321 lines
12 KiB
Python
321 lines
12 KiB
Python
from typing import List, Optional, Dict, Any
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import strawberry
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# from packaging.version import Version
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import os
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ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
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@strawberry.type
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class APIBotConfig:
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"""机器人配置类"""
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INNER_VERSION: str # 配置文件内部版本号(toml为字符串)
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MAI_VERSION: str # 硬编码的版本信息
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# bot
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BOT_QQ: Optional[int] # 机器人QQ号
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BOT_NICKNAME: Optional[str] # 机器人昵称
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BOT_ALIAS_NAMES: List[str] # 机器人别名列表
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# group
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talk_allowed_groups: List[int] # 允许回复消息的群号列表
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talk_frequency_down_groups: List[int] # 降低回复频率的群号列表
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ban_user_id: List[int] # 禁止回复和读取消息的QQ号列表
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# personality
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personality_core: str # 人格核心特点描述
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personality_sides: List[str] # 人格细节描述列表
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# identity
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identity_detail: List[str] # 身份特点列表
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age: int # 年龄(岁)
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gender: str # 性别
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appearance: str # 外貌特征描述
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# platforms
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platforms: Dict[str, str] # 平台信息
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# chat
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allow_focus_mode: bool # 是否允许专注聊天状态
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base_normal_chat_num: int # 最多允许多少个群进行普通聊天
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base_focused_chat_num: int # 最多允许多少个群进行专注聊天
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observation_context_size: int # 观察到的最长上下文大小
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message_buffer: bool # 是否启用消息缓冲
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ban_words: List[str] # 禁止词列表
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ban_msgs_regex: List[str] # 禁止消息的正则表达式列表
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# normal_chat
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model_reasoning_probability: float # 推理模型概率
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model_normal_probability: float # 普通模型概率
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emoji_chance: float # 表情符号出现概率
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thinking_timeout: int # 思考超时时间
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willing_mode: str # 意愿模式
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response_willing_amplifier: float # 回复意愿放大器
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response_interested_rate_amplifier: float # 回复兴趣率放大器
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down_frequency_rate: float # 降低频率率
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emoji_response_penalty: float # 表情回复惩罚
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mentioned_bot_inevitable_reply: bool # 提及 bot 必然回复
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at_bot_inevitable_reply: bool # @bot 必然回复
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# focus_chat
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reply_trigger_threshold: float # 回复触发阈值
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default_decay_rate_per_second: float # 默认每秒衰减率
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# compressed
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compressed_length: int # 压缩长度
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compress_length_limit: int # 压缩长度限制
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# emoji
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max_emoji_num: int # 最大表情符号数量
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max_reach_deletion: bool # 达到最大数量时是否删除
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check_interval: int # 检查表情包的时间间隔(分钟)
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save_pic: bool # 是否保存图片
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save_emoji: bool # 是否保存表情包
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steal_emoji: bool # 是否偷取表情包
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enable_check: bool # 是否启用表情包过滤
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check_prompt: str # 表情包过滤要求
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# memory
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build_memory_interval: int # 记忆构建间隔
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build_memory_distribution: List[float] # 记忆构建分布
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build_memory_sample_num: int # 采样数量
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build_memory_sample_length: int # 采样长度
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memory_compress_rate: float # 记忆压缩率
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forget_memory_interval: int # 记忆遗忘间隔
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memory_forget_time: int # 记忆遗忘时间(小时)
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memory_forget_percentage: float # 记忆遗忘比例
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consolidate_memory_interval: int # 记忆整合间隔
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consolidation_similarity_threshold: float # 相似度阈值
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consolidation_check_percentage: float # 检查节点比例
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memory_ban_words: List[str] # 记忆禁止词列表
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# mood
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mood_update_interval: float # 情绪更新间隔
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mood_decay_rate: float # 情绪衰减率
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mood_intensity_factor: float # 情绪强度因子
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# keywords_reaction
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keywords_reaction_enable: bool # 是否启用关键词反应
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keywords_reaction_rules: List[Dict[str, Any]] # 关键词反应规则
