{"repo": "chopinau/nexuspulse-influence-project", "files": {"python_backend/config.py": {"359daa4d119dc1551d99bc2bffda7e7afd49cae6": {"t": 1785746497.6138878, "n": 8324, "h": "70994016b7e6ee74", "c": "# -*- coding: utf-8 -*-\n\"\"\"\n微舆配置文件\n\n此模块使用 pydantic-settings 管理全局配置，支持从环境变量和 .env 文件自动加载。\n数据模型定义位置：\n- 本文件 - 配置模型定义\n\"\"\"\n\nfrom pathlib import Path\nfrom pydantic_settings import BaseSettings\nfrom pydantic import Field, ConfigDict\nfrom typing import Optional, Literal\nfrom loguru import logger\n\n\n# 计算 .env 优先级：优先当前工作目录，其次项目根目录\nPROJECT_ROOT: Path = Path(__file__).resolve().parent\nCWD_ENV: Path = Path.cwd() / \".env\"\nENV_FILE: str = str(CWD_ENV if CWD_ENV.exists() else (PROJECT_ROOT / \".env\"))\n\n\nclass Settings(BaseSettings):\n    \"\"\"\n    全局配置；支持 .env 和环境变量自动加载。\n    变量名与原 config.py 大写一致，便于平滑过渡。\n    \"\"\"\n    # ================== Flask 服务器配置 ====================\n    HOST: str = Field(\"0.0.0.0\", description=\"BETTAFISH 主机地址，例如 0.0.0.0 或 127.0.0.1\")\n    PORT: int = Field(5000, description=\"Flask服务器端口号，默认5000\")\n\n    # ================== 日志配置 ====================\n    LOG_FILE: str = Field(\"logs/app.log\", description=\"日志文件路径\")\n\n    # ================== 报告输出配置 ====================\n    OUTPUT_DIR: str = Field(\"final_reports\", description=\"最终报告输出目录\")\n    CHAPTER_OUTPUT_DIR: str = Field(\"final_reports/chapters\", description=\"章节中间文件输出目录\")\n    DOCUMENT_IR_OUTPUT_DIR: str = Field(\"final_reports/ir\", description=\"文档中间表示输出目录\")\n\n    TEMPLATE_DIR: str = Field(\"ReportEngine/report_template\", description=\"报告模板目录\")\n    JSON_ERROR_LOG_DIR: str = Field(\"logs/json_errors\", description=\"JSON解析错误日志目录\")\n    CHAPTER_JSON_MAX_ATTEMPTS: int = Field(3, description=\"章节JSON生成最大尝试次数\")\n\n    # ====================== 数据库配置 ======================\n    DB_DIALECT: str = Field(\"postgresql\", description=\"数据库类型，可选 mysql 或 postgresql；请与其他连接信息同时配置\")\n    DB_HOST: str = Field(\"your_db_host\", description=\"数据库主机，例如localhost 或 127.0.0.1\")\n    DB_PORT: int = Field(3306, description=\"数据库端口号，默认为3306\")\n    DB_USER: str = Field(\"your_db_user\", description=\"数据库用户名\")\n    DB_PASSWORD: str = Field(\"your_db_password\", description=\"数据库密码\")\n    DB_NAME: str = Field(\"your_db_name\", description=\"数据库名称\")\n    DB_CHARSET: str = Field(\"utf8mb4\", description=\"数据库字符集，推荐utf8mb4，兼容emoji\")\n    \n    # ======================= LLM 相关 =======================\n    # 我们的LLM模型API赞助商有：https://aihubmix.com/?aff=8Ds9，提供了非常全面的模型api\n    \n    # Insight Agent（推荐Kimi，申请地址：https://platform.moonshot.cn/）\n    INSIGHT_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Insight Agent（推荐 kimi-k2，官方申请地址：https://platform.moonshot.cn/）API 密钥，用于主 LLM。🚩请先按推荐配置申请并跑通，再根据需要调整 KEY、BASE_URL 与 MODEL_NAME。\")\n    INSIGHT_ENGINE_BASE_URL: Optional[str] = Field(\"https://api.moonshot.cn/v1\", description=\"Insight Agent LLM BaseUrl，可根据厂商自定义\")\n    INSIGHT_ENGINE_MODEL_NAME: str = Field(\"kimi-k2-0711-preview\", description=\"Insight Agent LLM 模型名称，例如 kimi-k2-0711-preview\")\n    \n    # Media Agent（推荐Gemini，推荐中转厂商：https://aihubmix.com/?aff=8Ds9）\n    MEDIA_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Media Agent（推荐 gemini-2.5-pro，中转厂商申请地址：https://aihubmix.com/?aff=8Ds9）API 密钥\")\n    MEDIA_ENGINE_BASE_URL: Optional[str] = Field(\"https://aihubmix.com/v1\", description=\"Media Agent LLM BaseUrl，可根据中转服务调整\")\n    MEDIA_ENGINE_MODEL_NAME: str = Field(\"gemini-2.5-pro\", description=\"Media Agent LLM 模型名称，如 gemini-2.5-pro\")\n    \n    # Query Agent（推荐DeepSeek，申请地址：https://www.deepseek.com/）\n    QUERY_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Query Agent（推荐 deepseek，官方申请地址：https://platform.deepseek.com/）API 密钥\")\n    QUERY_ENGINE_BASE_URL: Optional[str] = Field(\"https://api.deepseek.com\", description=\"Query Agent LLM BaseUrl\")\n    QUERY_ENGINE_MODEL_NAME: str = Field(\"deepseek-chat\", description=\"Query Agent LLM 模型名称，如 deepseek-reasoner\")\n    \n    # Report Agent（推荐Gemini，推荐中转厂商：https://aihubmix.com/?aff=8Ds9）\n    REPORT_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Report Agent（推荐 gemini-2.5-pro，中转厂商申请地址：https://aihubmix.com/?aff=8Ds9）API 密钥\")\n    