Merge remote-tracking branch 'origin/dev' into pr/1404

CODE_OF_CONDUCT.md

@@ -71,7 +71,6 @@
 
 1. **GitHub Issues**: 对于公开的违规行为，可以在相关issue中直接指出
 2. **私下联系**: 可以通过GitHub私信联系项目维护者
-3. **邮件联系**: [如果有项目邮箱地址，请在此提供]
 
 所有报告都将得到及时和公正的处理。我们承诺保护报告者的隐私和安全。
 

changelogs/changelog.md

@@ -1,5 +1,11 @@
 # Changelog
 
+## [0.11.7] - 2025-12-2
+- 增加麦麦做梦功能
+
+- 添加全局记忆配置项
+
+
 ## [0.11.6] - 2025-12-2
 ### 🌟 重大更新
 - 大幅提高记忆检索能力，略微提高token消耗

docker-compose.yml

@@ -27,7 +27,7 @@ services:
     # image: infinitycat/maibot:dev
     environment:
       - TZ=Asia/Shanghai
-#      - EULA_AGREE=1b662741904d7155d1ce1c00b3530d0d # 同意EULA
+#      - EULA_AGREE=99f08e0cab0190de853cb6af7d64d4de # 同意EULA
 #      - PRIVACY_AGREE=9943b855e72199d0f5016ea39052f1b6 # 同意EULA
     ports:
       - "18001:8001" # webui端口

dummy

@@ -0,0 +1,10 @@
+{
+  "cells": [],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

pyproject.toml

@@ -14,6 +14,7 @@ dependencies = [
     "json-repair>=0.47.6",
     "maim-message",
     "matplotlib>=3.10.3",
+    "msgpack>=1.1.2",
     "numpy>=2.2.6",
     "openai>=1.95.0",
     "pandas>=2.3.1",
@@ -23,6 +24,7 @@ dependencies = [
     "pydantic>=2.11.7",
     "pypinyin>=0.54.0",
     "python-dotenv>=1.1.1",
+    "python-multipart>=0.0.20",
     "quick-algo>=0.1.3",
     "rich>=14.0.0",
     "ruff>=0.12.2",
@@ -32,6 +34,7 @@ dependencies = [
     "tomlkit>=0.13.3",
     "urllib3>=2.5.0",
     "uvicorn>=0.35.0",
+    "zstandard>=0.25.0",
 ]
 
 

