数据库字符集")
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DB_DIALECT: Optional[str] = Field("mysql", description="数据库方言，如mysql、postgresql等，SQLAlchemy后端选择")
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MAX_REFLECTIONS: int = Field(3, description="最大反思次数")
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MAX_PARAGRAPHS: int = Field(6, description="最大段落数")
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SEARCH_TIMEOUT: int = Field(240, description="单次搜索请求超时")
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MAX_CONTENT_LENGTH: int = Field(500000, description="搜索最大内容长度")
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DEFAULT_SEARCH_HOT_CONTENT_LIMIT: int = Field(100, description="热榜内容默认最大数")
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DEFAULT_SEARCH_TOPIC_GLOBALLY_LIMIT_PER_TABLE: int = Field(50, description="按表全局话题最大数")
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DEFAULT_SEARCH_TOPIC_BY_DATE_LIMIT_PER_TABLE: int = Field(100, description="按日期话题最大数")
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DEFAULT_GET_COMMENTS_FOR_TOPIC_LIMIT: int = Field(500, description="单话题评论最大数")
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DEFAULT_SEARCH_TOPIC_ON_PLATFORM_LIMIT: int = Field(200, description="平台搜索话题最大数")
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MAX_SEARCH_RESULTS_FOR_LLM: int = Field(0, description="供LLM用搜索结果最大数")
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MAX_HIGH_CONFIDENCE_SENTIMENT_RESULTS: int = Field(0, description="高置信度情感分析最大数")
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OUTPUT_DIR: str = Field("reports", description="输出路径")
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SAVE_INTERMEDIATE_STATES: bool = Field(True, description="是否保存中间状态")
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def _get_value(source, key: str, default=None):
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"""
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Helper to fetch a configuration value with environment fallback.
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"""
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value = None
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if isinstance(source, dict):
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value = source.get(key)
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else:
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value = getattr(source, key, None)
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class Config:
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env_file = ".env"
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env_prefix = ""
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case_sensitive = False
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extra = "allow"
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if value is None:
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value = os.getenv(key, default)
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return value if value not in ("", None) else default
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@dataclass
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class Config:
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"""Insight Engine configuration."""
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# LLM 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 # kept for backward compatibility
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# Database configuration
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db_host: Optional[str] = None
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db_user: Optional[str] = None
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db_password: Optional[str] = None
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db_name: Optional[str] = None
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db_port: int = 3306
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db_charset: str = "utf8mb4"
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# Model behaviour configuration
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max_reflections: int = 3
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max_paragraphs: int = 6
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search_timeout: int = 240
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max_content_length: int = 500000
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# Search result limits
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default_search_hot_content_limit: int = 100
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default_search_topic_globally_limit_per_table: int = 50
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default_search_topic_by_date_limit_per_table: int = 100
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default_get_comments_for_topic_limit: int = 500
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default_search_topic_on_platform_limit: int = 200
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max_search_results_for_llm: int = 0
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max_high_confidence_sentiment_results: int = 0
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# Output configuration
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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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# Provider is no longer used, but keep the attribute for compatibility.
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self.llm_provider = self.llm_model_name
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def validate(self) -> bool:
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"""Validate configuration."""
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if not self.llm_api_key:
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print("错误: Insight Engine LLM API Key 未设置 (INSIGHT_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: Insight Engine 模型名称未设置 (INSIGHT_ENGINE_MODEL_NAME)。")
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return False
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if not all([self.db_host, self.db_user, self.db_password, self.db_name]):
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print("错误: 数据库连接信息不完整，请检查 config.py 中的 DB_* 配置。")
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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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"""Create configuration from file."""