REPORT_ENGINE_BASE_URL: Optional[str] = Field(\"https://aihubmix.com/v1\", description=\"Report Agent LLM BaseUrl，可根据中转服务调整\")\n    REPORT_ENGINE_MODEL_NAME: str = Field(\"gemini-2.5-pro\", description=\"Report Agent LLM 模型名称，如 gemini-2.5-pro\")\n\n    # MindSpider Agent（推荐Deepseek，官方申请地址：https://platform.deepseek.com/）\n    MINDSPIDER_API_KEY: Optional[str] = Field(None, description=\"MindSpider Agent（推荐 deepseek，官方申请地址：https://platform.deepseek.com/）API 密钥\")\n    MINDSPIDER_BASE_URL: Optional[str] = Field(None, description=\"MindSpider Agent BaseUrl，可按所选服务配置\")\n    MINDSPIDER_MODEL_NAME: Optional[str] = Field(None, description=\"MindSpider Agent 模型名称，例如 deepseek-reasoner\")\n    \n    # Forum Host（Qwen3最新模型，这里我使用了硅基流动这个平台，申请地址：https://cloud.siliconflow.cn/）\n    FORUM_HOST_API_KEY: Optional[str] = Field(None, description=\"Forum Host（推荐 qwen-plus，官方申请地址：https://www.aliyun.com/product/bailian）API 密钥\")\n    FORUM_HOST_BASE_URL: Optional[str] = Field(None, description=\"Forum Host LLM BaseUrl，可按所选服务配置\")\n    FORUM_HOST_MODEL_NAME: Optional[str] = Field(None, description=\"Forum Host LLM 模型名称，例如 qwen-plus\")\n    \n    # SQL keyword Optimizer（小参数Qwen3模型，这里我使用了硅基流动这个平台，申请地址：https://cloud.siliconflow.cn/）\n    KEYWORD_OPTIMIZER_API_KEY: Optional[str] = Field(None, description=\"SQL Keyword Optimizer（推荐 qwen-plus，官方申请地址：https://www.aliyun.com/product/bailian）API 密钥\")\n    KEYWORD_OPTIMIZER_BASE_URL: Optional[str] = Field(None, description=\"Keyword Optimizer BaseUrl，可按所选服务配置\")\n    KEYWORD_OPTIMIZER_MODEL_NAME: Optional[str] = Field(None, description=\"Keyword Optimizer LLM 模型名称，例如 qwen-plus\")\n    \n    # ================== GraphRAG 配置 ====================\n    GRAPHRAG_ENABLED: bool = Field(False, description=\"是否启用GraphRAG知识图谱功能（true/false）\")\n    GRAPHRAG_MAX_QUERIES: int = Field(3, description=\"GraphRAG每个章节生成前的最大查询次数\")\n    \n    # ================== 网络工具配置 ====================\n    # Tavily API（申请地址：https://www.tavily.com/）\n    TAVILY_API_KEY: Optional[str] = Field(None, description=\"Tavily API（申请地址：https://www.tavily.com/）API密钥，用于Tavily网络搜索\")\n\n    SEARCH_TOOL_TYPE: Literal[\"AnspireAPI\", \"BochaAPI\"] = Field(\"AnspireAPI\", description=\"网络搜索工具类型，支持BochaAPI或AnspireAPI两种，默认为AnspireAPI\")\n    # Bocha API（申请地址：https://open.bochaai.com/）\n    BOCHA_BASE_URL: Optional[str] = Field(\"https://api.bocha.cn/v1/ai-search\", description=\"Bocha AI 搜索BaseUrl或博查网页搜索BaseUrl\")\n    BOCHA_WEB_SEARCH_API_KEY: Optional[str] = Field(None, description=\"Bocha API（申请地址：https://open.bochaai.com/）API密钥，用于Bocha搜索\")\n\n    # Anspire AI Search API（申请地址：https://open.anspire.cn/?share_code=3E1FUOUH）\n    ANSPIRE_BASE_URL: Optional[str] = Field(\"https://plugin.anspire.cn/api/ntsearch/search\", description=\"Anspire AI 搜索BaseUrl\")\n    ANSPIRE_API_KEY: Optional[str] = Field(None, description=\"Anspire AI Search API（申请地址：https://open.anspire.cn/?share_code=3E1FUOUH）API密钥，用于Anspire搜索\")\n\n    # ================== 飞书配置 ====================\n    FEISHU_APP_ID: Optional[str] = Field(None, description=\"飞书应用 App ID，用于飞书多维表格API认证\")\n    FEISHU_APP_SECRET: Optional[str] = Field(None, description=\"飞书应用 App Secret，用于飞书多维表格API认证\")\n    FEISHU_APP_TOKEN: Optional[str] = Field(None, description=\"飞书多维表格 App Token，用于指定默认操作的Bitable\")\n\n    \n    # ================== Insight Engine 搜索配置 ====================\n    DEFAULT_SEARCH_HOT_CONTENT_LIMIT: int = Field(100, description=\"热榜内容默认最大数\")\n    DEFAULT_SEARCH_TOPIC_GLOBALLY_LIMIT_PER_TABLE: int = Field(50, description=\"按表全局话题最大数\")\n    DEFAULT_SEARCH_TOPIC_BY_DATE_LIMIT_PER_TABLE: int = Field(100, description=\"按日期话题最大数\")\n    DEFAULT_GET_COMMENTS_FOR_TOPIC_LIMIT: int = Field(500, description=\"单话题评论最大数\")\n    DEFAULT_SEARCH_TOPIC_ON_PLATFORM_LIMIT: int = Field(200, description=\"平台搜索话题最大数\")\n    MAX_SEARCH_RESULTS_FOR_LLM: int = Field(0, description=\"供LLM用搜索结果最大数\")\n    MAX_HIGH_CONFIDENCE_SENTIMENT_RESULTS: int = Field(0, description=\"高置信度情感分析最大数\")\n    MAX_REFLECTIONS: int = Field(3, description=\"最大反思次数\")\n    MAX_PARAGRAPHS: int = Field(6, description=\"最大段落数\")\n    SEARCH_TIMEOUT: int = Field(240, description=\"单次搜索请求超时\")\n    MAX_CONTENT_LENGTH: int = Field(500000, description=\"搜索最大内容长度\")\n    \n    model_config = ConfigDict(\n        env_file=ENV_FILE,\n        env_prefix=\"\",\n        case_sensitive=False,\n        extra=\"allow\"\n    )\n\n\n# 创建全局配置实例\nsettings = Settings()\n\n\ndef reload_settings() -> Settings:\n    \"\"\"\n    重新加载配置\n    \n    从 .env 文件和环境变量重新加载配置，更新全局 settings 实例。