src/chat/brain_chat/PFC/action_planner.py

@@ -0,0 +1,491 @@
+import time
+from typing import Tuple, Optional  # 增加了 Optional
+from src.common.logger_manager import get_logger
+from ..models.utils_model import LLMRequest
+from ...config.config import global_config
+from .chat_observer import ChatObserver
+from .pfc_utils import get_items_from_json
+from src.individuality.individuality import Individuality
+from .observation_info import ObservationInfo
+from .conversation_info import ConversationInfo
+from src.plugins.utils.chat_message_builder import build_readable_messages
+
+
+logger = get_logger("pfc_action_planner")
+
+
+# --- 定义 Prompt 模板 ---
+
+# Prompt(1): 首次回复或非连续回复时的决策 Prompt
+PROMPT_INITIAL_REPLY = """{persona_text}。现在你在参与一场QQ私聊，请根据以下【所有信息】审慎且灵活的决策下一步行动，可以回复，可以倾听，可以调取知识，甚至可以屏蔽对方：
+
+【当前对话目标】
+{goals_str}
+{knowledge_info_str}
+
+【最近行动历史概要】
+{action_history_summary}
+【上一次行动的详细情况和结果】
+{last_action_context}
+【时间和超时提示】
+{time_since_last_bot_message_info}{timeout_context}
+【最近的对话记录】(包括你已成功发送的消息 和 新收到的消息)
+{chat_history_text}
+
+------
+可选行动类型以及解释：
+fetch_knowledge: 需要调取知识或记忆，当需要专业知识或特定信息时选择，对方若提到你不太认识的人名或实体也可以尝试选择
+listening: 倾听对方发言，当你认为对方话才说到一半，发言明显未结束时选择
+direct_reply: 直接回复对方
+rethink_goal: 思考一个对话目标，当你觉得目前对话需要目标，或当前目标不再适用，或话题卡住时选择。注意私聊的环境是灵活的，有可能需要经常选择
+end_conversation: 结束对话，对方长时间没回复或者当你觉得对话告一段落时可以选择
+block_and_ignore: 更加极端的结束对话方式，直接结束对话并在一段时间内无视对方所有发言（屏蔽），当对话让你感到十分不适，或你遭到各类骚扰时选择
+
+请以JSON格式输出你的决策：
+{{
+    "action": "选择的行动类型 (必须是上面列表中的一个)",
+    "reason": "选择该行动的详细原因 (必须有解释你是如何根据“上一次行动结果”、“对话记录”和自身设定人设做出合理判断的)"
+}}
+
+注意：请严格按照JSON格式输出，不要包含任何其他内容。"""
+
+# Prompt(2): 上一次成功回复后，决定继续发言时的决策 Prompt
+PROMPT_FOLLOW_UP = """{persona_text}。现在你在参与一场QQ私聊，刚刚你已经回复了对方，请根据以下【所有信息】审慎且灵活的决策下一步行动，可以继续发送新消息，可以等待，可以倾听，可以调取知识，甚至可以屏蔽对方： 
+
+【当前对话目标】
+{goals_str}
+{knowledge_info_str}
+
+【最近行动历史概要】
+{action_history_summary}
+【上一次行动的详细情况和结果】
+{last_action_context}
+【时间和超时提示】
+{time_since_last_bot_message_info}{timeout_context} 
+【最近的对话记录】(包括你已成功发送的消息 和 新收到的消息)
+{chat_history_text}
+
+------
+可选行动类型以及解释：
+fetch_knowledge: 需要调取知识，当需要专业知识或特定信息时选择，对方若提到你不太认识的人名或实体也可以尝试选择
+wait: 暂时不说话，留给对方交互空间，等待对方回复（尤其是在你刚发言后、或上次发言因重复、发言过多被拒时、或不确定做什么时，这是不错的选择）
+listening: 倾听对方发言（虽然你刚发过言，但如果对方立刻回复且明显话没说完，可以选择这个）
+send_new_message: 发送一条新消息继续对话，允许适当的追问、补充、深入话题，或开启相关新话题。**但是避免在因重复被拒后立即使用，也不要在对方没有回复的情况下过多的“消息轰炸”或重复发言**
+rethink_goal: 思考一个对话目标，当你觉得目前对话需要目标，或当前目标不再适用，或话题卡住时选择。注意私聊的环境是灵活的，有可能需要经常选择
+end_conversation: 结束对话，对方长时间没回复或者当你觉得对话告一段落时可以选择
+block_and_ignore: 更加极端的结束对话方式，直接结束对话并在一段时间内无视对方所有发言（屏蔽），当对话让你感到十分不适，或你遭到各类骚扰时选择
+
+请以JSON格式输出你的决策：
+{{
+    "action": "选择的行动类型 (必须是上面列表中的一个)",
+    "reason": "选择该行动的详细原因 (必须有解释你是如何根据“上一次行动结果”、“对话记录”和自身设定人设做出合理判断的。请说明你为什么选择继续发言而不是等待，以及打算发送什么类型的新消息连续发言，必须记录已经发言了几次)"
+}}
+
+注意：请严格按照JSON格式输出，不要包含任何其他内容。"""
+
+# 新增：Prompt(3): 决定是否在结束对话前发送告别语
+PROMPT_END_DECISION = """{persona_text}。刚刚你决定结束一场 QQ 私聊。
+
+【你们之前的聊天记录】
+{chat_history_text}
+
+你觉得你们的对话已经完整结束了吗？有时候，在对话自然结束后再说点什么可能会有点奇怪，但有时也可能需要一条简短的消息来圆满结束。
+如果觉得确实有必要再发一条简短、自然、符合你人设的告别消息（比如 "好，下次再聊~" 或 "嗯，先这样吧"），就输出 "yes"。
+如果觉得当前状态下直接结束对话更好，没有必要再发消息，就输出 "no"。
+
+请以 JSON 格式输出你的选择：
+{{
+    "say_bye": "yes/no",
+    "reason": "选择 yes 或 no 的原因和内心想法 (简要说明)"
+}}
+
+注意：请严格按照 JSON 格式输出，不要包含任何其他内容。"""
+
+
+# ActionPlanner 类定义，顶格
+class ActionPlanner:
+    """行动规划器"""
+
+    def __init__(self, stream_id: str, private_name: str):
+        self.llm = LLMRequest(
+            model=global_config.llm_PFC_action_planner,
+            temperature=global_config.llm_PFC_action_planner["temp"],
+            max_tokens=1500,
+            request_type="action_planning",
+        )
+        self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
+        self.name = global_config.BOT_NICKNAME
+        self.private_name = private_name
+        self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
+        # self.action_planner_info = ActionPlannerInfo() # 移除未使用的变量
+
+    # 修改 plan 方法签名，增加 last_successful_reply_action 参数
+    async def plan(
+        self,
+        observation_info: ObservationInfo,
+        conversation_info: ConversationInfo,
+        last_successful_reply_action: Optional[str],
+    ) -> Tuple[str, str]:
+        """规划下一步行动
+
+        Args:
+            observation_info: 决策信息
+            conversation_info: 对话信息
+            last_successful_reply_action: 上一次成功的回复动作类型 ('direct_reply' 或 'send_new_message' 或 None)
+
+        Returns:
