Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev
This commit is contained in:
SengokuCola
@@ -1,5 +1,5 @@
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#Programmable Friendly Conversationalist
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#Prefrontal cortex
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# Programmable Friendly Conversationalist
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# Prefrontal cortex
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import datetime
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import asyncio
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from typing import List, Optional, Tuple, TYPE_CHECKING
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@@ -29,20 +29,17 @@ logger = get_module_logger("pfc")
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class GoalAnalyzer:
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"""对话目标分析器"""
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def __init__(self, stream_id: str):
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self.llm = LLM_request(
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model=global_config.llm_normal,
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temperature=0.7,
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max_tokens=1000,
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request_type="conversation_goal"
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model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal"
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)
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self.personality_info = Individuality.get_instance().get_prompt(type = "personality", x_person = 2, level = 2)
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self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=2)
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self.name = global_config.BOT_NICKNAME
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self.nick_name = global_config.BOT_ALIAS_NAMES
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self.chat_observer = ChatObserver.get_instance(stream_id)
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# 多目标存储结构
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self.goals = [] # 存储多个目标
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self.max_goals = 3 # 同时保持的最大目标数量
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@@ -50,10 +47,10 @@ class GoalAnalyzer:
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async def analyze_goal(self) -> Tuple[str, str, str]:
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"""分析对话历史并设定目标
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Args:
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chat_history: 聊天历史记录列表
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Returns:
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Tuple[str, str, str]: (目标, 方法, 原因)
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"""
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@@ -70,16 +67,16 @@ class GoalAnalyzer:
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if sender == self.name:
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sender = "你说"
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chat_history_text += f"{time_str},{sender}:{msg.get('processed_plain_text', '')}\n"
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personality_text = f"你的名字是{self.name}，{self.personality_info}"
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# 构建当前已有目标的文本
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existing_goals_text = ""
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if self.goals:
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existing_goals_text = "当前已有的对话目标:\n"
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for i, (goal, _, reason) in enumerate(self.goals):
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existing_goals_text += f"{i+1}. 目标: {goal}, 原因: {reason}\n"
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existing_goals_text += f"{i + 1}. 目标: {goal}, 原因: {reason}\n"
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prompt = f"""{personality_text}。现在你在参与一场QQ聊天，请分析以下聊天记录，并根据你的性格特征确定多个明确的对话目标。
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这些目标应该反映出对话的不同方面和意图。
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@@ -107,46 +104,44 @@ class GoalAnalyzer:
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logger.debug(f"发送到LLM的提示词: {prompt}")
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content, _ = await self.llm.generate_response_async(prompt)
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logger.debug(f"LLM原始返回内容: {content}")
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# 使用简化函数提取JSON内容
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success, result = get_items_from_json(
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content,
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"goal", "reasoning",
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required_types={"goal": str, "reasoning": str}
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content, "goal", "reasoning", required_types={"goal": str, "reasoning": str}
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)
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if not success:
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logger.error(f"无法解析JSON，重试第{retry + 1}次")
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continue
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goal = result["goal"]
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reasoning = result["reasoning"]
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# 使用默认的方法
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method = "以友好的态度回应"
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# 更新目标列表
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await self._update_goals(goal, method, reasoning)
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# 返回当前最主要的目标
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if self.goals:
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current_goal, current_method, current_reasoning = self.goals[0]
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return current_goal, current_method, current_reasoning
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else:
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return goal, method, reasoning
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except Exception as e:
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logger.error(f"分析对话目标时出错: {str(e)}，重试第{retry + 1}次")
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if retry == max_retries - 1:
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return "保持友好的对话", "以友好的态度回应", "确保对话顺利进行"
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continue
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# 所有重试都失败后的默认返回
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return "保持友好的对话", "以友好的态度回应", "确保对话顺利进行"
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async def _update_goals(self, new_goal: str, method: str, reasoning: str):
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"""更新目标列表
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Args:
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new_goal: 新的目标
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method: 实现目标的方法
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@@ -160,23 +155,23 @@ class GoalAnalyzer:
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# 将此目标移到列表前面（最主要的位置）
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self.goals.insert(0, self.goals.pop(i))
