mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'PFC-test' of https://github.com/Dax233/MaiMBot into PFC-test
Bakadax
commit
5a89bf4981
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@ -3,6 +3,7 @@ import traceback
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from typing import Tuple, Optional, Dict, Any, List
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from src.common.logger_manager import get_logger
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# from src.individuality.individuality import Individuality
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from src.plugins.utils.chat_message_builder import build_readable_messages
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from ..models.utils_model import LLMRequest
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@ -154,6 +155,7 @@ block_and_ignore: 更加极端的结束对话方式，直接结束对话并在
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注意：请严格按照JSON格式输出，不要包含任何其他内容。"""
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class ActionPlanner:
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"""行动规划器"""
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@ -214,13 +216,12 @@ class ActionPlanner:
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goals_str = self._build_goals_string(conversation_info)
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chat_history_text = await self._build_chat_history_text(observation_info)
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# 获取 sender_name, relationship_text, current_emotion_text
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sender_name_str = getattr(observation_info, 'sender_name', '对方') # 从 observation_info 获取
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sender_name_str = getattr(observation_info, "sender_name", "对方") # 从 observation_info 获取
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if not sender_name_str:
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sender_name_str = '对方' # 再次确保有默认值
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relationship_text_str = getattr(conversation_info, 'relationship_text', '你们还不熟悉。')
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current_emotion_text_str = getattr(conversation_info, 'current_emotion_text', '心情平静。')
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sender_name_str = "对方" # 再次确保有默认值
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relationship_text_str = getattr(conversation_info, "relationship_text", "你们还不熟悉。")
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current_emotion_text_str = getattr(conversation_info, "current_emotion_text", "心情平静。")
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persona_text = f"{self.name}。"
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action_history_summary, last_action_context = self._build_action_history_context(conversation_info)
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@ -237,14 +238,16 @@ class ActionPlanner:
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# --- 2. 选择并格式化 Prompt ---
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try:
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if use_reflect_prompt: # 新增的判断
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if use_reflect_prompt: # 新增的判断
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prompt_template = PROMPT_REFLECT_AND_ACT
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log_msg = "使用 PROMPT_REFLECT_AND_ACT (反思决策)"
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# 对于 PROMPT_REFLECT_AND_ACT，它不包含 send_new_message 选项，所以 spam_warning_message 中的相关提示可以调整或省略
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# 但为了保持占位符填充的一致性，我们仍然计算它
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spam_warning_message = ""
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if conversation_info.my_message_count > 5: # 这里的 my_message_count 仍有意义，表示之前连续发送了多少
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spam_warning_message = f"⚠️【警告】**你之前已连续发送{str(conversation_info.my_message_count)}条消息！请谨慎决策。**"
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if conversation_info.my_message_count > 5: # 这里的 my_message_count 仍有意义，表示之前连续发送了多少
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spam_warning_message = (
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f"⚠️【警告】**你之前已连续发送{str(conversation_info.my_message_count)}条消息！请谨慎决策。**"
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)
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elif conversation_info.my_message_count > 2:
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spam_warning_message = f"💬【提示】**你之前已连续发送{str(conversation_info.my_message_count)}条消息。请注意保持对话平衡。**"
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@ -260,12 +263,12 @@ class ActionPlanner:
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else:
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prompt_template = PROMPT_INITIAL_REPLY
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log_msg = "使用 PROMPT_INITIAL_REPLY (首次/非连续回复决策)"
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spam_warning_message = "" # 初始回复时通常不需要刷屏警告
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spam_warning_message = "" # 初始回复时通常不需要刷屏警告
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logger.debug(f"[私聊][{self.private_name}] {log_msg}")
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current_time_value = "获取时间失败"
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if observation_info and hasattr(observation_info, 'current_time_str') and observation_info.current_time_str:
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if observation_info and hasattr(observation_info, "current_time_str") and observation_info.current_time_str:
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current_time_value = observation_info.current_time_str
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if spam_warning_message:
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@ -285,7 +288,7 @@ class ActionPlanner:
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spam_warning_info=spam_warning_message,
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sender_name=sender_name_str,
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relationship_text=relationship_text_str,
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current_emotion_text=current_emotion_text_str
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current_emotion_text=current_emotion_text_str,
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)
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logger.debug(f"[私聊][{self.private_name}] 发送到LLM的最终提示词:\n------\n{prompt}\n------")
