from typing import Literal, Optional, List from pydantic import Field from src.config.config_base import ValidatedConfigBase """ 须知： 1. 本文件中记录了所有的配置项 2. 重要的配置类继承自ValidatedConfigBase进行Pydantic验证 3. 所有新增的class都应在config.py中的Config类中添加字段 4. 对于新增的字段，若为可选项，则应在其后添加field()并设置default_factory或default """ class DatabaseConfig(ValidatedConfigBase): """数据库配置类""" database_type: Literal["sqlite", "mysql"] = Field(default="sqlite", description="数据库类型") sqlite_path: str = Field(default="data/MaiBot.db", description="SQLite数据库文件路径") mysql_host: str = Field(default="localhost", description="MySQL服务器地址") mysql_port: int = Field(default=3306, ge=1, le=65535, description="MySQL服务器端口") mysql_database: str = Field(default="maibot", description="MySQL数据库名") mysql_user: str = Field(default="root", description="MySQL用户名") mysql_password: str = Field(default="", description="MySQL密码") mysql_charset: str = Field(default="utf8mb4", description="MySQL字符集") mysql_unix_socket: str = Field(default="", description="MySQL Unix套接字路径") mysql_ssl_mode: Literal["DISABLED", "PREFERRED", "REQUIRED", "VERIFY_CA", "VERIFY_IDENTITY"] = Field(default="DISABLED", description="SSL模式") mysql_ssl_ca: str = Field(default="", description="SSL CA证书路径") mysql_ssl_cert: str = Field(default="", description="SSL客户端证书路径") mysql_ssl_key: str = Field(default="", description="SSL客户端密钥路径") mysql_autocommit: bool = Field(default=True, description="自动提交事务") mysql_sql_mode: str = Field(default="TRADITIONAL", description="SQL模式") connection_pool_size: int = Field(default=10, ge=1, description="连接池大小") connection_timeout: int = Field(default=10, ge=1, description="连接超时时间") class BotConfig(ValidatedConfigBase): """QQ机器人配置类""" platform: str = Field(..., description="平台") qq_account: int = Field(..., description="QQ账号") nickname: str = Field(..., description="昵称") alias_names: List[str] = Field(default_factory=list, description="别名列表") class PersonalityConfig(ValidatedConfigBase): """人格配置类""" personality_core: str = Field(..., description="核心人格") personality_side: str = Field(..., description="人格侧写") identity: str = Field(default="", description="身份特征") reply_style: str = Field(default="", description="表达风格") prompt_mode: Literal["s4u", "normal"] = Field(default="s4u", description="Prompt模式") compress_personality: bool = Field(default=True, description="是否压缩人格") compress_identity: bool = Field(default=True, description="是否压缩身份") class RelationshipConfig(ValidatedConfigBase): """关系配置类""" enable_relationship: bool = Field(default=True, description="是否启用关系") relation_frequency: float = Field(default=1.0, description="关系频率") class ChatConfig(ValidatedConfigBase): """聊天配置类""" max_context_size: int = Field(default=18, description="最大上下文大小") replyer_random_probability: float = Field(default=0.5, description="回复者随机概率") thinking_timeout: int = Field(default=40, description="思考超时时间") talk_frequency: float = Field(default=1.0, description="聊天频率") mentioned_bot_inevitable_reply: bool = Field(default=False, description="提到机器人的必然回复") at_bot_inevitable_reply: bool = Field(default=False, description="@机器人的必然回复") talk_frequency_adjust: list[list[str]] = Field(default_factory=lambda: [], description="聊天频率调整") focus_value: float = Field(default=1.0, description="专注值") force_focus_private: bool = Field(default=False, description="强制专注私聊") group_chat_mode: Literal["auto", "normal", "focus"] = Field(default="auto", description="群聊模式") timestamp_display_mode: Literal["normal", "normal_no_YMD", "relative"] = Field(default="normal_no_YMD", description="时间戳显示模式") enable_proactive_thinking: bool = Field(default=False, description="启用主动思考") proactive_thinking_interval: int = Field(default=1500, description="主动思考间隔") The_scope_that_proactive_thinking_can_trigger: str = Field(default="all", description="主动思考可以触发的范围") proactive_thinking_in_private: bool = Field(default=True, description="主动思考可以在私聊里面启用") proactive_thinking_in_group: bool = Field(default=True, description="主动思考可以在群聊里面启用") proactive_thinking_enable_ids: List[int] = Field(default_factory=list, description="启用主动思考的范围，不区分群聊和私聊，为空则不限制") delta_sigma: int = Field(default=120, description="采用正态分布随机时间间隔") enable_ids: List[int] = Field(default_factory=lambda: [123456, 234567], description="启用主动思考的范围，不区分群聊和私聊，为空则不限制") proactive_thinking_prompt_template: str = Field(default="""现在群里面已经隔了{time}没有人发送消息了，请你结合上下文以及群聊里面之前聊过的话题和你的人设来决定要不要主动发送消息，你可以选择： 1. 继续保持沉默（当{time}以前已经结束了一个话题并且你不想挑起新话题时） 2. 选择回复（当{time}以前你发送了一条消息且没有人回复你时、你想主动挑起一个话题时） 请根据当前情况做出选择。如果选择回复，请直接发送你想说的内容；如果选择保持沉默，请只回复"沉默"（注意：这个词不会被发送到群聊中）。""", description="主动思考提示模板") def get_current_talk_frequency(self, chat_stream_id: Optional[str] = None) -> float: """ 根据当前时间和聊天流获取对应的 talk_frequency Args: chat_stream_id: 聊天流ID，格式为 "platform:chat_id:type" Returns: float: 对应的频率值 """ if not self.talk_frequency_adjust: return self.talk_frequency # 优先检查聊天流特定的配置 if chat_stream_id: stream_frequency = self._get_stream_specific_frequency(chat_stream_id) if stream_frequency is not None: return stream_frequency # 检查全局时段配置（第一个元素为空字符串的配置） global_frequency = self._get_global_frequency() if global_frequency is not None: return global_frequency # 如果都没有匹配，返回默认值 return self.talk_frequency def _get_time_based_frequency(self, time_freq_list: list[str]) -> Optional[float]: """ 根据时间配置列表获取当前时段的频率 Args: time_freq_list: 时间频率配置列表，格式为 ["HH:MM,frequency", ...] Returns: float: 频率值，如果没有配置则返回 None """ from datetime import datetime current_time = datetime.now().strftime("%H:%M") current_hour, current_minute = map(int, current_time.split(":")) current_minutes = current_hour * 60 + current_minute # 解析时间频率配置 time_freq_pairs = [] for time_freq_str in time_freq_list: try: time_str, freq_str = time_freq_str.split(",") hour, minute = map(int, time_str.split(":")) frequency = float(freq_str) minutes = hour * 60 + minute time_freq_pairs.append((minutes, frequency)) except (ValueError, IndexError): continue if not time_freq_pairs: return None # 按时间排序 time_freq_pairs.sort(key=lambda x: x[0]) # 查找当前时间对应的频率 current_frequency = None for minutes, frequency in time_freq_pairs: if current_minutes >= minutes: current_frequency = frequency else: break # 如果当前时间在所有配置时间之前，使用最后一个时间段的频率（跨天逻辑） if current_frequency is None and time_freq_pairs: current_frequency = time_freq_pairs[-1][1] return current_frequency def _get_stream_specific_frequency(self, chat_stream_id: str): """ 获取特定聊天流在当前时间的频率 Args: chat_stream_id: 聊天流ID（哈希值） Returns: float: 频率值，如果没有配置则返回 None """ # 查找匹配的聊天流配置 for config_item in self.talk_frequency_adjust: if not config_item or len(config_item) < 2: continue stream_config_str = config_item[0] # 例如 "qq:1026294844:group" # 解析配置字符串并生成对应的 chat_id config_chat_id = self._parse_stream_config_to_chat_id(stream_config_str) if config_chat_id is None: continue # 比较生成的 chat_id if config_chat_id != chat_stream_id: continue # 使用通用的时间频率解析方法 return self._get_time_based_frequency(config_item[1:]) return None def _parse_stream_config_to_chat_id(self, stream_config_str: str) -> Optional[str]: """ 解析流配置字符串并生成对应的 chat_id Args: stream_config_str: 格式为 "platform:id:type" 的字符串 Returns: str: 生成的 chat_id，如果解析失败则返回 None """ try: parts = stream_config_str.split(":") if len(parts) != 3: return None platform = parts[0] id_str = parts[1] stream_type = parts[2] # 判断是否为群聊 is_group = stream_type == "group" # 使用与 ChatStream.get_stream_id 相同的逻辑生成 chat_id import hashlib if is_group: components = [platform, str(id_str)] else: components = [platform, str(id_str), "private"] key = "_".join(components) return hashlib.md5(key.encode()).hexdigest() except (ValueError, IndexError): return None def _get_global_frequency(self) -> Optional[float]: """ 获取全局默认频率配置 Returns: float: 频率值，如果没有配置则返回 None """ for config_item in self.talk_frequency_adjust: if not config_item or len(config_item) < 2: continue # 检查是否为全局默认配置（第一个元素为空字符串） if config_item[0] == "": return self._get_time_based_frequency(config_item[1:]) return None class MessageReceiveConfig(ValidatedConfigBase): """消息接收配置类""" ban_words: List[str] = Field(default_factory=lambda: list(), description="禁用词列表") ban_msgs_regex: List[str] = Field(default_factory=lambda: list(), description="禁用消息正则列表") class NormalChatConfig(ValidatedConfigBase): """普通聊天配置类""" willing_mode: str = Field(default="classical", description="意愿模式") class ExpressionConfig(ValidatedConfigBase): """表达配置类""" expression_learning: list[list] = Field(default_factory=lambda: [], description="表达学习") expression_groups: list[list[str]] = Field(default_factory=list, description="表达组") def _parse_stream_config_to_chat_id(self, stream_config_str: str) -> Optional[str]: """ 解析流配置字符串并生成对应的 chat_id Args: stream_config_str: 格式为 "platform:id:type" 的字符串 Returns: str: 生成的 chat_id，如果解析失败则返回 None """ try: parts = stream_config_str.split(":") if len(parts) != 3: return None platform = parts[0] id_str = parts[1] stream_type = parts[2] # 判断是否为群聊 is_group = stream_type == "group" # 使用与 ChatStream.get_stream_id 相同的逻辑生成 chat_id import hashlib if is_group: components = [platform, str(id_str)] else: components = [platform, str(id_str), "private"] key = "_".join(components) return hashlib.md5(key.encode()).hexdigest() except (ValueError, IndexError): return None def get_expression_config_for_chat(self, chat_stream_id: Optional[str] = None) -> tuple[bool, bool, float]: """ 根据聊天流ID获取表达配置 Args: chat_stream_id: 聊天流ID，格式为哈希值 Returns: tuple: (是否使用表达, 是否学习表达, 学习间隔) """ if not self.expression_learning: # 如果没有配置，使用默认值：启用表达，启用学习，300秒间隔 return True, True, 300 # 优先检查聊天流特定的配置 if chat_stream_id: specific_config = self._get_stream_specific_config(chat_stream_id) if specific_config is not None: return specific_config # 检查全局配置（第一个元素为空字符串的配置） global_config = self._get_global_config() if global_config is not None: return global_config # 如果都没有匹配，返回默认值 return True, True, 300 def _get_stream_specific_config(self, chat_stream_id: str) -> Optional[tuple[bool, bool, float]]: """ 获取特定聊天流的表达配置 Args: chat_stream_id: 聊天流ID（哈希值） Returns: tuple: (是否使用表达, 是否学习表达, 学习间隔)，如果没有配置则返回 None """ for config_item in self.expression_learning: if not config_item or len(config_item) < 4: continue stream_config_str = config_item[0] # 例如 "qq:1026294844:group" # 如果是空字符串，跳过（这是全局配置） if stream_config_str == "": continue # 解析配置字符串并生成对应的 chat_id config_chat_id = self._parse_stream_config_to_chat_id(stream_config_str) if config_chat_id is None: continue # 比较生成的 chat_id if config_chat_id != chat_stream_id: continue # 解析配置 try: use_expression = config_item[1].lower() == "enable" enable_learning = config_item[2].lower() == "enable" learning_intensity = float(config_item[3]) return use_expression, enable_learning, learning_intensity except (ValueError, IndexError): continue return None def _get_global_config(self) -> Optional[tuple[bool, bool, float]]: """ 获取全局表达配置 Returns: tuple: (是否使用表达, 是否学习表达, 学习间隔)，如果没有配置则返回 None """ for config_item in self.expression_learning: if not config_item or len(config_item) < 4: continue # 检查是否为全局配置（第一个元素为空字符串） if config_item[0] == "": try: use_expression = config_item[1].lower() == "enable" enable_learning = config_item[2].lower() == "enable" learning_intensity = float(config_item[3]) return use_expression, enable_learning, learning_intensity except (ValueError, IndexError): continue return None class ToolConfig(ValidatedConfigBase): """工具配置类""" enable_tool: bool = Field(default=False, description="启用工具") class VoiceConfig(ValidatedConfigBase): """语音识别配置类""" enable_asr: bool = Field(default=False, description="启用语音识别") class EmojiConfig(ValidatedConfigBase): """表情包配置类""" emoji_chance: float = Field(default=0.6, description="表情包出现概率") emoji_activate_type: str = Field(default="random", description="表情包激活类型") max_reg_num: int = Field(default=200, description="最大表情包数量") do_replace: bool = Field(default=True, description="是否替换表情包") check_interval: int = Field(default=120, description="检查间隔") steal_emoji: bool = Field(default=True, description="是否偷取表情包") content_filtration: bool = Field(default=False, description="内容过滤") filtration_prompt: str = Field(default="符合公序良俗", description="过滤提示") enable_emotion_analysis: bool = Field(default=True, description="启用情感分析") class MemoryConfig(ValidatedConfigBase): """记忆配置类""" enable_memory: bool = Field(default=True, description="启用记忆") memory_build_interval: int = Field(default=600, description="记忆构建间隔") memory_build_distribution: list[float] = Field(default_factory=lambda: [6.0, 3.0, 0.6, 32.0, 12.0, 0.4], description="记忆构建分布") memory_build_sample_num: int = Field(default=8, description="记忆构建样本数量") memory_build_sample_length: int = Field(default=40, description="记忆构建样本长度") memory_compress_rate: float = Field(default=0.1, description="记忆压缩率") forget_memory_interval: int = Field(default=1000, description="遗忘记忆间隔") memory_forget_time: int = Field(default=24, description="记忆遗忘时间") memory_forget_percentage: float = Field(default=0.01, description="记忆遗忘百分比") consolidate_memory_interval: int = Field(default=1000, description="记忆巩固间隔") consolidation_similarity_threshold: float = Field(default=0.7, description="巩固相似性阈值") consolidate_memory_percentage: float = Field(default=0.01, description="巩固记忆百分比") memory_ban_words: list[str] = Field(default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"], description="记忆禁用词") enable_instant_memory: bool = Field(default=True, description="启用即时记忆") enable_llm_instant_memory: bool = Field(default=True, description="启用基于LLM的瞬时记忆") enable_vector_instant_memory: bool = Field(default=True, description="启用基于向量的瞬时记忆") class MoodConfig(ValidatedConfigBase): """情绪配置类""" enable_mood: bool = Field(default=False, description="启用情绪") mood_update_threshold: float = Field(default=1.0, description="情绪更新阈值") class KeywordRuleConfig(ValidatedConfigBase): """关键词规则配置类""" keywords: list[str] = Field(default_factory=lambda: [], description="关键词列表") regex: list[str] = Field(default_factory=lambda: [], description="正则表达式列表") reaction: str = Field(default="", description="反应内容") def __post_init__(self): import re if not self.keywords and not self.regex: raise ValueError("关键词规则必须至少包含keywords或regex中的一个") if not self.reaction: raise ValueError("关键词规则必须包含reaction") for pattern in self.regex: try: re.compile(pattern) except re.error as e: raise ValueError(f"无效的正则表达式 '{pattern}': {str(e)}") from e class KeywordReactionConfig(ValidatedConfigBase): """关键词配置类""" keyword_rules: list[KeywordRuleConfig] = Field(default_factory=lambda: [], description="关键词规则列表") regex_rules: list[KeywordRuleConfig] = Field(default_factory=lambda: [], description="正则表达式规则列表") class CustomPromptConfig(ValidatedConfigBase): """自定义提示词配置类""" image_prompt: str = Field(default="", description="图片提示词") planner_custom_prompt_enable: bool = Field(default=False, description="启用规划器自定义提示词") planner_custom_prompt_content: str = Field(default="", description="规划器自定义提示词内容") class ResponsePostProcessConfig(ValidatedConfigBase): """回复后处理配置类""" enable_response_post_process: bool = Field(default=True, description="启用回复后处理") class ChineseTypoConfig(ValidatedConfigBase): """中文错别字配置类""" enable: bool = Field(default=True, description="启用") error_rate: float = Field(default=0.01, description="错误率") min_freq: int = Field(default=9, description="最小频率") tone_error_rate: float = Field(default=0.1, description="语调错误率") word_replace_rate: float = Field(default=0.006, description="词语替换率") class ResponseSplitterConfig(ValidatedConfigBase): """回复分割器配置类""" enable: bool = Field(default=True, description="启用") max_length: int = Field(default=256, description="最大长度") max_sentence_num: int = Field(default=3, description="最大句子数") enable_kaomoji_protection: bool = Field(default=False, description="启用颜文字保护") class TelemetryConfig(ValidatedConfigBase): """遥测配置类""" enable: bool = Field(default=True, description="启用") class DebugConfig(ValidatedConfigBase): """调试配置类""" show_prompt: bool = Field(default=False, description="显示提示") class ExperimentalConfig(ValidatedConfigBase): """实验功能配置类""" enable_friend_chat: bool = Field(default=False, description="启用好友聊天") pfc_chatting: bool = Field(default=False, description="启用PFC聊天") class MaimMessageConfig(ValidatedConfigBase): """maim_message配置类""" use_custom: bool = Field(default=False, description="启用自定义") host: str = Field(default="127.0.0.1", description="主机") port: int = Field(default=8090, description="端口") mode: Literal["ws", "tcp"] = Field(default="ws", description="模式") use_wss: bool = Field(default=False, description="启用WSS") cert_file: str = Field(default="", description="证书文件") key_file: str = Field(default="", description="密钥文件") auth_token: list[str] = Field(default_factory=lambda: [], description="认证令牌列表") class LPMMKnowledgeConfig(ValidatedConfigBase): """LPMM知识库配置类""" enable: bool = Field(default=True, description="启用") rag_synonym_search_top_k: int = Field(default=10, description="RAG同义词搜索Top K") rag_synonym_threshold: float = Field(default=0.8, description="RAG同义词阈值") info_extraction_workers: int = Field(default=3, description="信息提取工作线程数") qa_relation_search_top_k: int = Field(default=10, description="QA关系搜索Top K") qa_relation_threshold: float = Field(default=0.75, description="QA关系阈值") qa_paragraph_search_top_k: int = Field(default=1000, description="QA段落搜索Top K") qa_paragraph_node_weight: float = Field(default=0.05, description="QA段落节点权重") qa_ent_filter_top_k: int = Field(default=10, description="QA实体过滤Top K") qa_ppr_damping: float = Field(default=0.8, description="QA PPR阻尼系数") qa_res_top_k: int = Field(default=10, description="QA结果Top K") embedding_dimension: int = Field(default=1024, description="嵌入维度") class ScheduleConfig(ValidatedConfigBase): """日程配置类""" enable: bool = Field(default=True, description="启用") guidelines: Optional[str] = Field(default=None, description="指导方针") enable_is_sleep: bool = Field(default=True, description="让AI会根据日程表睡觉和苏醒") class DependencyManagementConfig(ValidatedConfigBase): """插件Python依赖管理配置类""" auto_install: bool = Field(default=True, description="启用自动安装") auto_install_timeout: int = Field(default=300, description="自动安装超时时间") use_mirror: bool = Field(default=False, description="使用镜像") mirror_url: str = Field(default="", description="镜像URL") use_proxy: bool = Field(default=False, description="使用代理") proxy_url: str = Field(default="", description="代理URL") pip_options: list[str] = Field(default_factory=lambda: ["--no-warn-script-location", "--disable-pip-version-check"], description="Pip选项") prompt_before_install: bool = Field(default=False, description="安装前提示") install_log_level: str = Field(default="INFO", description="安装日志级别") class ExaConfig(ValidatedConfigBase): """EXA搜索引擎配置类""" api_keys: list[str] = Field(default_factory=lambda: [], description="API密钥列表") class TavilyConfig(ValidatedConfigBase): """Tavily搜索引擎配置类""" api_keys: list[str] = Field(default_factory=lambda: [], description="API密钥列表") class VideoAnalysisConfig(ValidatedConfigBase): """视频分析配置类""" enable: bool = Field(default=True, description="启用") analysis_mode: str = Field(default="batch_frames", description="分析模式") max_frames: int = Field(default=8, description="最大帧数") frame_quality: int = Field(default=85, description="帧质量") max_image_size: int = Field(default=800, description="最大图像大小") enable_frame_timing: bool = Field(default=True, description="启用帧时间") batch_analysis_prompt: str = Field(default="", description="批量分析提示") class WebSearchConfig(ValidatedConfigBase): """联网搜索组件配置类""" enable_web_search_tool: bool = Field(default=True, description="启用网络搜索工具") enable_url_tool: bool = Field(default=True, description="启用URL工具") enabled_engines: list[str] = Field(default_factory=lambda: ["ddg"], description="启用的搜索引擎") search_strategy: Literal["fallback","single","parallel"] = Field(default="single", description="搜索策略") class AntiPromptInjectionConfig(ValidatedConfigBase): """LLM反注入系统配置类""" enabled: bool = Field(default=True, description="启用") enabled_LLM: bool = Field(default=True, description="启用LLM") enabled_rules: bool = Field(default=True, description="启用规则") process_mode: str = Field(default="lenient", description="处理模式") whitelist: list[list[str]] = Field(default_factory=list, description="白名单") llm_detection_enabled: bool = Field(default=True, description="启用LLM检测") llm_model_name: str = Field(default="anti_injection", description="LLM模型名称") llm_detection_threshold: float = Field(default=0.7, description="LLM检测阈值") cache_enabled: bool = Field(default=True, description="启用缓存") cache_ttl: int = Field(default=3600, description="缓存TTL") max_message_length: int = Field(default=4096, description="最大消息长度") stats_enabled: bool = Field(default=True, description="启用统计信息") auto_ban_enabled: bool = Field(default=True, description="启用自动禁用") auto_ban_violation_threshold: int = Field(default=3, description="自动禁用违规阈值") auto_ban_duration_hours: int = Field(default=2, description="自动禁用持续时间（小时）") shield_prefix: str = Field(default="🛡️ ", description="保护前缀") shield_suffix: str = Field(default=" 🛡️", description="保护后缀") class PluginsConfig(ValidatedConfigBase): """插件配置""" centralized_config: bool = Field(default=True, description="是否启用插件配置集中化管理") class WakeUpSystemConfig(ValidatedConfigBase): """唤醒度系统配置类""" enable: bool = Field(default=True, description="是否启用唤醒度系统") wakeup_threshold: float = Field(default=15.0, ge=1.0, description="唤醒阈值，达到此值时会被唤醒") private_message_increment: float = Field(default=3.0, ge=0.1, description="私聊消息增加的唤醒度") group_mention_increment: float = Field(default=2.0, ge=0.1, description="群聊艾特增加的唤醒度") decay_rate: float = Field(default=0.2, ge=0.0, description="每次衰减的唤醒度数值") decay_interval: float = Field(default=30.0, ge=1.0, description="唤醒度衰减间隔(秒)") angry_duration: float = Field(default=300.0, ge=10.0, description="愤怒状态持续时间(秒)") angry_prompt: str = Field(default="你被人吵醒了非常生气，说话带着怒气", description="被吵醒后的愤怒提示词") class MonthlyPlanSystemConfig(ValidatedConfigBase): """月层计划系统配置类""" enable: bool = Field(default=True, description="是否启用本功能") generation_threshold: int = Field(default=10, ge=0, description="启动时，如果当月计划少于此数量，则触发LLM生成") plans_per_generation: int = Field(default=5, ge=1, description="每次调用LLM期望生成的计划数量") deletion_probability_on_use: float = Field(default=0.5, ge=0.0, le=1.0, description="计划被使用后，被删除的概率") max_plans_per_month: int = Field(default=20, ge=1, description="每个月允许存在的最大计划数量") class ContextGroup(ValidatedConfigBase): """上下文共享组配置""" name: str = Field(..., description="共享组的名称") chat_ids: List[str] = Field(..., description="属于该组的聊天ID列表") class CrossContextConfig(ValidatedConfigBase): """跨群聊上下文共享配置""" enable: bool = Field(default=False, description="是否启用跨群聊上下文共享功能") groups: List[ContextGroup] = Field(default_factory=list, description="上下文共享组列表") class PermissionConfig(ValidatedConfigBase): """权限系统配置类""" # Master用户配置（拥有最高权限，无视所有权限节点） master_users: List[List[str]] = Field(default_factory=list, description="Master用户列表，格式: [[platform, user_id], ...]")