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return
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# 添加新目标到列表前面
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self.goals.insert(0, (new_goal, method, reasoning))
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# 限制目标数量
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if len(self.goals) > self.max_goals:
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self.goals.pop() # 移除最老的目标
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def _calculate_similarity(self, goal1: str, goal2: str) -> float:
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"""简单计算两个目标之间的相似度
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这里使用一个简单的实现，实际可以使用更复杂的文本相似度算法
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Args:
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goal1: 第一个目标
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goal2: 第二个目标
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Returns:
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float: 相似度得分 (0-1)
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"""
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@@ -186,18 +181,18 @@ class GoalAnalyzer:
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overlap = len(words1.intersection(words2))
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total = len(words1.union(words2))
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return overlap / total if total > 0 else 0
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async def get_all_goals(self) -> List[Tuple[str, str, str]]:
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"""获取所有当前目标
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Returns:
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List[Tuple[str, str, str]]: 目标列表，每项为(目标, 方法, 原因)
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"""
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return self.goals.copy()
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async def get_alternative_goals(self) -> List[Tuple[str, str, str]]:
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"""获取除了当前主要目标外的其他备选目标
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Returns:
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List[Tuple[str, str, str]]: 备选目标列表
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"""
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@@ -215,9 +210,9 @@ class GoalAnalyzer:
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if sender == self.name:
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sender = "你说"
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chat_history_text += f"{time_str},{sender}:{msg.get('processed_plain_text', '')}\n"
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personality_text = f"你的名字是{self.name}，{self.personality_info}"
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prompt = f"""{personality_text}。现在你在参与一场QQ聊天，
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当前对话目标：{goal}
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产生该对话目标的原因：{reasoning}
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@@ -247,7 +242,7 @@ class GoalAnalyzer:
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"goal_achieved", "stop_conversation", "reason",
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required_types={"goal_achieved": bool, "stop_conversation": bool, "reason": str}
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)
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if not success:
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logger.error("无法解析对话分析结果JSON")
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return False, False, "解析结果失败"
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@@ -265,14 +260,15 @@ class GoalAnalyzer:
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class Waiter:
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"""快 速 等 待"""
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def __init__(self, stream_id: str):
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self.chat_observer = ChatObserver.get_instance(stream_id)
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self.personality_info = Individuality.get_instance().get_prompt(type = "personality", x_person = 2, level = 2)
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self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=2)
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self.name = global_config.BOT_NICKNAME
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async def wait(self) -> bool:
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"""等待
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Returns:
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bool: 是否超时（True表示超时）
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"""
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@@ -298,7 +294,7 @@ class Waiter:
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class DirectMessageSender:
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"""直接发送消息到平台的发送器"""
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def __init__(self):
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self.logger = get_module_logger("direct_sender")
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self.storage = MessageStorage()
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@@ -310,7 +306,7 @@ class DirectMessageSender:
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reply_to_message: Optional[Message] = None,
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) -> None:
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"""直接发送消息到平台
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Args:
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chat_stream: 聊天流
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content: 消息内容
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@@ -323,7 +319,7 @@ class DirectMessageSender:
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user_nickname=global_config.BOT_NICKNAME,
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platform=chat_stream.platform,
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)
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message = MessageSending(
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message_id=f"dm{round(time.time(), 2)}",
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chat_stream=chat_stream,
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@@ -343,18 +339,17 @@ class DirectMessageSender:
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try:
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message_json = message.to_dict()
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end_point = global_config.api_urls.get(chat_stream.platform, None)
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if not end_point:
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raise ValueError(f"未找到平台：{chat_stream.platform} 的url配置")
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await global_api.send_message_REST(end_point, message_json)
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# 存储消息
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await self.storage.store_message(message, message.chat_stream)
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self.logger.info(f"直接发送消息成功: {content[:30]}...")
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except Exception as e:
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self.logger.error(f"直接发送消息失败: {str(e)}")
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raise
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