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# chinese_typo
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chinese_typo_enable: bool # 是否启用中文错别字
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chinese_typo_error_rate: float # 中文错别字错误率
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chinese_typo_min_freq: int # 中文错别字最小频率
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chinese_typo_tone_error_rate: float # 中文错别字声调错误率
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chinese_typo_word_replace_rate: float # 中文错别字单词替换率
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# response_splitter
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enable_response_splitter: bool # 是否启用回复分割器
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response_max_length: int # 回复最大长度
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response_max_sentence_num: int # 回复最大句子数
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enable_kaomoji_protection: bool # 是否启用颜文字保护
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model_max_output_length: int # 模型最大输出长度
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# remote
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remote_enable: bool # 是否启用远程功能
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# experimental
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enable_friend_chat: bool # 是否启用好友聊天
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talk_allowed_private: List[int] # 允许私聊的QQ号列表
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pfc_chatting: bool # 是否启用PFC聊天
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# 模型配置
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llm_reasoning: Dict[str, Any] # 推理模型配置
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llm_normal: Dict[str, Any] # 普通模型配置
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llm_topic_judge: Dict[str, Any] # 主题判断模型配置
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summary: Dict[str, Any] # 总结模型配置
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vlm: Dict[str, Any] # VLM模型配置
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llm_heartflow: Dict[str, Any] # 心流模型配置
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llm_observation: Dict[str, Any] # 观察模型配置
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llm_sub_heartflow: Dict[str, Any] # 子心流模型配置
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llm_plan: Optional[Dict[str, Any]] # 计划模型配置
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embedding: Dict[str, Any] # 嵌入模型配置
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llm_PFC_action_planner: Optional[Dict[str, Any]] # PFC行动计划模型配置
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llm_PFC_chat: Optional[Dict[str, Any]] # PFC聊天模型配置
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llm_PFC_reply_checker: Optional[Dict[str, Any]] # PFC回复检查模型配置
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llm_tool_use: Optional[Dict[str, Any]] # 工具使用模型配置
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api_urls: Optional[Dict[str, str]] # API地址配置
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@staticmethod
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def validate_config(config: dict):
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"""
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校验传入的 toml 配置字典是否合法。
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:param config: toml库load后的配置字典
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:raises: ValueError, KeyError, TypeError
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"""
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# 检查主层级
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required_sections = [
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"inner",
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"bot",
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"groups",
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"personality",
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"identity",
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"platforms",
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"chat",
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"normal_chat",
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"focus_chat",
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"emoji",
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"memory",
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"mood",
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"keywords_reaction",
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"chinese_typo",
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"response_splitter",
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"remote",
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"experimental",
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"model",
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]
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for section in required_sections:
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if section not in config:
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raise KeyError(f"缺少配置段: [{section}]")
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# 检查部分关键字段
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if "version" not in config["inner"]:
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raise KeyError("缺少 inner.version 字段")
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if not isinstance(config["inner"]["version"], str):
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raise TypeError("inner.version 必须为字符串")
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if "qq" not in config["bot"]:
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raise KeyError("缺少 bot.qq 字段")
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if not isinstance(config["bot"]["qq"], int):
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raise TypeError("bot.qq 必须为整数")
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if "personality_core" not in config["personality"]:
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raise KeyError("缺少 personality.personality_core 字段")
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if not isinstance(config["personality"]["personality_core"], str):
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raise TypeError("personality.personality_core 必须为字符串")
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if "identity_detail" not in config["identity"]:
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raise KeyError("缺少 identity.identity_detail 字段")
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if not isinstance(config["identity"]["identity_detail"], list):
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raise TypeError("identity.identity_detail 必须为列表")
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# 可继续添加更多字段的类型和值检查
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# ...
