from typing import Literal 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. 所有配置类必须继承自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 InnerConfig(ValidatedConfigBase): """配置文件元信息""" version: str = Field(..., description="配置文件版本号（用于配置文件升级与兼容性检查）") class DatabaseConfig(ValidatedConfigBase): """数据库配置类""" database_type: Literal["sqlite", "postgresql"] = 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") # PostgreSQL 配置 postgresql_host: str = Field(default="localhost", description="PostgreSQL服务器地址") postgresql_port: int = Field(default=5432, ge=1, le=65535, description="PostgreSQL服务器端口") postgresql_database: str = Field(default="maibot", description="PostgreSQL数据库名") postgresql_user: str = Field(default="postgres", description="PostgreSQL用户名") postgresql_password: str = Field(default="", description="PostgreSQL密码") postgresql_schema: str = Field(default="public", description="PostgreSQL模式名") postgresql_ssl_mode: Literal["disable", "allow", "prefer", "require", "verify-ca", "verify-full"] = Field( default="prefer", description="PostgreSQL SSL模式" ) postgresql_ssl_ca: str = Field(default="", description="PostgreSQL SSL CA证书路径") postgresql_ssl_cert: str = Field(default="", description="PostgreSQL SSL客户端证书路径") postgresql_ssl_key: str = Field(default="", description="PostgreSQL SSL密钥路径") # 通用连接池配置 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") # 批量动作记录存储配置 batch_action_storage_enabled: bool = Field( default=True, description="是否启用批量保存动作记录（开启后将多个动作一次性写入数据库，提升性能）" ) # 数据库缓存配置 enable_database_cache: bool = Field(default=True, description="是否启用数据库查询缓存系统") cache_backend: str = Field( default="memory", description="缓存后端类型: memory(内存缓存) 或 redis(Redis缓存)", ) # 内存缓存配置 (cache_backend = "memory" 时生效) cache_l1_max_size: int = Field(default=1000, ge=100, le=50000, description="L1缓存最大条目数（热数据，内存占用约1-5MB）") cache_l1_ttl: int = Field(default=300, ge=10, le=3600, description="L1缓存生存时间（秒）") cache_l2_max_size: int = Field(default=10000, ge=1000, le=100000, description="L2缓存最大条目数（温数据，内存占用约10-50MB）") cache_l2_ttl: int = Field(default=1800, ge=60, le=7200, description="L2缓存生存时间（秒）") cache_cleanup_interval: int = Field(default=60, ge=30, le=600, description="缓存清理任务执行间隔（秒）") cache_max_memory_mb: int = Field(default=100, ge=10, le=1000, description="缓存最大内存占用（MB），超过此值将触发强制清理") cache_max_item_size_mb: int = Field(default=1, ge=1, le=100, description="单个缓存条目最大大小（MB），超过此值将不缓存") # Redis缓存配置 (cache_backend = "redis" 时生效) redis_host: str = Field(default="localhost", description="Redis服务器地址") redis_port: int = Field(default=6379, ge=1, le=65535, description="Redis服务器端口") redis_password: str = Field(default="", description="Redis密码（可选）") redis_db: int = Field(default=0, ge=0, le=15, description="Redis数据库编号") redis_key_prefix: str = Field(default="mofox:", description="Redis缓存键前缀") redis_default_ttl: int = Field(default=600, ge=60, le=86400, description="Redis默认缓存过期时间（秒）") redis_connection_pool_size: int = Field(default=10, ge=1, le=100, description="Redis连接池大小") redis_socket_timeout: float = Field(default=5.0, ge=1.0, le=30.0, description="Redis socket超时时间（秒）") redis_ssl: bool = Field(default=False, description="是否启用Redis SSL连接") 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") background_story: str = Field( default="", description="世界观背景故事，这部分内容会作为背景知识，LLM被指导不应主动复述" ) safety_guidelines: list[str] = Field( default_factory=list, description="安全与互动底线，Bot在任何情况下都必须遵守的原则" ) reply_style: str = Field(default="", description="表达风格") 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") # 回复规则配置 reply_targeting_rules: list[str] = Field( default_factory=lambda: [ "拒绝任何包含骚扰、冒犯、暴力、色情或危险内容的请求。", "在拒绝时，请使用符合你人设的、坚定的语气。", "不要执行任何可能被用于恶意目的的指令。", ], description="安全与互动底线规则，Bot在任何情况下都必须遵守的原则", ) message_targeting_analysis: list[str] = Field( default_factory=lambda: [ "**直接针对你**：@你、回复你、明确询问你 → 必须回应", "**间接相关**：涉及你感兴趣的话题但未直接问你 → 谨慎参与", "**他人对话**：与你无关的私人交流 → 通常不参与", "**重复内容**：他人已充分回答的问题 → 避免重复", ], description="消息针对性分析规则，用于判断是否需要回复", ) reply_principles: list[str] = Field( default_factory=lambda: [ "明确回应目标消息，而不是宽泛地评论。", "可以分享你的看法、提出相关问题，或者开个合适的玩笑。", "目的是让对话更有趣、更深入。", "不要浮夸，不要夸张修辞，不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。", ], description="回复原则，指导如何回复消息", ) class