+            Tuple[str, str]: (行动类型, 行动原因)
+        """
+        # --- 获取 Bot 上次发言时间信息 ---
+        # (这部分逻辑不变)
+        time_since_last_bot_message_info = ""
+        try:
+            bot_id = str(global_config.BOT_QQ)
+            if hasattr(observation_info, "chat_history") and observation_info.chat_history:
+                for i in range(len(observation_info.chat_history) - 1, -1, -1):
+                    msg = observation_info.chat_history[i]
+                    if not isinstance(msg, dict):
+                        continue
+                    sender_info = msg.get("user_info", {})
+                    sender_id = str(sender_info.get("user_id")) if isinstance(sender_info, dict) else None
+                    msg_time = msg.get("time")
+                    if sender_id == bot_id and msg_time:
+                        time_diff = time.time() - msg_time
+                        if time_diff < 60.0:
+                            time_since_last_bot_message_info = (
+                                f"提示：你上一条成功发送的消息是在 {time_diff:.1f} 秒前。\n"
+                            )
+                        break
+            else:
+                logger.debug(
+                    f"[私聊][{self.private_name}]Observation info chat history is empty or not available for bot time check."
+                )
+        except AttributeError:
+            logger.warning(
+                f"[私聊][{self.private_name}]ObservationInfo object might not have chat_history attribute yet for bot time check."
+            )
+        except Exception as e:
+            logger.warning(f"[私聊][{self.private_name}]获取 Bot 上次发言时间时出错: {e}")
+
+        # --- 获取超时提示信息 ---
+        # (这部分逻辑不变)
+        timeout_context = ""
+        try:
+            if hasattr(conversation_info, "goal_list") and conversation_info.goal_list:
+                last_goal_dict = conversation_info.goal_list[-1]
+                if isinstance(last_goal_dict, dict) and "goal" in last_goal_dict:
+                    last_goal_text = last_goal_dict["goal"]
+                    if isinstance(last_goal_text, str) and "分钟，思考接下来要做什么" in last_goal_text:
+                        try:
+                            timeout_minutes_text = last_goal_text.split("，")[0].replace("你等待了", "")
+                            timeout_context = f"重要提示：对方已经长时间（{timeout_minutes_text}）没有回复你的消息了（这可能代表对方繁忙/不想回复/没注意到你的消息等情况，或在对方看来本次聊天已告一段落），请基于此情况规划下一步。\n"
+                        except Exception:
+                            timeout_context = "重要提示：对方已经长时间没有回复你的消息了（这可能代表对方繁忙/不想回复/没注意到你的消息等情况，或在对方看来本次聊天已告一段落），请基于此情况规划下一步。\n"
+            else:
+                logger.debug(
+                    f"[私聊][{self.private_name}]Conversation info goal_list is empty or not available for timeout check."
+                )
+        except AttributeError:
+            logger.warning(
+                f"[私聊][{self.private_name}]ConversationInfo object might not have goal_list attribute yet for timeout check."
+            )
+        except Exception as e:
+            logger.warning(f"[私聊][{self.private_name}]检查超时目标时出错: {e}")
+
+        # --- 构建通用 Prompt 参数 ---
+        logger.debug(
+            f"[私聊][{self.private_name}]开始规划行动：当前目标: {getattr(conversation_info, 'goal_list', '不可用')}"
+        )
+
+        # 构建对话目标 (goals_str)
+        goals_str = ""
+        try:
+            if hasattr(conversation_info, "goal_list") and conversation_info.goal_list:
+                for goal_reason in conversation_info.goal_list:
+                    if isinstance(goal_reason, dict):
+                        goal = goal_reason.get("goal", "目标内容缺失")
+                        reasoning = goal_reason.get("reasoning", "没有明确原因")
+                    else:
+                        goal = str(goal_reason)
+                        reasoning = "没有明确原因"
+
+                    goal = str(goal) if goal is not None else "目标内容缺失"
+                    reasoning = str(reasoning) if reasoning is not None else "没有明确原因"
+                    goals_str += f"- 目标：{goal}\n  原因：{reasoning}\n"
+
+                if not goals_str:
+                    goals_str = "- 目前没有明确对话目标，请考虑设定一个。\n"
+            else:
+                goals_str = "- 目前没有明确对话目标，请考虑设定一个。\n"
+        except AttributeError:
+            logger.warning(
+                f"[私聊][{self.private_name}]ConversationInfo object might not have goal_list attribute yet."