\n    用于在运行时动态更新配置。\n    \n    Returns:\n        Settings: 新创建的配置实例\n    \"\"\"\n    \n    global settings\n    settings = Settings()\n    return settings\n"}, "7a674841724d4f84a48b940bd38ca59a7d5c5598": {"t": 1785764973.657902, "n": 8324, "h": "70994016b7e6ee74", "c": "# -*- coding: utf-8 -*-\n\"\"\"\n微舆配置文件\n\n此模块使用 pydantic-settings 管理全局配置，支持从环境变量和 .env 文件自动加载。\n数据模型定义位置：\n- 本文件 - 配置模型定义\n\"\"\"\n\nfrom pathlib import Path\nfrom pydantic_settings import BaseSettings\nfrom pydantic import Field, ConfigDict\nfrom typing import Optional, Literal\nfrom loguru import logger\n\n\n# 计算 .env 优先级：优先当前工作目录，其次项目根目录\nPROJECT_ROOT: Path = Path(__file__).resolve().parent\nCWD_ENV: Path = Path.cwd() / \".env\"\nENV_FILE: str = str(CWD_ENV if CWD_ENV.exists() else (PROJECT_ROOT / \".env\"))\n\n\nclass Settings(BaseSettings):\n    \"\"\"\n    全局配置；支持 .env 和环境变量自动加载。\n    变量名与原 config.py 大写一致，便于平滑过渡。\n    \"\"\"\n    # ================== Flask 服务器配置 ====================\n    HOST: str = Field(\"0.0.0.0\", description=\"BETTAFISH 主机地址，例如 0.0.0.0 或 127.0.0.1\")\n    PORT: int = Field(5000, description=\"Flask服务器端口号，默认5000\")\n\n    # ================== 日志配置 ====================\n    LOG_FILE: str = Field(\"logs/app.log\", description=\"日志文件路径\")\n\n    # ================== 报告输出配置 ====================\n    OUTPUT_DIR: str = Field(\"final_reports\", description=\"最终报告输出目录\")\n    CHAPTER_OUTPUT_DIR: str = Field(\"final_reports/chapters\", description=\"章节中间文件输出目录\")\n    DOCUMENT_IR_OUTPUT_DIR: str = Field(\"final_reports/ir\", description=\"文档中间表示输出目录\")\n\n    TEMPLATE_DIR: str = Field(\"ReportEngine/report_template\", description=\"报告模板目录\")\n    JSON_ERROR_LOG_DIR: str = Field(\"logs/json_errors\", description=\"JSON解析错误日志目录\")\n    CHAPTER_JSON_MAX_ATTEMPTS: int = Field(3, description=\"章节JSON生成最大尝试次数\")\n\n    # ====================== 数据库配置 ======================\n    DB_DIALECT: str = Field(\"postgresql\", description=\"数据库类型，可选 mysql 或 postgresql；请与其他连接信息同时配置\")\n    DB_HOST: str = Field(\"your_db_host\", description=\"数据库主机，例如localhost 或 127.0.0.1\")\n    DB_PORT: int = Field(3306, description=\"数据库端口号，默认为3306\")\n    DB_USER: str = Field(\"your_db_user\", description=\"数据库用户名\")\n    DB_PASSWORD: str = Field(\"your_db_password\", description=\"数据库密码\")\n    DB_NAME: str = Field(\"your_db_name\", description=\"数据库名称\")\n    DB_CHARSET: str = Field(\"utf8mb4\", description=\"数据库字符集，推荐utf8mb4，兼容emoji\")\n    \n    # ======================= LLM 相关 =======================\n    # 我们的LLM模型API赞助商有：https://aihubmix.com/?aff=8Ds9，提供了非常全面的模型api\n    \n    # Insight Agent（推荐Kimi，申请地址：https://platform.moonshot.cn/）\n    INSIGHT_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Insight Agent（推荐 kimi-k2，官方申请地址：https://platform.moonshot.cn/）API 密钥，用于主 LLM。🚩请先按推荐配置申请并跑通，再根据需要调整 KEY、BASE_URL 与 MODEL_NAME。\")\n    INSIGHT_ENGINE_BASE_URL: Optional[str] = Field(\"https://api.moonshot.cn/v1\", description=\"Insight Agent LLM BaseUrl，可根据厂商自定义\")\n    INSIGHT_ENGINE_MODEL_NAME: str = Field(\"kimi-k2-0711-preview\", description=\"Insight Agent LLM 模型名称，例如 kimi-k2-0711-preview\")\n    \n    # Media Agent（推荐Gemini，推荐中转厂商：https://aihubmix.com/?aff=8Ds9）\n    MEDIA_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Media Agent（推荐 gemini-2.5-pro，中转厂商申请地址：https://aihubmix.com/?aff=8Ds9）API 密钥\")\n    MEDIA_ENGINE_BASE_URL: Optional[str] = Field(\"https://aihubmix.com/v1\", description=\"Media Agent LLM BaseUrl，可根据中转服务调整\")\n    MEDIA_ENGINE_MODEL_NAME: str = Field(\"gemini-2.5-pro\", description=\"Media Agent LLM 模型名称，如 gemini-2.5-pro\")\n    \n    # Query Agent（推荐DeepSeek，申请地址：https://www.deepseek.com/）\n    QUERY_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Query Agent（推荐 deepseek，官方申请地址：https://platform.deepseek.com/）API 密钥\")\n    QUERY_ENGINE_BASE_URL: Optional[str] = Field(\"https://api.deepseek.com\", description=\"Query Agent LLM BaseUrl\")\n    QUERY_ENGINE_MODEL_NAME: str = Field(\"deepseek-chat\", description=\"Query Agent LLM 模型名称，如 deepseek-reasoner\")\n    \n    # Report Agent（推荐Gemini，推荐中转厂商：https://aihubmix.com/?aff=8Ds9）\n    REPORT_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Report Agent（推荐 gemini-2.5-pro，中转厂商申请地址：https://aihubmix.com/?aff=8Ds9）API 密钥\")\n    REPORT_ENGINE_BASE_URL: Optional[str] = Field(\"https://aihubmix.com/v1\", description=\"Report Agent LLM BaseUrl，可根据中转服务调整\")\n    REPORT_ENGINE_MODEL_NAME: str = Field(\"gemini-2.5-pro\", description=\"Report Agent LLM 模型名称，如 gemini-2.5-pro\")\n\n    # MindSpider Agent（推荐Deepseek，官方申请地址：https://platform.deepseek.com/）\n    MINDSPIDER_API_KEY: Optional[str] = Field(None, description=\"MindSpider Agent（推荐 deepseek，官方申请地址：https://platform.deepseek.com/）API 密钥\")\n    MINDSPIDER_BASE_URL: Optional[str] = Field(None, description=\"MindSpider Agent BaseUrl，可按所选服务配置\")\n    MINDSPIDER_MODEL_NAME: Optional[str] = Field(None, description=\"MindSpider Agent 模型名称，例如 deepseek-reasoner\")\n    \n    # Forum Host（Qwen3最新模型，这里我使用了硅基流动这个平台，申请地址：https://cloud.siliconflow.cn/）\n    FORUM_HOST_API_KEY: Optional[str] = Field(None, description=\"Forum Host（推荐 qwen-plus，官方申请地址：https://www.aliyun.com/product/bailian）API 密钥\")\n    FORUM_HOST_BASE_URL: Optional[str] = Field(None, description=\"Forum Host LLM BaseUrl，可按所选服务配置\")\n    FORUM_HOST_MODEL_NAME: Optional[str] = Field(None, description=\"Forum Host LLM 模型名称，例如 qwen-plus\")\n    \n    # SQL keyword Optimizer（小参数Qwen3模型，这里我使用了硅基流动这个平台，申请地址：https://cloud.siliconflow.cn/）\n    KEYWORD_OPTIMIZER_API_KEY: Optional[str] = Field(None, description=\"SQL Keyword Optimizer（推荐 qwen-plus，官方申请地址：https://www.aliyun.com/product/bailian）API 密钥\")\n    KEYWORD_OPTIMIZER_BASE_URL: Optional[str] = Field(None, description=\"Keyword Optimizer BaseUrl，可按所选服务配置\")\n    KEYWORD_OPTIMIZER_MODEL_NAME: Optional[str] = Field(None, description=\"Keyword Optimizer LLM 模型名称，例如 qwen-plus\")\n    \n    # ================== GraphRAG 配置 ====================\n    GRAPHRAG_ENABLED: bool = Field(False, description=\"是否启用GraphRAG知识图谱功能（true/false）\")\n    GRAPHRAG_MAX_QUERIES: int = Field(3, description=\"GraphRAG每个章节生成前的最大查询次数\")\n    \n    # ================== 网络工具配置 ====================\n    # Tavily API（申请地址：https://www.tavily.com/）\n    TAVILY_API_KEY: Optional[str] = Field(None, description=\"Tavily API（申请地址：https://www.tavily.com/）API密钥，用于Tavily网络搜索\")\n\n    SEARCH_TOOL_TYPE: Literal[\"AnspireAPI\", \"BochaAPI\"] = Field(\"AnspireAPI\", description=\"网络搜索工具类型，支持BochaAPI或AnspireAPI两种，默认为AnspireAPI\")\n    # Bocha API（申请地址：https://open.bochaai.com/）\n    BOCHA_BASE_URL: Optional[str] = Field(\"https://api.bocha.cn/v1/ai-search\", description=\"Bocha AI 搜索BaseUrl或博查网页搜索BaseUrl\")\n    BOCHA_WEB_SEARCH_API_KEY: Optional[str] = Field(None, description=\"Bocha API（申请地址：https://open.bochaai.com/）API密钥，用于Bocha搜索\")\n\n    # Anspire AI Search API（申请地址：https://open.anspire.cn/?share_code=3E1FUOUH）\n    ANSPIRE_BASE_URL: Optional[str] = Field(\"https://plugin.anspire.cn/api/ntsearch/search\", description=\"Anspire AI 搜索BaseUrl\")\n    ANSPIRE_API_KEY: Optional[str] = Field(None, description=\"Anspire AI Search API（申请地址：https://open.anspire.cn/?share_code=3E1FUOUH）API密钥，用于Anspire搜索\")\n\n    # ================== 飞书配置 ====================\n    FEISHU_APP_ID: Optional[str] = Field(None, description=\"飞书应用 App ID，用于飞书多维表格API认证\")\n    FEISHU_APP_SECRET: Optional[str] = Field(None, description=\"飞书应用 App Secret，用于飞书多维表格API认证\")\n    FEISHU_APP_TOKEN: Optional[str] = Field(None, description=\"飞书多维表格 App Token，用于指定默认操作的Bitable\")\n\n    \n    # ================== Insight Engine 搜索配置 ====================\n    DEFAULT_SEARCH_HOT_CONTENT_LIMIT: int = Field(100, description=\"热榜内容默认最大数\")\n    DEFAULT_SEARCH_TOPIC_GLOBALLY_LIMIT_PER_TABLE: int = Field(50, description=\"按表全局话题最大数\")\n    DEFAULT_SEARCH_TOPIC_BY_DATE_LIMIT_PER_TABLE: int = Field(100, description=\"按日期话题最大数\")\n    DEFAULT_GET_COMMENTS_FOR_TOPIC_LIMIT: int = Field(500, description=\"单话题评论最大数\")\n    DEFAULT_SEARCH_TOPIC_ON_PLATFORM_LIMIT: int = Field(200, description=\"平台搜索话题最大数\")\n    MAX_SEARCH_RESULTS_FOR_LLM: int = Field(0, description=\"供LLM用搜索结果最大数\")\n    MAX_HIGH_CONFIDENCE_SENTIMENT_RESULTS: int = Field(0, description=\"高置信度情感分析最大数\")\n    MAX_REFLECTIONS: int = Field(3, description=\"最大反思次数\")\n    MAX_PARAGRAPHS: int = Field(6, description=\"最大段落数\")\n    SEARCH_TIMEOUT: int = Field(240, description=\"单次搜索请求超时\")\n    MAX_CONTENT_LENGTH: int = Field(500000, description=\"搜索最大内容长度\")\n    \n    model_config = ConfigDict(\n        env_file=ENV_FILE,\n        env_prefix=\"\",\n        case_sensitive=False,\n        extra=\"allow\"\n    )\n\n\n# 创建全局配置实例\nsettings = Settings()\n\n\ndef reload_settings() -> Settings:\n    \"\"\"\n    重新加载配置\n    \n    从 .env 文件和环境变量重新加载配置，更新全局 settings 实例。\n    用于在运行时动态更新配置。\n    \n    Returns:\n        Settings: 新创建的配置实例\n    \"\"\"\n    \n    global settings\n    settings = Settings()\n    return settings\n"}, "8987f47d1f1c54bb008f7bbe164da39444ad5996": {"t": 1785764975.122001, "n": 7989, "h": "8eac2567330019bb", "c": "# -*- coding: utf-8 -*-\n\"\"\"\n微舆配置文件\n\n此模块使用 pydantic-settings 管理全局配置，支持从环境变量和 .env 文件自动加载。\n数据模型定义位置：\n- 本文件 - 配置模型定义\n\"\"\"\n\nfrom pathlib import Path\nfrom pydantic_settings import BaseSettings\nfrom pydantic import Field, ConfigDict\nfrom typing import Optional, Literal\nfrom loguru import logger\n\n\n# 计算 .env 优先级：优先当前工作目录，其次项目根目录\nPROJECT_ROOT: Path = Path(__file__).resolve().parent\nCWD_ENV: Path = Path.cwd() / \".env\"\nENV_FILE: str = str(CWD_ENV if CWD_ENV.exists() else (PROJECT_ROOT / \".env\"))\n\n\nclass Settings(BaseSettings):\n    \"\"\"\n    全局配置；支持 .env 和环境变量自动加载。\n    变量名与原 config.py 大写一致，便于平滑过渡。\n    \"\"\"\n    # ================== Flask 服务器配置 ====================\n    HOST: str = Field(\"0.0.0.0\", description=\"BETTAFISH 主机地址，例如 0.0.0.0 或 127.0.0.1\")\n    PORT: int = Field(5000, description=\"Flask服务器端口号，默认5000\")\n\n    # ================== 日志配置 ====================\n    LOG_FILE: str = Field(\"logs/app.log\", description=\"日志文件路径\")\n\n    # ================== 报告输出配置 ====================\n    OUTPUT_DIR: str = Field(\"final_reports\", description=\"最终报告输出目录\")\n    CHAPTER_OUTPUT_DIR: str = Field(\"final_reports/chapters\", description=\"章节中间文件输出目录\")\n    DOCUMENT_IR_OUTPUT_DIR: str = Field(\"final_reports/ir\", description=\"文档中间表示输出目录\")\n\n    TEMPLATE_DIR: str = Field(\"ReportEngine/report_template\", description=\"报告模板目录\")\n    JSON_ERROR_LOG_DIR: str = Field(\"logs/json_errors\", description=\"JSON解析错误日志目录\")\n    CHAPTER_JSON_MAX_ATTEMPTS: int = Field(3, description=\"章节JSON生成最大尝试次数\")\n\n    # ====================== 数据库配置 ======================\n    DB_DIALECT: str = Field(\"postgresql\", description=\"数据库类型，可选 mysql 或 postgresql；请与其他连接信息同时配置\")\n    DB_HOST: str = Field(\"your_db_host\", description=\"数据库主机，例如localhost 或 127.0.0.1\")\n    DB_PORT: int = Field(3306, description=\"数据库端口号，默认为3306\")\n    DB_USER: str = Field(\"your_db_user\", description=\"数据库用户名\")\n    DB_PASSWORD: str = Field(\"your_db_password\", description=\"数据库密码\")\n    DB_NAME: str = Field(\"your_db_name\", description=\"数据库名称\")\n    DB_CHARSET: str = Field(\"utf8mb4\", description=\"数据库字符集，推荐utf8mb4，兼容emoji\")\n    \n    # ======================= LLM 相关 =======================\n    # 我们的LLM模型API赞助商有：https://aihubmix.com/?aff=8Ds9，提供了非常全面的模型api\n    \n    # Insight Agent（推荐Kimi，申请地址：https://platform.moonshot.cn/）\n    INSIGHT_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Insight Agent（推荐 kimi-k2，官方申请地址：https://platform.moonshot.cn/）API 密钥，用于主 LLM。🚩请先按推荐配置申请并跑通，再根据需要调整 KEY、BASE_URL 与 MODEL_NAME。