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# 检查模型配置
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model_keys = [
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"llm_reasoning",
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"llm_normal",
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"llm_topic_judge",
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"summary",
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"vlm",
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"llm_heartflow",
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"llm_observation",
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"llm_sub_heartflow",
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"embedding",
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]
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if "model" not in config:
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raise KeyError("缺少 [model] 配置段")
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for key in model_keys:
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if key not in config["model"]:
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raise KeyError(f"缺少 model.{key} 配置")
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# 检查通过
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return True
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@strawberry.type
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class APIEnvConfig:
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"""环境变量配置"""
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HOST: str # 服务主机地址
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PORT: int # 服务端口
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PLUGINS: List[str] # 插件列表
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MONGODB_HOST: str # MongoDB 主机地址
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MONGODB_PORT: int # MongoDB 端口
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DATABASE_NAME: str # 数据库名称
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CHAT_ANY_WHERE_BASE_URL: str # ChatAnywhere 基础URL
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SILICONFLOW_BASE_URL: str # SiliconFlow 基础URL
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DEEP_SEEK_BASE_URL: str # DeepSeek 基础URL
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DEEP_SEEK_KEY: Optional[str] # DeepSeek API Key
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CHAT_ANY_WHERE_KEY: Optional[str] # ChatAnywhere API Key
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SILICONFLOW_KEY: Optional[str] # SiliconFlow API Key
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SIMPLE_OUTPUT: Optional[bool] # 是否简化输出
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CONSOLE_LOG_LEVEL: Optional[str] # 控制台日志等级
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FILE_LOG_LEVEL: Optional[str] # 文件日志等级
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DEFAULT_CONSOLE_LOG_LEVEL: Optional[str] # 默认控制台日志等级
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DEFAULT_FILE_LOG_LEVEL: Optional[str] # 默认文件日志等级
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@strawberry.field
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def get_env(self) -> str:
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return "env"
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@staticmethod
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def validate_config(config: dict):
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"""
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校验环境变量配置字典是否合法。
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:param config: 环境变量配置字典
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:raises: KeyError, TypeError
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"""
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required_fields = [
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"HOST",
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"PORT",
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"PLUGINS",
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"MONGODB_HOST",
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"MONGODB_PORT",
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"DATABASE_NAME",
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"CHAT_ANY_WHERE_BASE_URL",
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"SILICONFLOW_BASE_URL",
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"DEEP_SEEK_BASE_URL",
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]
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for field in required_fields:
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if field not in config:
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raise KeyError(f"缺少环境变量配置字段: {field}")
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if not isinstance(config["HOST"], str):
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raise TypeError("HOST 必须为字符串")
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if not isinstance(config["PORT"], int):
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raise TypeError("PORT 必须为整数")
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if not isinstance(config["PLUGINS"], list):
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raise TypeError("PLUGINS 必须为列表")
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if not isinstance(config["MONGODB_HOST"], str):
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raise TypeError("MONGODB_HOST 必须为字符串")
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if not isinstance(config["MONGODB_PORT"], int):
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raise TypeError("MONGODB_PORT 必须为整数")
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if not isinstance(config["DATABASE_NAME"], str):
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raise TypeError("DATABASE_NAME 必须为字符串")
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if not isinstance(config["CHAT_ANY_WHERE_BASE_URL"], str):
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raise TypeError("CHAT_ANY_WHERE_BASE_URL 必须为字符串")
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if not isinstance(config["SILICONFLOW_BASE_URL"], str):
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raise TypeError("SILICONFLOW_BASE_URL 必须为字符串")
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if not isinstance(config["DEEP_SEEK_BASE_URL"], str):
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raise TypeError("DEEP_SEEK_BASE_URL 必须为字符串")
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# 可选字段类型检查
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optional_str_fields = [
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"DEEP_SEEK_KEY",
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"CHAT_ANY_WHERE_KEY",
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"SILICONFLOW_KEY",
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"CONSOLE_LOG_LEVEL",
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"FILE_LOG_LEVEL",
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"DEFAULT_CONSOLE_LOG_LEVEL",
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"DEFAULT_FILE_LOG_LEVEL",
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]
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for field in optional_str_fields:
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if field in config and config[field] is not None and not isinstance(config[field], str):
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raise TypeError(f"{field} 必须为字符串或None")
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if (
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"SIMPLE_OUTPUT" in config
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and config["SIMPLE_OUTPUT"] is not None
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and not isinstance(config["SIMPLE_OUTPUT"], bool)
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):
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raise TypeError("SIMPLE_OUTPUT 必须为布尔值或None")
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# 检查通过
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return True
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print("当前路径:")
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print(ROOT_PATH)
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