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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, "INSIGHT_ENGINE_API_KEY"),
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llm_base_url=_get_value(config_module, "INSIGHT_ENGINE_BASE_URL"),
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llm_model_name=_get_value(config_module, "INSIGHT_ENGINE_MODEL_NAME"),
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db_host=_get_value(config_module, "DB_HOST"),
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db_user=_get_value(config_module, "DB_USER"),
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db_password=_get_value(config_module, "DB_PASSWORD"),
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db_name=_get_value(config_module, "DB_NAME"),
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db_port=int(_get_value(config_module, "DB_PORT", 3306)),
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db_charset=_get_value(config_module, "DB_CHARSET", "utf8mb4"),
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max_reflections=int(_get_value(config_module, "MAX_REFLECTIONS", 3)),
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max_paragraphs=int(_get_value(config_module, "MAX_PARAGRAPHS", 6)),
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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", 500000)),
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default_search_hot_content_limit=int(
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_get_value(config_module, "DEFAULT_SEARCH_HOT_CONTENT_LIMIT", 100)
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),
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default_search_topic_globally_limit_per_table=int(
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_get_value(config_module, "DEFAULT_SEARCH_TOPIC_GLOBALLY_LIMIT_PER_TABLE", 50)
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),
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default_search_topic_by_date_limit_per_table=int(
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_get_value(config_module, "DEFAULT_SEARCH_TOPIC_BY_DATE_LIMIT_PER_TABLE", 100)
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),
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default_get_comments_for_topic_limit=int(
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_get_value(config_module, "DEFAULT_GET_COMMENTS_FOR_TOPIC_LIMIT", 500)
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),
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default_search_topic_on_platform_limit=int(
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_get_value(config_module, "DEFAULT_SEARCH_TOPIC_ON_PLATFORM_LIMIT", 200)
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),
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max_search_results_for_llm=int(_get_value(config_module, "MAX_SEARCH_RESULTS_FOR_LLM", 0)),
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max_high_confidence_sentiment_results=int(
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_get_value(config_module, "MAX_HIGH_CONFIDENCE_SENTIMENT_RESULTS", 0)
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),
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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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# .env style configuration
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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, "INSIGHT_ENGINE_API_KEY"),
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llm_base_url=_get_value(config_dict, "INSIGHT_ENGINE_BASE_URL"),
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llm_model_name=_get_value(config_dict, "INSIGHT_ENGINE_MODEL_NAME"),
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db_host=_get_value(config_dict, "DB_HOST"),
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db_user=_get_value(config_dict, "DB_USER"),
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db_password=_get_value(config_dict, "DB_PASSWORD"),
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db_name=_get_value(config_dict, "DB_NAME"),
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db_port=int(_get_value(config_dict, "DB_PORT", 3306)),
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db_charset=_get_value(config_dict, "DB_CHARSET", "utf8mb4"),
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max_reflections=int(_get_value(config_dict, "MAX_REFLECTIONS", 3)),
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max_paragraphs=int(_get_value(config_dict, "MAX_PARAGRAPHS", 6)),
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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", 500000)),
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default_search_hot_content_limit=int(
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_get_value(config_dict, "DEFAULT_SEARCH_HOT_CONTENT_LIMIT", 100)
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),
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default_search_topic_globally_limit_per_table=int(
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_get_value(config_dict, "DEFAULT_SEARCH_TOPIC_GLOBALLY_LIMIT_PER_TABLE", 50)
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),
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default_search_topic_by_date_limit_per_table=int(
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_get_value(config_dict, "DEFAULT_SEARCH_TOPIC_BY_DATE_LIMIT_PER_TABLE", 100)
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),
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default_get_comments_for_topic_limit=int(
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_get_value(config_dict, "DEFAULT_GET_COMMENTS_FOR_TOPIC_LIMIT", 500)
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),
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default_search_topic_on_platform_limit=int(
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_get_value(config_dict, "DEFAULT_SEARCH_TOPIC_ON_PLATFORM_LIMIT", 200)
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),
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max_search_results_for_llm=int(_get_value(config_dict, "MAX_SEARCH_RESULTS_FOR_LLM", 0)),
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max_high_confidence_sentiment_results=int(
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_get_value(config_dict, "MAX_HIGH_CONFIDENCE_SENTIMENT_RESULTS", 0)
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),
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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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def load_config(config_file: Optional[str] = None) -> Config:
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"""
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Load configuration.
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"""
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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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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 configuration (sensitive values masked)."""
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print("\n=== Insight 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"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"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(f"数据库连接: {'已配置' if all([config.db_host, config.db_user, config.db_password, config.db_name]) else '未配置'}")
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print("========================\n")
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settings = Settings()
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