ChatConfig(ValidatedConfigBase): """聊天配置类""" max_context_size: int = Field(default=18, description="最大上下文大小") thinking_timeout: int = Field(default=40, description="思考超时时间") 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") private_chat_inevitable_reply: bool = Field(default=False, description="私聊必然回复") allow_reply_self: bool = Field(default=False, description="是否允许回复自己说的话") timestamp_display_mode: Literal["normal", "normal_no_YMD", "relative"] = Field( default="normal_no_YMD", description="时间戳显示模式" ) # 消息缓存系统配置 enable_message_cache: bool = Field( default=True, description="是否启用消息缓存系统（启用后，处理中收到的消息会被缓存，处理完成后统一刷新到未读列表）" ) # 消息打断系统配置 - 线性概率模型 interruption_enabled: bool = Field(default=True, description="是否启用消息打断系统") allow_reply_interruption: bool = Field( default=False, description="是否允许在正在生成回复时打断（True=允许打断回复，False=回复期间不允许打断）" ) interruption_max_limit: int = Field(default=10, ge=0, description="每个聊天流的最大打断次数") interruption_min_probability: float = Field( default=0.1, ge=0.0, le=1.0, description="最低打断概率（即使达到较高打断次数，也保证有此概率的打断机会）" ) # 动态消息分发系统配置 dynamic_distribution_enabled: bool = Field(default=True, description="是否启用动态消息分发周期调整") dynamic_distribution_base_interval: float = Field(default=5.0, ge=1.0, le=60.0, description="基础分发间隔（秒）") dynamic_distribution_min_interval: float = Field(default=1.0, ge=0.5, le=10.0, description="最小分发间隔（秒）") dynamic_distribution_max_interval: float = Field(default=30.0, ge=5.0, le=300.0, description="最大分发间隔（秒）") dynamic_distribution_jitter_factor: float = Field(default=0.2, ge=0.0, le=0.5, description="分发间隔随机扰动因子") max_concurrent_distributions: int = Field(default=10, ge=1, le=100, description="最大并发处理的消息流数量") enable_decision_history: bool = Field(default=True, description="是否启用决策历史功能") decision_history_length: int = Field( default=3, ge=1, le=10, description="决策历史记录的长度，用于增强语言模型的上下文连续性" ) # 多重回复控制配置 enable_multiple_replies: bool = Field( default=True, description="是否允许多重回复（True=允许多个回复动作，False=只保留一个回复动作）" ) multiple_replies_strategy: Literal["keep_first", "keep_best", "keep_last"] = Field( default="keep_first", description="多重回复处理策略：keep_first(保留第一个)，keep_best(保留最佳)，keep_last(保留最后一个)" ) # 表情包回复配置 allow_reply_to_emoji: bool = Field(default=True, description="是否允许回复表情包消息") class MessageReceiveConfig(ValidatedConfigBase): """消息接收配置类""" ban_words: list[str] = Field(default_factory=lambda: [], description="禁用词列表") ban_msgs_regex: list[str] = Field(default_factory=lambda: [], description="禁用消息正则列表") mute_group_list: list[str] = Field( default_factory=list, description="静默群组列表，在这些群组中，只有在被@或回复时才会响应" ) class NoticeConfig(ValidatedConfigBase): """Notice消息配置类""" enable_notice_trigger_chat: bool = Field(default=True, description="是否允许notice消息触发聊天流程") notice_in_prompt: bool = Field(default=True, description="是否在提示词中展示最近的notice消息") notice_prompt_limit: int = Field(default=5, ge=1, le=20, description="在提示词中展示的最大notice数量") notice_time_window: int = Field(default=3600, ge=10, le=86400, description="notice时间窗口(秒)") max_notices_per_chat: int = Field(default=30, ge=10, le=100, description="每个聊天保留的notice数量上限") notice_retention_time: int = Field(default=86400, ge=10, le=604800, description="notice保留时间(秒)") class ExpressionRule(ValidatedConfigBase): """表达学习规则""" chat_stream_id: str = Field(..., description="聊天流ID，空字符串表示全局") use_expression: bool = Field(default=True, description="是否使用学到的表达") learn_expression: bool = Field(default=True, description="是否学习表达") learning_strength: float = Field(default=1.0, description="学习强度") group: str | None = Field(default=None, description="表达共享组") class ExpressionConfig(ValidatedConfigBase): """表达配置类""" mode: Literal["classic", "exp_model"] = Field( default="classic", description="表达方式选择模式: classic=经典LLM评估, exp_model=机器学习模型预测" ) model_temperature: float = Field( default=1.0, ge=0.0, le=5.0, description="表达模型采样温度，0为贪婪，值越大越容易采样到低分表达" ) expiration_days: int = Field( default=90, description="表达方式过期天数，超过此天数未激活的表达方式将被清理" ) rules: list[ExpressionRule] = Field(default_factory=list, description="表达学习规则") @staticmethod def _parse_stream_config_to_chat_id(stream_config_str: str) -> str | None: """ 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: str | None = 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.rules: # 如果没有配置，使用默认值：启用表达，启用学习，强度1.0 return True, True, 