+            )
+            goals_str = "- 获取对话目标时出错。\n"
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]构建对话目标字符串时出错: {e}")
+            goals_str = "- 构建对话目标时出错。\n"
+
+        # --- 知识信息字符串构建开始 ---
+        knowledge_info_str = "【已获取的相关知识和记忆】\n"
+        try:
+            # 检查 conversation_info 是否有 knowledge_list 并且不为空
+            if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list:
+                # 最多只显示最近的 5 条知识，防止 Prompt 过长
+                recent_knowledge = conversation_info.knowledge_list[-5:]
+                for i, knowledge_item in enumerate(recent_knowledge):
+                    if isinstance(knowledge_item, dict):
+                        query = knowledge_item.get("query", "未知查询")
+                        knowledge = knowledge_item.get("knowledge", "无知识内容")
+                        source = knowledge_item.get("source", "未知来源")
+                        # 只取知识内容的前 2000 个字，避免太长
+                        knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge
+                        knowledge_info_str += (
+                            f"{i + 1}. 关于 '{query}' 的知识 (来源: {source}):\n   {knowledge_snippet}\n"
+                        )
+                    else:
+                        # 处理列表里不是字典的异常情况
+                        knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n"
+
+                if not recent_knowledge:  # 如果 knowledge_list 存在但为空
+                    knowledge_info_str += "- 暂无相关知识和记忆。\n"
+
+            else:
+                # 如果 conversation_info 没有 knowledge_list 属性，或者列表为空
+                knowledge_info_str += "- 暂无相关知识记忆。\n"
+        except AttributeError:
+            logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。")
+            knowledge_info_str += "- 获取知识列表时出错。\n"
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}")
+            knowledge_info_str += "- 处理知识列表时出错。\n"
+        # --- 知识信息字符串构建结束 ---
+
+        # 获取聊天历史记录 (chat_history_text)
+        try:
+            if hasattr(observation_info, "chat_history") and observation_info.chat_history:
+                chat_history_text = observation_info.chat_history_str
+                if not chat_history_text:
+                    chat_history_text = "还没有聊天记录。\n"
+            else:
+                chat_history_text = "还没有聊天记录。\n"
+
+            if hasattr(observation_info, "new_messages_count") and observation_info.new_messages_count > 0:
+                if hasattr(observation_info, "unprocessed_messages") and observation_info.unprocessed_messages:
+                    new_messages_list = observation_info.unprocessed_messages
+                    new_messages_str = await build_readable_messages(
+                        new_messages_list,
+                        replace_bot_name=True,
+                        merge_messages=False,
+                        timestamp_mode="relative",
+                        read_mark=0.0,
+                    )
+                    chat_history_text += (
+                        f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}"
+                    )
+                else:
+                    logger.warning(
+                        f"[私聊][{self.private_name}]ObservationInfo has new_messages_count > 0 but unprocessed_messages is empty or missing."
+                    )
+        except AttributeError:
+            logger.warning(
+                f"[私聊][{self.private_name}]ObservationInfo object might be missing expected attributes for chat history."
+            )
+            chat_history_text = "获取聊天记录时出错。\n"
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]处理聊天记录时发生未知错误: {e}")
+            chat_history_text = "处理聊天记录时出错。\n"
+
+        # 构建 Persona 文本 (persona_text)
+        persona_text = f"你的名字是{self.name}，{self.personality_info}。"
+
+        # 构建行动历史和上一次行动结果 (action_history_summary, last_action_context)
+        # (这部分逻辑不变)
+        action_history_summary = "你最近执行的行动历史：\n"
+        last_action_context = "关于你【上一次尝试】的行动：\n"
+        action_history_list = []
+        try:
+            if hasattr(conversation_info, "done_action") and conversation_info.done_action:
+                action_history_list = conversation_info.done_action[-5:]
+            else:
+                logger.debug(f"[私聊][{self.private_name}]Conversation info done_action is empty or not available.")
+        except AttributeError:
+            logger.warning(