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except KeyError as fmt_key_err:
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@ -333,10 +336,11 @@ class ActionPlanner:
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time_str_for_end_decision = observation_info.current_time_str
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final_action, final_reason = await self._handle_end_conversation_decision(
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persona_text,
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chat_history_text, initial_reason,
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time_str_for_end_decision,
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chat_history_text,
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initial_reason,
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time_str_for_end_decision,
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sender_name_str=sender_name_str,
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relationship_text_str=relationship_text_str
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relationship_text_str=relationship_text_str,
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)
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except Exception as end_dec_err:
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logger.error(f"[私聊][{self.private_name}] 处理结束对话决策时出错: {end_dec_err}")
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@ -361,7 +365,7 @@ class ActionPlanner:
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"block_and_ignore",
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"say_goodbye",
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]
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valid_actions_reflect = [ # PROMPT_REFLECT_AND_ACT 的动作
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valid_actions_reflect = [ # PROMPT_REFLECT_AND_ACT 的动作
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"wait",
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"listening",
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"rethink_goal",
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@ -502,9 +506,7 @@ class ActionPlanner:
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)
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logger.debug(f"[私聊][{self.private_name}] 向 LLM 追加了 {other_unread_count} 条未读消息。")
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else:
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chat_history_text += (
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"\n--- 以上均为已读消息，未读消息均已处理完毕 ---\n"
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)
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chat_history_text += "\n--- 以上均为已读消息，未读消息均已处理完毕 ---\n"
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except AttributeError as e:
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logger.warning(f"[私聊][{self.private_name}] 构建聊天记录文本时属性错误: {e}")
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chat_history_text = "[获取聊天记录时出错]\n"
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@ -562,11 +564,23 @@ class ActionPlanner:
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# --- Helper method for handling end_conversation decision ---
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async def _handle_end_conversation_decision(
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self, persona_text: str, chat_history_text: str, initial_reason: str, current_time_str: str, sender_name_str: str, relationship_text_str: str
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self,
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persona_text: str,
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chat_history_text: str,
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initial_reason: str,
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current_time_str: str,
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sender_name_str: str,
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relationship_text_str: str,
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) -> Tuple[str, str]:
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"""处理结束对话前的告别决策"""
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logger.info(f"[私聊][{self.private_name}] 初步规划结束对话，进入告别决策...")
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end_decision_prompt = PROMPT_END_DECISION.format(persona_text=persona_text, chat_history_text=chat_history_text,current_time_str=current_time_str,sender_name = sender_name_str, relationship_text = relationship_text_str)
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end_decision_prompt = PROMPT_END_DECISION.format(
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persona_text=persona_text,
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chat_history_text=chat_history_text,
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current_time_str=current_time_str,
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sender_name=sender_name_str,
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relationship_text=relationship_text_str,
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)
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logger.debug(f"[私聊][{self.private_name}] 发送到LLM的结束决策提示词:\n------\n{end_decision_prompt}\n------")
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llm_start_time = time.time()
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end_content, _ = await self.llm.generate_response_async(end_decision_prompt)
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@ -295,7 +295,9 @@ async def handle_action(
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# 后续的 plan 循环会检测到这个 "done_no_reply" 状态并使用反思 prompt
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elif is_suitable: # 适用于 direct_reply 或 (send_new_message 且 RG决定发送并通过检查)
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logger.debug(f"[私聊][{conversation_instance.private_name}] 动作 '{action}': 找到合适的回复，准备发送。")
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logger.debug(
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f"[私聊][{conversation_instance.private_name}] 动作 '{action}': 找到合适的回复，准备发送。"
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)
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# conversation_info.last_reply_rejection_reason = None # 已在循环内清除
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# conversation_info.last_rejected_reply_content = None
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conversation_instance.generated_reply = generated_content_for_check_or_send # 使用检查通过的内容
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