601 lines
16 KiB
Python
601 lines
16 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_core: str
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"""核心人格"""
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personality_side: str
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"""人格侧写"""
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identity: str = ""
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"""身份特征"""
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compress_personality: bool = True
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"""是否压缩人格，压缩后会精简人格信息，节省token消耗并提高回复性能，但是会丢失一些信息，如果人设不长，可以关闭"""
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compress_identity: bool = True
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"""是否压缩身份，压缩后会精简身份信息，节省token消耗并提高回复性能，但是会丢失一些信息，如果不长，可以关闭"""
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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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relation_frequency: int = 1
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"""关系频率，麦麦构建关系的速度"""
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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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willing_amplifier: float = 1.0
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replyer_random_probability: float = 0.5
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"""
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发言时选择推理模型的概率（0-1之间）
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选择普通模型的概率为 1 - reasoning_normal_model_probability
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"""
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thinking_timeout: int = 40
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"""麦麦最长思考规划时间，超过这个时间的思考会放弃（往往是api反应太慢）"""
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talk_frequency: float = 1
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"""回复频率阈值"""
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mentioned_bot_inevitable_reply: bool = False
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"""提及 bot 必然回复"""
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at_bot_inevitable_reply: bool = False
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"""@bot 必然回复"""
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# 修改：基于时段的回复频率配置，改为数组格式
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time_based_talk_frequency: list[str] = field(default_factory=lambda: [])
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"""
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基于时段的回复频率配置（全局）
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格式：["HH:MM,frequency", "HH:MM,frequency", ...]
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示例：["8:00,1", "12:00,2", "18:00,1.5", "00:00,0.5"]
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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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"""
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# 新增：基于聊天流的个性化时段频率配置
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talk_frequency_adjust: list[list[str]] = field(default_factory=lambda: [])
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"""
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基于聊天流的个性化时段频率配置
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格式：[["platform:chat_id:type", "HH:MM,frequency", "HH:MM,frequency", ...], ...]
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示例：[
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["qq:1026294844:group", "12:20,1", "16:10,2", "20:10,1", "00:10,0.3"],
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["qq:729957033:group", "8:20,1", "12:10,2", "20:10,1.5", "00:10,0.2"]
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]
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每个子列表的第一个元素是聊天流标识符，后续元素是"时间,频率"格式
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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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"""
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focus_value: float = 1.0
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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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def get_current_talk_frequency(self, chat_stream_id: Optional[str] = None) -> float:
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"""
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根据当前时间和聊天流获取对应的 talk_frequency
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Args:
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chat_stream_id: 聊天流ID，格式为 "platform:chat_id:type"
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Returns:
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float: 对应的频率值
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"""
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# 优先检查聊天流特定的配置
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if chat_stream_id and self.talk_frequency_adjust:
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stream_frequency = self._get_stream_specific_frequency(chat_stream_id)
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if stream_frequency is not None:
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return stream_frequency
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# 如果没有聊天流特定配置，检查全局时段配置
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if self.time_based_talk_frequency:
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global_frequency = self._get_time_based_frequency(self.time_based_talk_frequency)
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if global_frequency is not None:
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return global_frequency
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# 如果都没有匹配，返回默认值
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return self.talk_frequency
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def _get_time_based_frequency(self, time_freq_list: list[str]) -> Optional[float]:
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"""
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根据时间配置列表获取当前时段的频率
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Args:
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time_freq_list: 时间频率配置列表，格式为 ["HH:MM,frequency", ...]