+                f"[私聊][{self.private_name}]ConversationInfo object might not have done_action attribute yet."
+            )
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]访问行动历史时出错: {e}")
+
+        if not action_history_list:
+            action_history_summary += "- 还没有执行过行动。\n"
+            last_action_context += "- 这是你规划的第一个行动。\n"
+        else:
+            for i, action_data in enumerate(action_history_list):
+                action_type = "未知"
+                plan_reason = "未知"
+                status = "未知"
+                final_reason = ""
+                action_time = ""
+
+                if isinstance(action_data, dict):
+                    action_type = action_data.get("action", "未知")
+                    plan_reason = action_data.get("plan_reason", "未知规划原因")
+                    status = action_data.get("status", "未知")
+                    final_reason = action_data.get("final_reason", "")
+                    action_time = action_data.get("time", "")
+                elif isinstance(action_data, tuple):
+                    # 假设旧格式兼容
+                    if len(action_data) > 0:
+                        action_type = action_data[0]
+                    if len(action_data) > 1:
+                        plan_reason = action_data[1]  # 可能是规划原因或最终原因
+                    if len(action_data) > 2:
+                        status = action_data[2]
+                    if status == "recall" and len(action_data) > 3:
+                        final_reason = action_data[3]
+                    elif status == "done" and action_type in ["direct_reply", "send_new_message"]:
+                        plan_reason = "成功发送"  # 简化显示
+
+                reason_text = f", 失败/取消原因: {final_reason}" if final_reason else ""
+                summary_line = f"- 时间:{action_time}, 尝试行动:'{action_type}', 状态:{status}{reason_text}"
+                action_history_summary += summary_line + "\n"
+
+                if i == len(action_history_list) - 1:
+                    last_action_context += f"- 上次【规划】的行动是: '{action_type}'\n"
+                    last_action_context += f"- 当时规划的【原因】是: {plan_reason}\n"
+                    if status == "done":
+                        last_action_context += "- 该行动已【成功执行】。\n"
+                        # 记录这次成功的行动类型，供下次决策
+                        # self.last_successful_action_type = action_type # 不在这里记录，由 conversation 控制
+                    elif status == "recall":
+                        last_action_context += "- 但该行动最终【未能执行/被取消】。\n"
+                        if final_reason:
+                            last_action_context += f"- 【重要】失败/取消的具体原因是: “{final_reason}”\n"
+                        else:
+                            last_action_context += "- 【重要】失败/取消原因未明确记录。\n"
+                        # self.last_successful_action_type = None # 行动失败，清除记录
+                    else:
+                        last_action_context += f"- 该行动当前状态: {status}\n"
+                        # self.last_successful_action_type = None # 非完成状态，清除记录
+
+        # --- 选择 Prompt ---
+        if last_successful_reply_action in ["direct_reply", "send_new_message"]:
+            prompt_template = PROMPT_FOLLOW_UP
+            logger.debug(f"[私聊][{self.private_name}]使用 PROMPT_FOLLOW_UP (追问决策)")
+        else:
+            prompt_template = PROMPT_INITIAL_REPLY
+            logger.debug(f"[私聊][{self.private_name}]使用 PROMPT_INITIAL_REPLY (首次/非连续回复决策)")
+
+        # --- 格式化最终的 Prompt ---
+        prompt = prompt_template.format(
+            persona_text=persona_text,
+            goals_str=goals_str if goals_str.strip() else "- 目前没有明确对话目标，请考虑设定一个。",
+            action_history_summary=action_history_summary,
+            last_action_context=last_action_context,
+            time_since_last_bot_message_info=time_since_last_bot_message_info,
+            timeout_context=timeout_context,
+            chat_history_text=chat_history_text if chat_history_text.strip() else "还没有聊天记录。",
+            knowledge_info_str=knowledge_info_str,
+        )
+
+        logger.debug(f"[私聊][{self.private_name}]发送到LLM的最终提示词:\n------\n{prompt}\n------")
+        try:
+            content, _ = await self.llm.generate_response_async(prompt)
+            logger.debug(f"[私聊][{self.private_name}]LLM (行动规划) 原始返回内容: {content}")
+
+            # --- 初始行动规划解析 ---
+            success, initial_result = get_items_from_json(
