feat(chat): 实现并发消息处理系统
引入了一个全新的并发消息处理系统，以显著提升在高活跃度群聊中的响应速度。 在此之前，消息管理器对每个聊天流（如一个群聊）内的所有消息进行串行处理，导致用户需要排队等待机器人响应。新系统引入了可配置的并发模式： - 通过 `concurrent_message_processing` 开关启用。 - 允许并行处理来自同一群聊中不同用户的消息。 - 通过 `process_by_user_id` 保证对同一用户的消息处理仍然是串行的，以维持上下文的连贯性。 - 使用 `concurrent_per_user_limit` 控制并发处理的用户数量。 为了支持此功能，对 `MessageManager` 进行了大规模重构，用更高效的独立流检查机制取代了旧的全局轮询和优先级排序逻辑。同时，清理和移除了大量已废弃或冗余的配置项，简化了整体配置。 BREAKING CHANGE: 移除了多个已废弃的 `ChatConfig` 配置项，包括 `mentioned_bot_inevitable_reply`, `at_bot_inevitable_reply`, `focus_value`, `group_chat_mode` 等。这些功能已被新的 AFC 逻辑或其它机制取代。请参考最新的配置文件模板进行更新。
This commit is contained in:
tt-P607
Windpicker-owo
@@ -104,20 +104,9 @@ class ChatConfig(ValidatedConfigBase):
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"""聊天配置类"""
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max_context_size: int = Field(default=18, description="最大上下文大小")
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replyer_random_probability: float = Field(default=0.5, description="回复者随机概率")
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thinking_timeout: int = Field(default=40, description="思考超时时间")
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talk_frequency: float = Field(default=1.0, description="聊天频率")
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mentioned_bot_inevitable_reply: bool = Field(default=False, description="提到机器人的必然回复")
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at_bot_inevitable_reply: bool = Field(default=False, description="@机器人的必然回复")
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allow_reply_self: bool = Field(default=False, description="是否允许回复自己说的话")
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talk_frequency_adjust: list[list[str]] = Field(default_factory=lambda: [], description="聊天频率调整")
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focus_value: float = Field(default=1.0, description="专注值")
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focus_mode_quiet_groups: List[str] = Field(
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default_factory=list,
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description='专注模式下需要保持安静的群组列表, 格式: ["platform:group_id1", "platform:group_id2"]',
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)
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force_reply_private: bool = Field(default=False, description="强制回复私聊")
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group_chat_mode: Literal["auto", "normal", "focus"] = Field(default="auto", description="群聊模式")
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timestamp_display_mode: Literal["normal", "normal_no_YMD", "relative"] = Field(
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default="normal_no_YMD", description="时间戳显示模式"
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)
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@@ -158,47 +147,57 @@ class ChatConfig(ValidatedConfigBase):
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dynamic_distribution_jitter_factor: float = Field(
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default=0.2, ge=0.0, le=0.5, description="分发间隔随机扰动因子"
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)
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# 并发消息处理
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concurrent_message_processing: bool = Field(
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default=False, description="是否启用并发消息处理，在同一聊天流中并行处理多个消息"
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)
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concurrent_per_user_limit: int = Field(
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default=3, description="在并发模式下，每个聊天流（群/私聊）同时处理的最大用户数"
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)
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process_by_user_id: bool = Field(
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default=True, description="在并发模式下，是否按用户ID进行独立串行处理"
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)
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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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if not self.talk_frequency_adjust:
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return self.talk_frequency
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return 1.0
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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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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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# 检查全局时段配置（第一个元素为空字符串的配置）
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global_frequency = self._get_global_frequency()
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return self.talk_frequency if global_frequency is None else global_frequency
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@staticmethod
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def _get_time_based_frequency(time_freq_list: list[str]) -> Optional[float]:
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return 1.0 if global_frequency is None else global_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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@@ -210,13 +209,13 @@ class ChatConfig(ValidatedConfigBase):
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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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@@ -224,20 +223,20 @@ class ChatConfig(ValidatedConfigBase):
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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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@@ -245,31 +244,30 @@ class ChatConfig(ValidatedConfigBase):
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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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@staticmethod
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def _parse_stream_config_to_chat_id(stream_config_str: str) -> Optional[str]:
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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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@@ -277,42 +275,42 @@ class ChatConfig(ValidatedConfigBase):
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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_global_frequency(self) -> Optional[float]:
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"""
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获取全局默认频率配置
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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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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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# 检查是否为全局默认配置（第一个元素为空字符串）
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if config_item[0] == "":
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return self._get_time_based_frequency(config_item[1:])
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return None
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@@ -346,10 +344,10 @@ class ExpressionConfig(ValidatedConfigBase):
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def _parse_stream_config_to_chat_id(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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@@ -357,52 +355,52 @@ class ExpressionConfig(ValidatedConfigBase):
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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, float]:
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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.rules:
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# 如果没有配置，使用默认值：启用表达，启用学习，强度1.0
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return True, True, 1.0
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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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for rule in self.rules:
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if rule.chat_stream_id and self._parse_stream_config_to_chat_id(rule.chat_stream_id) == chat_stream_id:
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return rule.use_expression, rule.learn_expression, rule.learning_strength
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# 检查全局配置（chat_stream_id为空字符串的配置）
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for rule in self.rules:
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if rule.chat_stream_id == "":
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return rule.use_expression, rule.learn_expression, rule.learning_strength
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# 如果都没有匹配，返回默认值
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return True, True, 1.0
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@@ -476,7 +474,7 @@ class KeywordRuleConfig(ValidatedConfigBase):
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def __post_init__(self):
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import re
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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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@@ -499,7 +497,6 @@ class CustomPromptConfig(ValidatedConfigBase):
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"""自定义提示词配置类"""
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image_prompt: str = Field(default="", description="图片提示词")
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planner_custom_prompt_enable: bool = Field(default=False, description="启用规划器自定义提示词")
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planner_custom_prompt_content: str = Field(default="", description="规划器自定义提示词内容")
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