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Returns:
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float: 频率值，如果没有配置则返回 None
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"""
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from datetime import datetime
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current_time = datetime.now().strftime("%H:%M")
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current_hour, current_minute = map(int, current_time.split(":"))
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current_minutes = current_hour * 60 + current_minute
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# 解析时间频率配置
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time_freq_pairs = []
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for time_freq_str in time_freq_list:
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try:
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time_str, freq_str = time_freq_str.split(",")
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hour, minute = map(int, time_str.split(":"))
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frequency = float(freq_str)
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minutes = hour * 60 + minute
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time_freq_pairs.append((minutes, frequency))
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except (ValueError, IndexError):
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continue
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if not time_freq_pairs:
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return None
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# 按时间排序
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time_freq_pairs.sort(key=lambda x: x[0])
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# 查找当前时间对应的频率
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current_frequency = None
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for minutes, frequency in time_freq_pairs:
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if current_minutes >= minutes:
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current_frequency = frequency
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else:
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break
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# 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）
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if current_frequency is None and time_freq_pairs:
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current_frequency = time_freq_pairs[-1][1]
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return current_frequency
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def _get_stream_specific_frequency(self, chat_stream_id: str):
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"""
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获取特定聊天流在当前时间的频率
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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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float: 频率值，如果没有配置则返回 None
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"""
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# 查找匹配的聊天流配置
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for config_item in self.talk_frequency_adjust:
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if not config_item or len(config_item) < 2:
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continue
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stream_config_str = config_item[0] # 例如 "qq:1026294844:group"
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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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# 使用通用的时间频率解析方法
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return self._get_time_based_frequency(config_item[1:])
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return None
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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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@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 NormalChatConfig(ConfigBase):
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"""普通聊天配置类"""
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willing_mode: str = "classical"
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"""意愿模式"""
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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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enable_expression: bool = True
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"""是否启用表达方式"""
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expression_style: str = ""
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"""表达风格"""
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learning_interval: int = 300
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"""学习间隔（秒）"""
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enable_expression_learning: bool = True
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"""是否启用表达学习"""
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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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@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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emoji_activate_type: str = "random"
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"""表情包激活类型，可选：random，llm，random下，表情包动作随机启用，llm下，表情包动作根据llm判断是否启用"""
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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 MemoryConfig(ConfigBase):
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"""记忆配置类"""
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enable_memory: bool = True
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memory_build_interval: int = 600
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"""记忆构建间隔（秒）"""
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memory_build_distribution: tuple[
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float,
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float,
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float,
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float,
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float,
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float,
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] = field(default_factory=lambda: (6.0, 3.0, 0.6, 32.0, 12.0, 0.4))
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"""记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重"""
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memory_build_sample_num: int = 8
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"""记忆构建采样数量"""
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memory_build_sample_length: int = 40
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"""记忆构建采样长度"""
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memory_compress_rate: float = 0.1
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"""记忆压缩率"""
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forget_memory_interval: int = 1000
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"""记忆遗忘间隔（秒）"""
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memory_forget_time: int = 24
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"""记忆遗忘时间（小时）"""
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memory_forget_percentage: float = 0.01
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"""记忆遗忘比例"""
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consolidate_memory_interval: int = 1000
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"""记忆整合间隔（秒）"""
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consolidation_similarity_threshold: float = 0.7
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"""整合相似度阈值"""
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consolidate_memory_percentage: float = 0.01
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"""整合检查节点比例"""
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memory_ban_words: list[str] = field(default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"])
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"""不允许记忆的词列表"""
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enable_instant_memory: bool = True
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"""是否启用即时记忆"""
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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 CustomPromptConfig(ConfigBase):
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"""自定义提示词配置类"""
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image_prompt: str = ""
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"""图片提示词"""
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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):
|
||
"""遥测配置类"""
|
||
|
||
enable: bool = True
|
||
"""是否启用遥测"""
|
||
|
||
|
||
@dataclass
|
||
class DebugConfig(ConfigBase):
|
||
"""调试配置类"""
|
||
|
||
show_prompt: bool = False
|
||
"""是否显示prompt"""
|
||
|
||
|
||
@dataclass
|
||
class ExperimentalConfig(ConfigBase):
|
||
"""实验功能配置类"""
|
||
|
||
enable_friend_chat: bool = False
|
||
"""是否启用好友聊天"""
|
||
|
||
pfc_chatting: bool = False
|
||
"""是否启用PFC"""
|
||
|
||
|
||
@dataclass
|
||
class MaimMessageConfig(ConfigBase):
|
||
"""maim_message配置类"""
|
||
|
||
use_custom: bool = False
|
||
"""是否使用自定义的maim_message配置"""
|
||
|
||
host: str = "127.0.0.1"
|
||
"""主机地址"""
|
||
|
||
port: int = 8090
|
||
""""端口号"""
|
||
|
||
mode: Literal["ws", "tcp"] = "ws"
|
||
"""连接模式，支持ws和tcp"""
|
||
|
||
use_wss: bool = False
|
||
"""是否使用WSS安全连接"""
|
||
|
||
cert_file: str = ""
|
||
"""SSL证书文件路径，仅在use_wss=True时有效"""
|
||
|
||
key_file: str = ""
|
||
"""SSL密钥文件路径，仅在use_wss=True时有效"""
|
||
|
||
auth_token: list[str] = field(default_factory=lambda: [])
|
||
"""认证令牌，用于API验证，为空则不启用验证"""
|
||
|
||
|
||
@dataclass
|
||
class LPMMKnowledgeConfig(ConfigBase):
|
||
"""LPMM知识库配置类"""
|
||
|
||
enable: bool = True
|
||
"""是否启用LPMM知识库"""
|
||
|
||
rag_synonym_search_top_k: int = 10
|
||
"""RAG同义词搜索的Top K数量"""
|
||
|
||
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"""
|
||
|