1.0 # 优先检查聊天流特定的配置 if chat_stream_id: for rule in self.rules: if rule.chat_stream_id and self._parse_stream_config_to_chat_id(rule.chat_stream_id) == chat_stream_id: return rule.use_expression, rule.learn_expression, rule.learning_strength # 检查全局配置（chat_stream_id为空字符串的配置） for rule in self.rules: if rule.chat_stream_id == "": return rule.use_expression, rule.learn_expression, rule.learning_strength # 如果都没有匹配，返回默认值 return True, True, 1.0 class ToolConfig(ValidatedConfigBase): """工具配置类""" enable_tool: bool = Field(default=False, description="启用工具") force_parallel_execution: bool = Field( default=True, description="����LLM����ͬʱ������Ҫʹ�ù�����ʱǿ��ʹ�ò���ģʽ��ֹ���������Ϣ", ) max_parallel_invocations: int = Field( default=5, ge=1, le=50, description="��ͬһ�������п��Խ������ܹ��ߵ�������" ) tool_timeout: float = Field( default=60.0, ge=1.0, le=600.0, description="�������ߵ��õij�ʱʱ�䣨�룩" ) class VoiceConfig(ValidatedConfigBase): """语音识别配置类""" enable_asr: bool = Field(default=False, description="启用语音识别") asr_provider: str = Field(default="api", description="语音识别提供商") 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: float = Field(default=1.0, ge=0.01, 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") emoji_selection_mode: Literal["emotion", "description"] = Field(default="emotion", description="表情选择模式") max_context_emojis: int = Field(default=30, description="每次随机传递给LLM的表情包最大数量，0为全部") class MemoryConfig(ValidatedConfigBase): """记忆配置类""" enable_memory: bool = Field(default=True, description="启用记忆系统") memory_build_interval: int = Field(default=600, description="记忆构建间隔（秒）") # 记忆构建配置 min_memory_length: int = Field(default=10, description="最小记忆长度") max_memory_length: int = Field(default=500, description="最大记忆长度") memory_value_threshold: float = Field(default=0.7, description="记忆价值阈值") # 向量存储配置 vector_similarity_threshold: float = Field(default=0.8, description="向量相似度阈值") semantic_similarity_threshold: float = Field(default=0.6, description="语义相似度阈值") # 多阶段检索配置 metadata_filter_limit: int = Field(default=100, description="元数据过滤阶段返回数量") vector_search_limit: int = Field(default=50, description="向量搜索阶段返回数量") semantic_rerank_limit: int = Field(default=20, description="语义重排序阶段返回数量") final_result_limit: int = Field(default=10, description="最终结果数量") # 检索权重配置 vector_weight: float = Field(default=0.4, description="向量相似度权重") semantic_weight: float = Field(default=0.3, description="语义相似度权重") context_weight: float = Field(default=0.2, description="上下文权重") recency_weight: float = Field(default=0.1, description="时效性权重") # 记忆融合配置 fusion_similarity_threshold: float = Field(default=0.85, description="融合相似度阈值") deduplication_window_hours: int = Field(default=24, description="去重时间窗口（小时）") # 缓存配置 enable_memory_cache: bool = Field(default=True, description="启用记忆缓存") cache_ttl_seconds: int = Field(default=300, description="缓存生存时间（秒）") max_cache_size: int = Field(default=1000, description="最大缓存大小") # Vector DB记忆存储配置 (替代JSON存储) enable_vector_memory_storage: bool = Field(default=True, description="启用Vector DB记忆存储") 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") instant_memory_max_collections: int = Field(default=100, ge=1, description="瞬时记忆最大集合数") instant_memory_retention_hours: int = Field( default=0, ge=0, description="瞬时记忆保留时间（小时），0表示不基于时间清理" ) # Vector DB配置 vector_db_similarity_threshold: float = Field( default=0.5, description="Vector DB相似度阈值（推荐0.5-0.6，过高会导致检索不到结果）" ) vector_db_search_limit: int = Field(default=20, description="Vector DB搜索限制") vector_db_batch_size: int = Field(default=100, description="批处理大小") vector_db_enable_caching: bool = Field(default=True, description="启用内存缓存") vector_db_cache_size_limit: int = Field(default=1000, description="缓存大小限制") vector_db_auto_cleanup_interval: int = Field(default=3600, description="自动清理间隔（秒）") vector_db_retention_hours: int = Field(default=720, description="记忆保留时间（小时，默认30天）") # 遗忘引擎配置 enable_memory_forgetting: bool = Field(default=True, description="启用智能遗忘机制") forgetting_check_interval_hours: int = Field(default=24, description="遗忘检查间隔（小时）") base_forgetting_days: float = Field(default=30.0, description="基础遗忘天数") min_forgetting_days: float = Field(default=7.0, description="最小遗忘天数") max_forgetting_days: float = Field(default=365.0, description="最大遗忘天数") # 