\")\n    INSIGHT_ENGINE_BASE_URL: Optional[str] = Field(\"https://api.moonshot.cn/v1\", description=\"Insight Agent LLM BaseUrl，可根据厂商自定义\")\n    INSIGHT_ENGINE_MODEL_NAME: str = Field(\"kimi-k2-0711-preview\", description=\"Insight Agent LLM 模型名称，例如 kimi-k2-0711-preview\")\n    \n    # Media Agent（推荐Gemini，推荐中转厂商：https://aihubmix.com/?aff=8Ds9）\n    MEDIA_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Media Agent（推荐 gemini-2.5-pro，中转厂商申请地址：https://aihubmix.com/?aff=8Ds9）API 密钥\")\n    MEDIA_ENGINE_BASE_URL: Optional[str] = Field(\"https://aihubmix.com/v1\", description=\"Media Agent LLM BaseUrl，可根据中转服务调整\")\n    MEDIA_ENGINE_MODEL_NAME: str = Field(\"gemini-2.5-pro\", description=\"Media Agent LLM 模型名称，如 gemini-2.5-pro\")\n    \n    # Query Agent（推荐DeepSeek，申请地址：https://www.deepseek.com/）\n    QUERY_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Query Agent（推荐 deepseek，官方申请地址：https://platform.deepseek.com/）API 密钥\")\n    QUERY_ENGINE_BASE_URL: Optional[str] = Field(\"https://api.deepseek.com\", description=\"Query Agent LLM BaseUrl\")\n    QUERY_ENGINE_MODEL_NAME: str = Field(\"deepseek-chat\", description=\"Query Agent LLM 模型名称，如 deepseek-reasoner\")\n    \n    # Report Agent（推荐Gemini，推荐中转厂商：https://aihubmix.com/?aff=8Ds9）\n    REPORT_ENGINE_API_KEY: Optional[str] = Field(None, description=\"Report Agent（推荐 gemini-2.5-pro，中转厂商申请地址：https://aihubmix.com/?aff=8Ds9）API 密钥\")\n    REPORT_ENGINE_BASE_URL: Optional[str] = Field(\"https://aihubmix.com/v1\", description=\"Report Agent LLM BaseUrl，可根据中转服务调整\")\n    REPORT_ENGINE_MODEL_NAME: str = Field(\"gemini-2.5-pro\", description=\"Report Agent LLM 模型名称，如 gemini-2.5-pro\")\n\n    # MindSpider Agent（推荐Deepseek，官方申请地址：https://platform.deepseek.com/）\n    MINDSPIDER_API_KEY: Optional[str] = Field(None, description=\"MindSpider Agent（推荐 deepseek，官方申请地址：https://platform.deepseek.com/）API 密钥\")\n    MINDSPIDER_BASE_URL: Optional[str] = Field(None, description=\"MindSpider Agent BaseUrl，可按所选服务配置\")\n    MINDSPIDER_MODEL_NAME: Optional[str] = Field(None, description=\"MindSpider Agent 模型名称，例如 deepseek-reasoner\")\n    \n    # Forum Host（Qwen3最新模型，这里我使用了硅基流动这个平台，申请地址：https://cloud.siliconflow.cn/）\n    FORUM_HOST_API_KEY: Optional[str] = Field(None, description=\"Forum Host（推荐 qwen-plus，官方申请地址：https://www.aliyun.com/product/bailian）API 密钥\")\n    FORUM_HOST_BASE_URL: Optional[str] = Field(None, description=\"Forum Host LLM BaseUrl，可按所选服务配置\")\n    FORUM_HOST_MODEL_NAME: Optional[str] = Field(None, description=\"Forum Host LLM 模型名称，例如 qwen-plus\")\n    \n    # SQL keyword Optimizer（小参数Qwen3模型，这里我使用了硅基流动这个平台，申请地址：https://cloud.siliconflow.cn/）\n    KEYWORD_OPTIMIZER_API_KEY: Optional[str] = Field(None, description=\"SQL Keyword Optimizer（推荐 qwen-plus，官方申请地址：https://www.aliyun.com/product/bailian）API 密钥\")\n    KEYWORD_OPTIMIZER_BASE_URL: Optional[str] = Field(None, description=\"Keyword Optimizer BaseUrl，可按所选服务配置\")\n    KEYWORD_OPTIMIZER_MODEL_NAME: Optional[str] = Field(None, description=\"Keyword Optimizer LLM 模型名称，例如 qwen-plus\")\n    \n    # ================== GraphRAG 配置 ====================\n    GRAPHRAG_ENABLED: bool = Field(False, description=\"是否启用GraphRAG知识图谱功能（true/false）\")\n    GRAPHRAG_MAX_QUERIES: int = Field(3, description=\"GraphRAG每个章节生成前的最大查询次数\")\n    \n    # ================== 网络工具配置 ====================\n    # Tavily API（申请地址：https://www.tavily.com/）\n    TAVILY_API_KEY: Optional[str] = Field(None, description=\"Tavily API（申请地址：https://www.tavily.com/）API密钥，用于Tavily网络搜索\")\n\n    SEARCH_TOOL_TYPE: Literal[\"AnspireAPI\", \"BochaAPI\"] = Field(\"AnspireAPI\", description=\"网络搜索工具类型，支持BochaAPI或AnspireAPI两种，默认为AnspireAPI\")\n    # Bocha API（申请地址：https://open.bochaai.com/）\n    BOCHA_BASE_URL: Optional[str] = Field(\"https://api.bocha.cn/v1/ai-search\", description=\"Bocha AI 搜索BaseUrl或博查网页搜索BaseUrl\")\n    BOCHA_WEB_SEARCH_API_KEY: Optional[str] = Field(None, description=\"Bocha API（申请地址：https://open.bochaai.com/）API密钥，用于Bocha搜索\")\n\n    # Anspire AI Search API（申请地址：https://open.anspire.cn/?share_code=3E1FUOUH）\n    ANSPIRE_BASE_URL: Optional[str] = Field(\"https://plugin.anspire.cn/api/ntsearch/search\", description=\"Anspire AI 搜索BaseUrl\")\n    ANSPIRE_API_KEY: Optional[str] = Field(None, description=\"Anspire AI Search API（申请地址：https://open.anspire.cn/?share_code=3E1FUOUH）API密钥，用于Anspire搜索\")\n\n    \n    # ================== Insight Engine 搜索配置 ====================\n    DEFAULT_SEARCH_HOT_CONTENT_LIMIT: int = Field(100, description=\"热榜内容默认最大数\")\n    DEFAULT_SEARCH_TOPIC_GLOBALLY_LIMIT_PER_TABLE: int = Field(50, description=\"按表全局话题最大数\")\n    DEFAULT_SEARCH_TOPIC_BY_DATE_LIMIT_PER_TABLE: int = Field(100, description=\"按日期话题最大数\")\n    DEFAULT_GET_COMMENTS_FOR_TOPIC_LIMIT: int = Field(500, description=\"单话题评论最大数\")\n    DEFAULT_SEARCH_TOPIC_ON_PLATFORM_LIMIT: int = Field(200, description=\"平台搜索话题最大数\")\n    MAX_SEARCH_RESULTS_FOR_LLM: int = Field(0, description=\"供LLM用搜索结果最大数\")\n    MAX_HIGH_CONFIDENCE_SENTIMENT_RESULTS: int = Field(0, description=\"高置信度情感分析最大数\")\n    MAX_REFLECTIONS: int = Field(3, description=\"最大反思次数\")\n    MAX_PARAGRAPHS: int = Field(6, description=\"最大段落数\")\n    SEARCH_TIMEOUT: int = Field(240, description=\"单次搜索请求超时\")\n    MAX_CONTENT_LENGTH: int = Field(500000, description=\"搜索最大内容长度\")\n    \n    model_config = ConfigDict(\n        env_file=ENV_FILE,\n        env_prefix=\"\",\n        case_sensitive=False,\n        extra=\"allow\"\n    )\n\n\n# 创建全局配置实例\nsettings = Settings()\n\n\ndef reload_settings() -> Settings:\n    \"\"\"\n    重新加载配置\n    \n    从 .env 文件和环境变量重新加载配置，更新全局 settings 实例。\n    用于在运行时动态更新配置。\n    \n    Returns:\n        Settings: 新创建的配置实例\n    \"\"\"\n    \n    global settings\n    settings = Settings()\n    return settings\n"}}, "patch_report_engine_deepseek.py": {"359daa4d119dc1551d99bc2bffda7e7afd49cae6": {"t": 1785809880.3838441, "n": 2649, "h": "b6695d7e0f3a9d22", "c": "import os\n\ntarget_file = r\"D:\\my-web-app\\BettaFish\\report_engine_only.py\"\n\ntry:\n    with open(target_file, \"r\", encoding=\"utf-8\") as f:\n        content = f.read()\n\n    old_code = \"\"\"def build_agent_config(args) -> Settings:\n    \\\"\\\"\\\"基于 .env 配置并融合命令行覆盖项生成最终配置\\\"\\\"\\\"\n    config_overrides: Dict[str, Any] = {}\n\n    if args.graphrag_enabled is not None:\n        config_overrides[\"GRAPHRAG_ENABLED\"] = args.graphrag_enabled\n    if args.graphrag_max_queries is not None:\n        if args.graphrag_max_queries <= 0:\n            logger.warning(\"GRAPHRAG_MAX_QUERIES 必须大于 0，本次将继续使用 .env/默认值\")\n        else:\n            config_overrides[\"GRAPHRAG_MAX_QUERIES\"] = args.graphrag_max_queries\n\n    if not config_overrides:\n        return global_settings\n\n    return global_settings.model_copy(update=config_overrides)\"\"\"\n\n    new_code = \"\"\"def build_agent_config(args) -> Settings:\n    \\\"\\\"\\\"基于 .env 配置并融合命令行覆盖项生成最终配置\\\"\\\"\\\"\n    # 强制重新加载配置，确保获取最新的环境变量\n    try:\n        current_settings = Settings()\n    except Exception as e:\n        logger.warning(f\"重新加载配置失败，使用默认配置: {e}\")\n        current_settings = global_settings\n\n    config_overrides: Dict[str, Any] = {}\n\n    # 自动适配 DeepSeek (如果用户只配了 Key 没改 URL)\n    # 假设 DeepSeek Key 以 sk- 开头，且当前 Base URL 是默认的 aihubmix\n    api_key = current_settings.REPORT_ENGINE_API_KEY\n    base_url = current_settings.REPORT_ENGINE_BASE_URL\n    \n    if api_key and api_key.startswith(\"sk-\") and \"aihubmix\" in (base_url or \"\"):\n        logger.info(\"🔧 检测到 DeepSeek 风格 API Key，自动切换至 DeepSeek 官方节点\")\n        config_overrides[\"REPORT_ENGINE_BASE_URL\"] = \"https://api.deepseek.com\"\n        config_overrides[\"REPORT_ENGINE_MODEL_NAME\"] = \"deepseek-chat\"\n\n    if args.graphrag_enabled is not None:\n        config_overrides[\"GRAPHRAG_ENABLED\"] = args.graphrag_enabled\n    if args.graphrag_max_queries is not None:\n        if args.graphrag_max_queries <= 0:\n            logger.warning(\"GRAPHRAG_MAX_QUERIES 必须大于 0，本次将继续使用 .env/默认值\")\n        else:\n            config_overrides[\"GRAPHRAG_MAX_QUERIES\"] = args.graphrag_max_queries\n\n    if not config_overrides:\n        return current_settings\n\n    return current_settings.model_copy(update=config_overrides)\"\"\"\n\n    if old_code in content:\n        new_content = content.replace(old_code, new_code)\n        with open(target_file, \"w\", encoding=\"utf-8\") as f:\n            f.write(new_content)\n        print(\"Success: Patched report_engine_only.py\")\n    else:\n        print(\"Error: Could not find target code block. Content length:\", len(content))\n        # Optional: Print a snippet to verify\n        print(\"Snippet:\", content[539:600])\n\nexcept Exception as e:\n    print(f\"File operation failed: {e}\")\n"}}, "python_backend/news_engine.py": {"359daa4d119dc1551d99bc2bffda7e7afd49cae6": {"t": 1786067732.5665877, "n": 4002, "h": "38cf9897ec3dd8d8", "c": "# -*- coding: utf-8 -*-\nimport os\nimport json\nimport random\nimport requests\nfrom typing import List, Dict\nfrom tavily import TavilyClient\nfrom dotenv import load_dotenv\nfrom pathlib import Path\n\n# Load environment\ncurrent_file_path = Path(__file__).resolve()\nproject_root_dir = current_file_path.parent.parent\nload_dotenv(dotenv_path=project_root_dir / '.env')\n\nTAVILY_API_KEY = os.getenv(\"TAVILY_API_KEY\")\nDEEPSEEK_API_KEY = os.getenv(\"DEEPSEEK_API_KEY\")\n\ndef fetch_market_news(topic: str) -> List[Dict]:\n    \"\"\"\n    Fetch real-time news using Tavily or fallback to AI Synthesis.