mirror of https://github.com/Mai-with-u/MaiBot.git
528 lines
15 KiB
Python
528 lines
15 KiB
Python
import re
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from dataclasses import dataclass, field
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from typing import Literal, Optional
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from src.config.config_base import ConfigBase
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"""
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须知：
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1. 本文件中记录了所有的配置项
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2. 所有新增的class都需要继承自ConfigBase
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3. 所有新增的class都应在config.py中的Config类中添加字段
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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
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"""
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@dataclass
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class BotConfig(ConfigBase):
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"""QQ机器人配置类"""
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platform: str
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"""平台"""
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qq_account: str
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"""QQ账号"""
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nickname: str
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"""昵称"""
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alias_names: list[str] = field(default_factory=lambda: [])
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"""别名列表"""
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@dataclass
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class PersonalityConfig(ConfigBase):
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"""人格配置类"""
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personality: str
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"""人格"""
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emotion_style: str
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"""情感特征"""
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reply_style: str = ""
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"""表达风格"""
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interest: str = ""
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"""兴趣"""
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plan_style: str = ""
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"""说话规则，行为风格"""
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visual_style: str = ""
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"""图片提示词"""
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@dataclass
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class RelationshipConfig(ConfigBase):
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"""关系配置类"""
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enable_relationship: bool = True
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"""是否启用关系系统"""
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@dataclass
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class ChatConfig(ConfigBase):
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"""聊天配置类"""
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max_context_size: int = 18
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"""上下文长度"""
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interest_rate_mode: Literal["fast", "accurate"] = "fast"
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"""兴趣值计算模式，fast为快速计算，accurate为精确计算"""
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planner_size: float = 1.5
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"""副规划器大小，越小，麦麦的动作执行能力越精细，但是消耗更多token，调大可以缓解429类错误"""
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mentioned_bot_reply: bool = True
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"""是否启用提及必回复"""
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at_bot_inevitable_reply: float = 1
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"""@bot 必然回复，1为100%回复，0为不额外增幅"""
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talk_frequency: float = 0.5
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"""回复频率阈值"""
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talk_value: float = 1
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"""思考频率"""
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# 合并后的时段频率配置
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talk_frequency_adjust: list[list[str]] = field(default_factory=lambda: [])
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focus_value: float = 0.5
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"""麦麦的专注思考能力，越低越容易专注，消耗token也越多"""
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focus_value_adjust: list[list[str]] = field(default_factory=lambda: [])
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"""
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统一的活跃度和专注度配置
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格式：[["platform:chat_id:type", "HH:MM,frequency", "HH:MM,frequency", ...], ...]
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全局配置示例：
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[["", "8:00,1", "12:00,2", "18:00,1.5", "00:00,0.5"]]
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特定聊天流配置示例：
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[
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["", "8:00,1", "12:00,1.2", "18:00,1.5", "01:00,0.6"], # 全局默认配置
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["qq:1026294844:group", "12:20,1", "16:10,2", "20:10,1", "00:10,0.3"], # 特定群聊配置
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["qq:729957033:private", "8:20,1", "12:10,2", "20:10,1.5", "00:10,0.2"] # 特定私聊配置
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]
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说明：
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- 当第一个元素为空字符串""时，表示全局默认配置
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- 当第一个元素为"platform:id:type"格式时，表示特定聊天流配置
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- 后续元素是"时间,频率"格式，表示从该时间开始使用该频率，直到下一个时间点
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- 优先级：特定聊天流配置 > 全局配置 > 默认值
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注意：
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- talk_frequency_adjust 控制回复频率，数值越高回复越频繁
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- focus_value_adjust 控制专注思考能力，数值越低越容易专注，消耗token也越多
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"""
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@dataclass
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class MessageReceiveConfig(ConfigBase):
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"""消息接收配置类"""
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ban_words: set[str] = field(default_factory=lambda: set())
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"""过滤词列表"""
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ban_msgs_regex: set[str] = field(default_factory=lambda: set())
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"""过滤正则表达式列表"""
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@dataclass
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class ExpressionConfig(ConfigBase):
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"""表达配置类"""
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learning_list: list[list] = field(default_factory=lambda: [])
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"""
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表达学习配置列表，支持按聊天流配置
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格式: [["chat_stream_id", "use_expression", "enable_learning", learning_intensity], ...]