重要程度权重 critical_importance_bonus: float = Field(default=45.0, description="关键重要性额外天数") high_importance_bonus: float = Field(default=30.0, description="高重要性额外天数") normal_importance_bonus: float = Field(default=15.0, description="一般重要性额外天数") low_importance_bonus: float = Field(default=0.0, description="低重要性额外天数") # 置信度权重 verified_confidence_bonus: float = Field(default=30.0, description="已验证置信度额外天数") high_confidence_bonus: float = Field(default=20.0, description="高置信度额外天数") medium_confidence_bonus: float = Field(default=10.0, description="中等置信度额外天数") low_confidence_bonus: float = Field(default=0.0, description="低置信度额外天数") # 激活频率权重 activation_frequency_weight: float = Field(default=0.5, description="每次激活增加的天数权重") max_frequency_bonus: float = Field(default=10.0, description="最大激活频率奖励天数") # 休眠机制 dormant_threshold_days: int = Field(default=90, description="休眠状态判定天数") # === 混合记忆系统配置 === # 采样模式配置 memory_sampling_mode: Literal["all", "hippocampus", "immediate"] = Field( default="all", description="记忆采样模式：hippocampus(海马体定时采样)，immediate(即时采样)，all(所有模式)" ) # 海马体双峰采样配置 enable_hippocampus_sampling: bool = Field(default=True, description="启用海马体双峰采样策略") hippocampus_sample_interval: int = Field(default=1800, description="海马体采样间隔（秒，默认30分钟）") hippocampus_sample_size: int = Field(default=30, description="海马体每次采样的消息数量") hippocampus_batch_size: int = Field(default=5, description="海马体每批处理的记忆数量") # 双峰分布配置 [近期均值, 近期标准差, 近期权重, 远期均值, 远期标准差, 远期权重] hippocampus_distribution_config: list[float] = Field( default=[12.0, 8.0, 0.7, 48.0, 24.0, 0.3], description="海马体双峰分布配置：[近期均值(h), 近期标准差(h), 近期权重, 远期均值(h), 远期标准差(h), 远期权重]", ) # 自适应采样配置 adaptive_sampling_enabled: bool = Field(default=True, description="启用自适应采样策略") adaptive_sampling_threshold: float = Field(default=0.8, description="自适应采样负载阈值（0-1）") adaptive_sampling_check_interval: int = Field(default=300, description="自适应采样检查间隔（秒）") adaptive_sampling_max_concurrent_builds: int = Field(default=3, description="自适应采样最大并发记忆构建数") # 精准记忆配置（现有系统的增强版本） precision_memory_reply_threshold: float = Field( default=0.6, description="精准记忆回复触发阈值（对话价值评分超过此值时触发记忆构建）" ) precision_memory_max_builds_per_hour: int = Field(default=10, description="精准记忆每小时最大构建数量") # 混合系统优化配置 memory_system_load_balancing: bool = Field(default=True, description="启用记忆系统负载均衡") memory_build_throttling: bool = Field(default=True, description="启用记忆构建节流") memory_priority_queue_enabled: bool = Field(default=True, description="启用记忆优先级队列") # === 记忆图系统配置 (Memory Graph System) === # 新一代记忆系统的配置项 enable: bool = Field(default=True, description="启用记忆图系统") data_dir: str = Field(default="data/memory_graph", description="记忆数据存储目录") # 向量存储配置 vector_collection_name: str = Field(default="memory_nodes", description="向量集合名称") vector_db_path: str = Field(default="data/memory_graph/chroma_db", description="向量数据库路径") # 检索配置 search_top_k: int = Field(default=10, description="默认检索返回数量") search_min_importance: float = Field(default=0.3, description="最小重要性阈值") search_similarity_threshold: float = Field(default=0.5, description="向量相似度阈值") enable_query_optimization: bool = Field(default=True, description="启用查询优化") # 路径扩展配置 (新算法) enable_path_expansion: bool = Field(default=False, description="启用路径评分扩展算法（实验性功能）") path_expansion_max_hops: int = Field(default=2, description="路径扩展最大跳数") path_expansion_damping_factor: float = Field(default=0.85, description="路径分数衰减因子") path_expansion_max_branches: int = Field(default=10, description="每节点最大分叉数") path_expansion_merge_strategy: str = Field(default="weighted_geometric", description="路径合并策略: weighted_geometric, max_bonus") path_expansion_pruning_threshold: float = Field(default=0.9, description="路径剪枝阈值") path_expansion_path_score_weight: float = Field(default=0.50, description="路径分数在最终评分中的权重") path_expansion_importance_weight: float = Field(default=0.30, description="重要性在最终评分中的权重") path_expansion_recency_weight: float = Field(default=0.20, description="时效性在最终评分中的权重") # 🆕 路径扩展 - 记忆去重配置 enable_memory_deduplication: bool = Field(default=True, description="启用检索结果去重（合并相似记忆）") memory_deduplication_threshold: float = Field(default=0.85, description="记忆相似度阈值（0.85表示85%相似即合并）") # 遗忘配置 (记忆图系统) forgetting_enabled: bool = Field(default=True, description="是否启用自动遗忘") forgetting_activation_threshold: float = Field(default=0.1, description="激活度阈值") forgetting_min_importance: float = Field(default=0.8, description="最小保护重要性") # 激活配置 activation_decay_rate: float = Field(default=0.9, description="激活度衰减率") activation_propagation_strength: float = Field(default=0.5, description="激活传播强度") activation_propagation_depth: int = Field(default=2, description="激活传播深度") # 记忆激活配置（强制执行） auto_activate_base_strength: float = Field(default=0.1, description="记忆被检索时自动激活的基础强度") auto_activate_max_count: int = Field(default=5, description="单次搜索最多自动激活的记忆数量") # 性能配置 max_memory_nodes_per_memory: int = Field(default=10, description="每个记忆最多包含的节点数") max_related_memories: int = Field(default=5, description="相关记忆最大数量") # 节点去重合并配置 node_merger_similarity_threshold: float = Field(default=0.85, description="节点去重相似度阈值") node_merger_context_match_required: bool = Field(default=True, description="节点合并是否要求上下文匹配") node_merger_merge_batch_size: int = Field(default=50, description="节点合并批量处理大小") # ==================== 三层记忆系统配置 (Three-Tier Memory System) ==================== # 感知记忆层配置 perceptual_max_blocks: int = Field(default=50, description="记忆堆最大容量（全局）") perceptual_block_size: int = Field(default=5, description="每个记忆块包含的消息数量") perceptual_similarity_threshold: float = Field(default=0.55, description="相似度阈值（0-1）") perceptual_topk: int = Field(default=3, description="TopK召回数量") perceptual_activation_threshold: int = Field(default=3, description="激活阈值（召回次数→短期）") # 短期记忆层配置 short_term_max_memories: int = Field(default=30, description="短期记忆最大数量") short_term_transfer_threshold: float = Field(default=0.6, description="转移到长期记忆的重要性阈值") short_term_search_top_k: int = Field(default=5, description="搜索时返回的最大数量") short_term_decay_factor: float = Field(default=0.98, description="衰减因子") # 长期记忆层配置 use_judge: bool = Field(default=True, description="使用评判模型决定是否检索长期记忆") long_term_batch_size: int = Field(default=10, description="批量转移大小") long_term_decay_factor: float = Field(default=0.95, description="衰减因子") long_term_auto_transfer_interval: int = Field(default=60, description="自动转移间隔（秒）") 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 ReactionRuleConfig(ValidatedConfigBase): """反应规则配置类""" chat_stream_id: str = Field(default="", description='聊天流ID，格式为 "platform:id:type"，空字符串表示全局') rule_type: Literal["keyword", "regex"] = Field(..., description='规则类型，必须是 "keyword" 或 "regex"') patterns: list[str] = Field(..., description="关键词或正则表达式列表") reaction: str = Field(..., description="触发后的回复内容") def __post_init__(self): import re if not self.patterns: raise ValueError("patterns 列表不能为空") if self.rule_type == "regex": for pattern in self.patterns: try: re.compile(pattern) except re.error as e: raise ValueError(f"无效的正则表达式 '{pattern}': {e!s}") from e class ReactionConfig(ValidatedConfigBase): """反应规则系统配置""" rules: list[ReactionRuleConfig] = Field(default_factory=list, description="反应规则列表") 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") split_mode: str = Field(default="llm", description="分割模式: 'llm' 或 'punctuation'") 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 LogConfig(ValidatedConfigBase): """日志配置类""" date_style: str = Field(default="m-d H:i:s", description="日期格式") log_level_style: str = Field(default="lite", description="日志级别样式") color_text: str = Field(default="full", description="日志文本颜色") log_level: str = Field(default="INFO", description="全局日志级别（向下兼容，优先级低于分别设置）") file_retention_days: int = Field(default=7, description="文件日志保留天数，0=禁用文件日志，-1=永不删除") console_log_level: str = Field(default="INFO", description="控制台日志级别") file_log_level: str = Field(default="DEBUG", description="文件日志级别") suppress_libraries: list[str] = Field(default_factory=list, description="完全屏蔽日志的第三方库列表") library_log_levels: dict[str, str] = Field(default_factory=dict, description="设置特定库的日志级别") 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""" pfc_chatting: bool = Field(default=False, description="启用PFC聊天") class MessageBusConfig(ValidatedConfigBase): """mofox_wire 消息服务配置""" use_custom: bool = Field(default=False, description="是否使用自定义地址") host: str = Field(default="127.0.0.1", description="消息服务主机") port: int = Field(default=8090, description="消息服务端口") mode: Literal["ws", "tcp"] = Field(default="ws", description="传输模式") use_wss: bool = Field(default=False, description="是否启用 WSS") cert_file: str = Field(default="", description="证书文件路径") key_file: str = Field(default="", description="密钥文件路径") auth_token: list[str] = Field(default_factory=lambda: [], description="认证 token 列表") 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") enable_summary: bool = Field(default=True, description="是否启用知识库总结") 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_threshold: float = Field(default=0.3, description="QA段落阈值") 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 PlanningSystemConfig(ValidatedConfigBase): """规划系统配置 (日程与月度计划)""" # --- 日程生成 (原 ScheduleConfig) --- schedule_enable: bool = Field(default=True, description="是否启用每日日程生成功能") schedule_guidelines: str = Field(default="", description="日程生成指导原则") # --- 月度计划 (原 MonthlyPlanSystemConfig) --- monthly_plan_enable: bool = Field(default=True, description="是否启用月度计划系统") monthly_plan_guidelines: str = Field(default="", description="月度计划生成指导原则") max_plans_per_month: int = Field(default=10, description="每月最多生成的计划数量") avoid_repetition_days: int = Field(default=7, description="避免在多少天内重复使用同一个月度计划") completion_threshold: int = Field(default=3, description="一个月度计划被使用多少次后算作完成") 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 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") frame_extraction_mode: str = Field( default="keyframe", description="抽帧模式：keyframe(关键帧), fixed_number(固定数量), time_interval(时间间隔)" ) frame_interval_seconds: float = Field(default=2.0, description="抽帧时间间隔") 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") # Rust模块相关配置 rust_keyframe_threshold: float = Field(default=2.0, description="关键帧检测阈值") rust_use_simd: bool = Field(default=True, description="启用SIMD优化") rust_block_size: int = Field(default=8192, description="Rust处理块大小") rust_threads: int = Field(default=0, description="Rust线程数，0表示自动检测") ffmpeg_path: str = Field(default="ffmpeg", description="FFmpeg可执行文件路径") 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") tavily_api_keys: list[str] = Field(default_factory=lambda: [], description="Tavily API密钥列表，支持轮询机制") exa_api_keys: list[str] = Field(default_factory=lambda: [], description="exa API密钥列表，支持轮询机制") metaso_api_keys: list[str] = Field(default_factory=lambda: [], description="Metaso API密钥列表，支持轮询机制") searxng_instances: list[str] = Field(default_factory=list, description="SearXNG 实例 URL 列表") searxng_api_keys: list[str] = Field(default_factory=list, description="SearXNG 实例 API 密钥列表") serper_api_keys: list[str] = Field(default_factory=list, description="serper API 密钥列表") 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 MaizoneContextGroup(ValidatedConfigBase): """QQ空间专用互通组配置""" name: str = Field(..., description="QQ空间互通组的名称") chat_ids: list[list[str]] = Field( ..., description='定义组内成员的列表。格式为 [["type", "id"]]。type为"group"或"private"，id为群号或用户ID。', ) 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") # --- S4U模式: 用户中心上下文检索 --- s4u_mode: Literal["whitelist", "blacklist"] = Field( default="whitelist", description="S4U模式的白名单/黑名单模式", ) s4u_limit: int = Field(default=5, description="S4U模式下，每个聊天获取的消息条数") s4u_stream_limit: int = Field(default=3, description="S4U模式下，最多检索多少个不同的聊天流") s4u_whitelist_chats: list[str] = Field( default_factory=list, description='S4U模式的白名单列表。格式: ["platform:type:id", ...]', ) s4u_blacklist_chats: list[str] = Field( default_factory=list, description='S4U模式的黑名单列表。格式: ["platform:type:id", ...]', ) # --- QQ空间专用互通组 --- maizone_context_group: list[MaizoneContextGroup] = Field(default_factory=list, description="QQ空间专用互通组列表") class CommandConfig(ValidatedConfigBase): """命令系统配置类""" command_prefixes: list[str] = Field(default_factory=lambda: ["/", "!", ".", "#"], description="支持的命令前缀列表") class PluginHttpSystemConfig(ValidatedConfigBase): """插件http系统相关配置""" enable_plugin_http_endpoints: bool = Field( default=True, description="总开关，是否允许插件创建HTTP端点" ) plugin_api_rate_limit_enable: bool = Field( default=True, description="是否为插件API启用全局速率限制" ) plugin_api_rate_limit_default: str = Field( default="100/minute", description="插件API的默认速率限制策略" ) plugin_api_valid_keys: list[str] = Field( default_factory=list, description="��Ч��API��Կ�б������ڲ����֤" ) event_handler_timeout: float = Field( default=30.0, ge=1.0, le=300.0, description="�¼����������ִ�г�ʱʱ�䣨�룩" ) event_handler_max_concurrency: int = Field( default=20, ge=1, le=200, description="����ÿ���¼�ͬʱִ�е�������߸���0��ʾ����������" ) class MasterPromptConfig(ValidatedConfigBase): """主人身份提示词配置""" enable: bool = Field(default=False, description="是否启用主人提示词注入功能") master_hint: str = Field(default="", description="对主人注入的额外提示词内容") non_master_hint: str = Field(default="", description="对非主人注入的额外提示词内容") 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], ...]" ) master_prompt: MasterPromptConfig = Field( default_factory=MasterPromptConfig, description="主人身份提示词配置" ) class AffinityFlowConfig(ValidatedConfigBase): """亲和流配置类（兴趣度评分和人物关系系统）""" # Normal模式开关 enable_normal_mode: bool = Field(default=True, description="是否启用自动Normal模式切换") # 兴趣评分系统参数 reply_action_interest_threshold: float = Field(default=0.4, description="回复动作兴趣阈值") non_reply_action_interest_threshold: float = Field(default=0.2, description="非回复动作兴趣阈值") # 回复决策系统参数 no_reply_threshold_adjustment: float = Field(default=0.1, description="不回复兴趣阈值调整值") reply_cooldown_reduction: int = Field(default=2, description="回复后减少的不回复计数") max_no_reply_count: int = Field(default=5, description="最大不回复计数次数") # 回复后连续对话机制参数 enable_post_reply_boost: bool = Field(default=True, description="是否启用回复后阈值降低机制，使bot在回复后更容易进行连续对话") post_reply_threshold_reduction: float = Field(default=0.15, description="回复后初始阈值降低值（建议0.1-0.2）") post_reply_boost_max_count: int = Field(default=3, description="回复后阈值降低的最大持续次数（建议2-5）") post_reply_boost_decay_rate: float = Field(default=0.5, description="每次回复后阈值降低衰减率（0-1，建议0.3-0.7）") # 综合评分权重 keyword_match_weight: float = Field(default=0.4, description="兴趣关键词匹配度权重") mention_bot_weight: float = Field(default=0.3, description="提及bot分数权重") relationship_weight: float = Field(default=0.3, description="人物关系分数权重") # 提及bot相关参数 mention_bot_adjustment_threshold: float = Field(default=0.3, description="提及bot后的调整阈值") mention_bot_interest_score: float = Field(default=0.6, description="提及bot的兴趣分（已弃用，改用strong/weak_mention）") strong_mention_interest_score: float = Field(default=2.5, description="强提及的兴趣分（被@、被回复、私聊）") weak_mention_interest_score: float = Field(default=1.5, description="弱提及的兴趣分（文本匹配bot名字或别名）") base_relationship_score: float = Field(default=0.5, description="基础人物关系分") class ProactiveThinkingConfig(ValidatedConfigBase): """主动思考（主动发起对话）功能配置""" # --- 总开关 --- enable: bool = Field(default=False, description="是否启用主动发起对话功能") # --- 间隔配置 --- base_interval: int = Field(default=1800, ge=60, description="基础触发间隔（秒），默认30分钟") min_interval: int = Field(default=600, ge=60, description="最小触发间隔（秒），默认10分钟。兴趣分数高时会接近此值") max_interval: int = Field(default=7200, ge=60, description="最大触发间隔（秒），默认2小时。兴趣分数低时会接近此值") # --- 新增：动态调整配置 --- use_interest_score: bool = Field(default=True, description="是否根据兴趣分数动态调整间隔。关闭则使用固定base_interval") interest_score_factor: float = Field(default=2.0, ge=1.0, le=3.0, description="兴趣分数影响因子。公式: interval = base * (factor - score)") # --- 新增：黑白名单配置 --- whitelist_mode: bool = Field(default=False, description="是否启用白名单模式。启用后只对白名单中的聊天流生效") blacklist_mode: bool = Field(default=False, description="是否启用黑名单模式。启用后排除黑名单中的聊天流") whitelist_private: list[str] = Field( default_factory=list, description='私聊白名单，格式: ["platform:user_id:private", "qq:12345:private"]' ) whitelist_group: list[str] = Field( default_factory=list, description='群聊白名单，格式: ["platform:group_id:group", "qq:123456:group"]' ) blacklist_private: list[str] = Field( default_factory=list, description='私聊黑名单，格式: ["platform:user_id:private", "qq:12345:private"]' ) blacklist_group: list[str] = Field( default_factory=list, description='群聊黑名单，格式: ["platform:group_id:group", "qq:123456:group"]' ) # --- 新增：兴趣分数阈值 --- min_interest_score: float = Field(default=0.0, ge=0.0, le=1.0, description="最低兴趣分数阈值，低于此值不会主动思考") max_interest_score: float = Field(default=1.0, ge=0.0, le=1.0, description="最高兴趣分数阈值，高于此值不会主动思考（用于限制过度活跃）") # --- 新增：时间策略配置 --- enable_time_strategy: bool = Field(default=False, description="是否启用时间策略（根据时段调整频率）") quiet_hours_start: str = Field(default="00:00", description='安静时段开始时间，格式: "HH:MM"') quiet_hours_end: str = Field(default="07:00", description='安静时段结束时间，格式: "HH:MM"') active_hours_multiplier: float = Field(default=0.7, ge=0.1, le=2.0, description="活跃时段间隔倍数，<1表示更频繁，>1表示更稀疏") # --- 