\n    Strictly returns JSON format: [{ \"title\": str, \"source\": str, \"sentiment\": str }]\n    \"\"\"\n    news_items = []\n    \n    # ATTEMPT 1: REAL TAVILY DATA\n    if TAVILY_API_KEY:\n        try:\n            client = TavilyClient(api_key=TAVILY_API_KEY)\n            response = client.search(query=f\"{topic} market news business\", search_depth=\"basic\", max_results=5)\n            \n            for res in response.get('results', []):\n                # Map URL to cleaner source name\n                url = res.get('url', '')\n                source = \"Web Source\"\n                if \"bloomberg\" in url: source = \"Bloomberg\"\n                elif \"reuters\" in url: source = \"Reuters\"\n                elif \"cnbc\" in url: source = \"CNBC\"\n                elif \"wsj\" in url: source = \"WSJ\"\n                elif \"techcrunch\" in url: source = \"TechCrunch\"\n                else: \n                    # Extract domain roughly\n                    try:\n                        source = url.split('//')[1].split('/')[0].replace('www.', '').split('.')[0].capitalize()\n                    except:\n                        source = \"Industry Web\"\n\n                news_items.append({\n                    \"title\": res.get('title', 'Market Update'),\n                    \"source\": source,\n                    \"sentiment\": \"Neutral\" # Default, hard to analyze without LLM\n                })\n        except Exception as e:\n            print(f\"⚠️ Tavily fetch failed: {e}\")\n\n    # If Tavily returned items, return them (maybe enhance sentiment later)\n    if news_items:\n        return news_items[:5]\n\n    # ATTEMPT 2: AI SYNTHESIS (FALLBACK)\n    # If no API key or fetch failed, generate realistic headlines based on the topic.\n    print(\"🤖 Engaging AI News Synthesis...\")\n    \n    if not DEEPSEEK_API_KEY:\n        # ABSOLUTE FALLBACK if no keys at all - MUST be removed in production\n        # But keeping it for now as a last resort to prevent crash, but warning loudly\n        print(\"⚠️ CRITICAL: No API keys found. Returning empty list instead of static mocks.\")\n        return []\n\n    system_prompt = \"\"\"\n    You are a financial news generator. \n    Generate 3 REALISTIC, PROFESSIONAL news headlines about the user's topic based on GENERAL MARKET KNOWLEDGE.\n    Do NOT use \"Mock\" or \"Placeholder\". Make it sound like a real headline from today.\n    RETURN ONLY JSON. Format: [{\"title\": \"...\", \"source\": \"...\", \"sentiment\": \"Bullish\"|\"Bearish\"|\"Neutral\", \"url\": \"#\"}]\n    Sources should be reputable (Bloomberg, Reuters, FT, WSJ, TechCrunch).\n    \"\"\"\n    \n    try:\n        api_url = \"https://api.deepseek.com/v1/chat/completions\"\n        headers = {\"Authorization\": f\"Bearer {DEEPSEEK_API_KEY}\", \"Content-Type\": \"application/json\"}\n        payload = {\n            \"model\": \"deepseek-chat\",\n            \"messages\": [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": f\"Topic: {topic}\"}],\n            \"temperature\": 0.7\n        }\n        \n        response = requests.post(api_url, json=payload, headers=headers, timeout=10)\n        content = response.json()[\"choices\"][0][\"message\"][\"content\"]\n        \n        # Parse JSON from LLM\n        # Use regex to find JSON array if mixed with text\n        import re\n        match = re.search(r'\\[.*\\]', content, re.DOTALL)\n        if match:\n            return json.loads(match.group(0))\n            \n    except Exception as e:\n        print(f\"❌ AI Synthesis failed: {e}\")\n\n    return []\n"}}}}