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示例:
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[
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["", "enable", "enable", 1.0], # 全局配置：使用表达，启用学习，学习强度1.0
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["qq:1919810:private", "enable", "enable", 1.5], # 特定私聊配置：使用表达，启用学习，学习强度1.5
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["qq:114514:private", "enable", "disable", 0.5], # 特定私聊配置：使用表达，禁用学习，学习强度0.5
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]
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说明:
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- 第一位: chat_stream_id，空字符串表示全局配置
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- 第二位: 是否使用学到的表达 ("enable"/"disable")
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- 第三位: 是否学习表达 ("enable"/"disable")
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- 第四位: 学习强度（浮点数），影响学习频率，最短学习时间间隔 = 300/学习强度（秒）
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"""
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expression_groups: list[list[str]] = field(default_factory=list)
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"""
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表达学习互通组
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格式: [["qq:12345:group", "qq:67890:private"]]
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"""
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def _parse_stream_config_to_chat_id(self, stream_config_str: str) -> Optional[str]:
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"""
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解析流配置字符串并生成对应的 chat_id
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Args:
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stream_config_str: 格式为 "platform:id:type" 的字符串
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Returns:
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str: 生成的 chat_id，如果解析失败则返回 None
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"""
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try:
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parts = stream_config_str.split(":")
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if len(parts) != 3:
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return None
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platform = parts[0]
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id_str = parts[1]
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stream_type = parts[2]
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# 判断是否为群聊
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is_group = stream_type == "group"
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# 使用与 ChatStream.get_stream_id 相同的逻辑生成 chat_id
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import hashlib
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if is_group:
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components = [platform, str(id_str)]
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else:
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components = [platform, str(id_str), "private"]
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key = "_".join(components)
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return hashlib.md5(key.encode()).hexdigest()
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except (ValueError, IndexError):
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return None
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def get_expression_config_for_chat(self, chat_stream_id: Optional[str] = None) -> tuple[bool, bool, int]:
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"""
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根据聊天流ID获取表达配置
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Args:
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chat_stream_id: 聊天流ID，格式为哈希值
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Returns:
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tuple: (是否使用表达, 是否学习表达, 学习间隔)
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"""
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if not self.learning_list:
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# 如果没有配置，使用默认值：启用表达，启用学习，300秒间隔
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return True, True, 300
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# 优先检查聊天流特定的配置
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if chat_stream_id:
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specific_expression_config = self._get_stream_specific_config(chat_stream_id)
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if specific_expression_config is not None:
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return specific_expression_config
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# 检查全局配置（第一个元素为空字符串的配置）
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global_expression_config = self._get_global_config()
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if global_expression_config is not None:
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return global_expression_config
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# 如果都没有匹配，返回默认值
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return True, True, 300
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def _get_stream_specific_config(self, chat_stream_id: str) -> Optional[tuple[bool, bool, int]]:
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"""
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获取特定聊天流的表达配置
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Args:
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chat_stream_id: 聊天流ID（哈希值）
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Returns:
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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
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"""
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for config_item in self.learning_list:
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if not config_item or len(config_item) < 4:
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continue
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stream_config_str = config_item[0] # 例如 "qq:1026294844:group"
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# 如果是空字符串，跳过（这是全局配置）
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if stream_config_str == "":
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continue
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# 解析配置字符串并生成对应的 chat_id
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config_chat_id = self._parse_stream_config_to_chat_id(stream_config_str)
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if config_chat_id is None:
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continue
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# 比较生成的 chat_id
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if config_chat_id != chat_stream_id:
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continue
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# 解析配置
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try:
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use_expression: bool = config_item[1].lower() == "enable"
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enable_learning: bool = config_item[2].lower() == "enable"
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learning_intensity: float = float(config_item[3])
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return use_expression, enable_learning, learning_intensity # type: ignore
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except (ValueError, IndexError):
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continue
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return None
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def _get_global_config(self) -> Optional[tuple[bool, bool, int]]:
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"""
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获取全局表达配置
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Returns:
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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
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"""
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for config_item in self.learning_list:
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if not config_item or len(config_item) < 4:
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continue
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# 检查是否为全局配置（第一个元素为空字符串）
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if config_item[0] == "":
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try:
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use_expression: bool = config_item[1].lower() == "enable"
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enable_learning: bool = config_item[2].lower() == "enable"
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learning_intensity = float(config_item[3])
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return use_expression, enable_learning, learning_intensity # type: ignore
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except (ValueError, IndexError):
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continue
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return None
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@dataclass
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class ToolConfig(ConfigBase):
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"""工具配置类"""
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enable_tool: bool = False
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"""是否在聊天中启用工具"""
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@dataclass
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class VoiceConfig(ConfigBase):
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"""语音识别配置类"""
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enable_asr: bool = False
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"""是否启用语音识别"""
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@dataclass
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class EmojiConfig(ConfigBase):
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"""表情包配置类"""