新增：冷却与限制 --- reply_reset_enabled: bool = Field(default=True, description="bot回复后是否重置定时器（避免回复后立即又主动发言）") topic_throw_cooldown: int = Field(default=3600, ge=0, description="抛出话题后的冷却时间（秒），期间暂停主动思考") max_daily_proactive: int = Field(default=0, ge=0, description="每个聊天流每天最多主动发言次数，0表示不限制") # --- 新增：决策权重配置 --- do_nothing_weight: float = Field(default=0.4, ge=0.0, le=1.0, description="do_nothing动作的基础权重") simple_bubble_weight: float = Field(default=0.3, ge=0.0, le=1.0, description="simple_bubble动作的基础权重") throw_topic_weight: float = Field(default=0.3, ge=0.0, le=1.0, description="throw_topic动作的基础权重") # --- 新增：调试与监控 --- enable_statistics: bool = Field(default=True, description="是否启用统计功能（记录触发次数、决策分布等）") log_decisions: bool = Field(default=False, description="是否记录每次决策的详细日志（用于调试）") class KokoroFlowChatterProactiveConfig(ValidatedConfigBase): """ Kokoro Flow Chatter 主动思考子配置 设计哲学：主动行为源于内部状态和外部环境的自然反应，而非机械的限制。 她的主动是因为挂念、因为关心、因为想问候，而不是因为"任务"。 """ enabled: bool = Field(default=True, description="是否启用KFC的私聊主动思考") # 1. 沉默触发器：当感到长久的沉默时，她可能会想说些什么 silence_threshold_seconds: int = Field( default=7200, ge=60, le=86400, description="用户沉默超过此时长（秒），可能触发主动思考（默认2小时）" ) # 2. 关系门槛：她不会对不熟悉的人过于主动 min_affinity_for_proactive: float = Field( default=0.3, ge=0.0, le=1.0, description="需要达到最低好感度，她才会开始主动关心" ) # 3. 频率呼吸：为了避免打扰，她的关心总是有间隔的 min_interval_between_proactive: int = Field( default=1800, ge=0, description="两次主动思考之间的最小间隔（秒，默认30分钟）" ) # 4. 自然问候：在特定的时间，她会像朋友一样送上问候 enable_morning_greeting: bool = Field( default=True, description="是否启用早安问候 (例如: 8:00 - 9:00)" ) enable_night_greeting: bool = Field( default=True, description="是否启用晚安问候 (例如: 22:00 - 23:00)" ) # 5. 勿扰时段：在这段时间内不会主动发起对话 quiet_hours_start: str = Field( default="23:00", description="勿扰时段开始时间，格式: HH:MM" ) quiet_hours_end: str = Field( default="07:00", description="勿扰时段结束时间，格式: HH:MM" ) # 6. 触发概率：每次检查时主动发起的概率 trigger_probability: float = Field( default=0.3, ge=0.0, le=1.0, description="主动思考触发概率（0.0~1.0），用于避免过于频繁打扰" ) class KokoroFlowChatterWaitingConfig(ValidatedConfigBase): """Kokoro Flow Chatter 等待策略配置""" default_max_wait_seconds: int = Field( default=300, ge=0, le=3600, description="默认最大等待秒数（当LLM未给出等待时间时使用）", ) min_wait_seconds: int = Field( default=30, ge=0, le=1800, description="允许的最小等待秒数，防止等待时间过短导致频繁打扰", ) max_wait_seconds: int = Field( default=1800, ge=60, le=7200, description="允许的最大等待秒数，避免等待时间过长", ) wait_duration_multiplier: float = Field( default=1.0, ge=0.0, le=10.0, description="等待时长倍率，用于整体放大或缩短LLM给出的等待时间", ) max_consecutive_timeouts: int = Field( default=3, ge=0, le=10, description="允许的连续等待超时次数上限，达到后不再等待用户回复 (0 表示不限制)", ) class KokoroFlowChatterPromptConfig(ValidatedConfigBase): """Kokoro Flow Chatter 提示词/上下文构建配置""" activity_stream_format: Literal["narrative", "table", "both"] = Field( default="narrative", description='活动流格式: "narrative"(线性叙事) / "table"(结构化表格) / "both"(两者都输出)', ) max_activity_entries: int = Field( default=30, ge=0, le=200, description="活动流最多保留条数（越大越完整，但token越高）", ) max_entry_length: int = Field( default=500, ge=0, le=5000, description="活动流单条最大字符数（用于裁剪，避免单条过长拖垮上下文）", ) class KokoroFlowChatterConfig(ValidatedConfigBase): """ Kokoro Flow Chatter 配置类 - 私聊专用心流对话系统 设计理念：KFC不是独立人格，它复用全局的人设、情感框架和回复模型， 只作为Bot核心人格在私聊中的一种特殊表现模式。 """ # --- 总开关 --- enable: bool = Field( default=True, description="开启后KFC将接管所有私聊消息；关闭后私聊消息将由AFC处理" ) # --- 工作模式 --- mode: Literal["unified", "split"] = Field( default="split", description='工作模式: "unified"(单次调用) 或 "split"(planner+replyer两次调用)', ) # --- 核心行为配置 --- max_wait_seconds_default: int = Field( default=300, ge=30, le=3600, description="默认的最大等待秒数（AI发送消息后愿意等待用户回复的时间）" ) enable_continuous_thinking: bool = Field( default=True, description="是否在等待期间启用心理活动更新" ) # --- 自定义决策提示词 --- custom_decision_prompt: str = Field( default="", description="自定义KFC决策行为指导提示词（unified影响整体，split仅影响planner）", ) prompt: KokoroFlowChatterPromptConfig = Field( default_factory=KokoroFlowChatterPromptConfig, description="提示词/上下文构建配置（活动流格式、裁剪等）", ) waiting: KokoroFlowChatterWaitingConfig = Field( default_factory=KokoroFlowChatterWaitingConfig, description="等待策略配置（默认等待时间、倍率等）", ) # --- 私聊专属主动思考配置 --- proactive_thinking: KokoroFlowChatterProactiveConfig = Field( default_factory=KokoroFlowChatterProactiveConfig, description="私聊专属主动思考配置" )