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emoji_chance: float = 0.6
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"""发送表情包的基础概率"""
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max_reg_num: int = 200
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"""表情包最大注册数量"""
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do_replace: bool = True
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"""达到最大注册数量时替换旧表情包"""
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check_interval: int = 120
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"""表情包检查间隔（分钟）"""
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steal_emoji: bool = True
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"""是否偷取表情包，让麦麦可以发送她保存的这些表情包"""
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content_filtration: bool = False
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"""是否开启表情包过滤"""
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filtration_prompt: str = "符合公序良俗"
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"""表情包过滤要求"""
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@dataclass
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class MoodConfig(ConfigBase):
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"""情绪配置类"""
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enable_mood: bool = False
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"""是否启用情绪系统"""
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mood_update_threshold: float = 1.0
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"""情绪更新阈值,越高，更新越慢"""
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@dataclass
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class KeywordRuleConfig(ConfigBase):
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"""关键词规则配置类"""
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keywords: list[str] = field(default_factory=lambda: [])
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"""关键词列表"""
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regex: list[str] = field(default_factory=lambda: [])
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"""正则表达式列表"""
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reaction: str = ""
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"""关键词触发的反应"""
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def __post_init__(self):
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"""验证配置"""
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if not self.keywords and not self.regex:
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raise ValueError("关键词规则必须至少包含keywords或regex中的一个")
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if not self.reaction:
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raise ValueError("关键词规则必须包含reaction")
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# 验证正则表达式
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for pattern in self.regex:
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try:
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re.compile(pattern)
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except re.error as e:
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raise ValueError(f"无效的正则表达式 '{pattern}': {str(e)}") from e
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@dataclass
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class KeywordReactionConfig(ConfigBase):
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"""关键词配置类"""
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keyword_rules: list[KeywordRuleConfig] = field(default_factory=lambda: [])
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"""关键词规则列表"""
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regex_rules: list[KeywordRuleConfig] = field(default_factory=lambda: [])
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"""正则表达式规则列表"""
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def __post_init__(self):
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"""验证配置"""
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# 验证所有规则
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for rule in self.keyword_rules + self.regex_rules:
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if not isinstance(rule, KeywordRuleConfig):
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raise ValueError(f"规则必须是KeywordRuleConfig类型，而不是{type(rule).__name__}")
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@dataclass
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class ResponsePostProcessConfig(ConfigBase):
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"""回复后处理配置类"""
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enable_response_post_process: bool = True
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"""是否启用回复后处理，包括错别字生成器，回复分割器"""
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@dataclass
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class ChineseTypoConfig(ConfigBase):
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"""中文错别字配置类"""
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enable: bool = True
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"""是否启用中文错别字生成器"""
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error_rate: float = 0.01
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"""单字替换概率"""
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min_freq: int = 9
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"""最小字频阈值"""
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tone_error_rate: float = 0.1
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"""声调错误概率"""
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word_replace_rate: float = 0.006
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"""整词替换概率"""
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@dataclass
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class ResponseSplitterConfig(ConfigBase):
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"""回复分割器配置类"""
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enable: bool = True
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"""是否启用回复分割器"""
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max_length: int = 256
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"""回复允许的最大长度"""
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max_sentence_num: int = 3
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"""回复允许的最大句子数"""
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enable_kaomoji_protection: bool = False
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"""是否启用颜文字保护"""
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@dataclass
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class TelemetryConfig(ConfigBase):
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"""遥测配置类"""
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enable: bool = True
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"""是否启用遥测"""
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@dataclass
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class DebugConfig(ConfigBase):
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"""调试配置类"""
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show_prompt: bool = False
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"""是否显示prompt"""
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@dataclass
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class ExperimentalConfig(ConfigBase):
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"""实验功能配置类"""
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enable_friend_chat: bool = False
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"""是否启用好友聊天"""
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@dataclass
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class MaimMessageConfig(ConfigBase):
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"""maim_message配置类"""
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use_custom: bool = False
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"""是否使用自定义的maim_message配置"""
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host: str = "127.0.0.1"
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"""主机地址"""
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port: int = 8090
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""""端口号"""
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mode: Literal["ws", "tcp"] = "ws"
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"""连接模式，支持ws和tcp"""
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use_wss: bool = False
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"""是否使用WSS安全连接"""
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cert_file: str = ""
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"""SSL证书文件路径，仅在use_wss=True时有效"""
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key_file: str = ""
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"""SSL密钥文件路径，仅在use_wss=True时有效"""
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auth_token: list[str] = field(default_factory=lambda: [])
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"""认证令牌，用于API验证，为空则不启用验证"""
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@dataclass
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class LPMMKnowledgeConfig(ConfigBase):
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"""LPMM知识库配置类"""
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enable: bool = True
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"""是否启用LPMM知识库"""
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rag_synonym_search_top_k: int = 10
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"""RAG同义词搜索的Top K数量"""
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rag_synonym_threshold: float = 0.8
|
||
"""RAG同义词搜索的相似度阈值"""
|
||
|
||
info_extraction_workers: int = 3
|
||
"""信息提取工作线程数"""
|
||
|
||
qa_relation_search_top_k: int = 10
|
||
"""QA关系搜索的Top K数量"""
|
||
|
||
qa_relation_threshold: float = 0.75
|
||
"""QA关系搜索的相似度阈值"""
|
||
|
||
qa_paragraph_search_top_k: int = 1000
|
||
"""QA段落搜索的Top K数量"""
|
||
|
||
qa_paragraph_node_weight: float = 0.05
|
||
"""QA段落节点权重"""
|
||
|
||
qa_ent_filter_top_k: int = 10
|
||
"""QA实体过滤的Top K数量"""
|
||
|
||
qa_ppr_damping: float = 0.8
|
||
"""QA PageRank阻尼系数"""
|
||
|
||
qa_res_top_k: int = 10
|
||
"""QA最终结果的Top K数量"""
|
||
|
||
embedding_dimension: int = 1024
|
||
"""嵌入向量维度，应该与模型的输出维度一致"""
|