+                content,
+                self.private_name,
+                "action",
+                "reason",
+                default_values={"action": "wait", "reason": "LLM返回格式错误或未提供原因，默认等待"},
+            )
+
+            initial_action = initial_result.get("action", "wait")
+            initial_reason = initial_result.get("reason", "LLM未提供原因，默认等待")
+
+            # 检查是否需要进行结束对话决策 ---
+            if initial_action == "end_conversation":
+                logger.info(f"[私聊][{self.private_name}]初步规划结束对话，进入告别决策...")
+
+                # 使用新的 PROMPT_END_DECISION
+                end_decision_prompt = PROMPT_END_DECISION.format(
+                    persona_text=persona_text,  # 复用之前的 persona_text
+                    chat_history_text=chat_history_text,  # 复用之前的 chat_history_text
+                )
+
+                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------"
+                )
+                try:
+                    end_content, _ = await self.llm.generate_response_async(end_decision_prompt)  # 再次调用LLM
+                    logger.debug(f"[私聊][{self.private_name}]LLM (结束决策) 原始返回内容: {end_content}")
+
+                    # 解析结束决策的JSON
+                    end_success, end_result = get_items_from_json(
+                        end_content,
+                        self.private_name,
+                        "say_bye",
+                        "reason",
+                        default_values={"say_bye": "no", "reason": "结束决策LLM返回格式错误，默认不告别"},
+                        required_types={"say_bye": str, "reason": str},  # 明确类型
+                    )
+
+                    say_bye_decision = end_result.get("say_bye", "no").lower()  # 转小写方便比较
+                    end_decision_reason = end_result.get("reason", "未提供原因")
+
+                    if end_success and say_bye_decision == "yes":
+                        # 决定要告别，返回新的 'say_goodbye' 动作
+                        logger.info(
+                            f"[私聊][{self.private_name}]结束决策: yes, 准备生成告别语. 原因: {end_decision_reason}"
+                        )
+                        # 注意：这里的 reason 可以考虑拼接初始原因和结束决策原因，或者只用结束决策原因
+                        final_action = "say_goodbye"
+                        final_reason = f"决定发送告别语。决策原因: {end_decision_reason} (原结束理由: {initial_reason})"
+                        return final_action, final_reason
+                    else:
+                        # 决定不告别 (包括解析失败或明确说no)
+                        logger.info(
+                            f"[私聊][{self.private_name}]结束决策: no, 直接结束对话. 原因: {end_decision_reason}"
+                        )
+                        # 返回原始的 'end_conversation' 动作
+                        final_action = "end_conversation"
+                        final_reason = initial_reason  # 保持原始的结束理由
+                        return final_action, final_reason
+
+                except Exception as end_e:
+                    logger.error(f"[私聊][{self.private_name}]调用结束决策LLM或处理结果时出错: {str(end_e)}")
+                    # 出错时，默认执行原始的结束对话
+                    logger.warning(f"[私聊][{self.private_name}]结束决策出错，将按原计划执行 end_conversation")
+                    return "end_conversation", initial_reason  # 返回原始动作和原因
+
+            else:
+                action = initial_action
+                reason = initial_reason
+
+                # 验证action类型 (保持不变)
+                valid_actions = [
+                    "direct_reply",
+                    "send_new_message",
+                    "fetch_knowledge",
+                    "wait",
+                    "listening",
+                    "rethink_goal",
+                    "end_conversation",  # 仍然需要验证，因为可能从上面决策后返回
+                    "block_and_ignore",
+                    "say_goodbye",  # 也要验证这个新动作
+                ]
+                if action not in valid_actions:
+                    logger.warning(f"[私聊][{self.private_name}]LLM返回了未知的行动类型: '{action}'，强制改为 wait")
+                    reason = f"(原始行动'{action}'无效，已强制改为wait) {reason}"
+                    action = "wait"
+
+                logger.info(f"[私聊][{self.private_name}]规划的行动: {action}")
+                logger.info(f"[私聊][{self.private_name}]行动原因: {reason}")
+                return action, reason
+
+        except Exception as e:
+            # 外层异常处理保持不变
+            logger.error(f"[私聊][{self.private_name}]规划行动时调用 LLM 或处理结果出错: {str(e)}")
+            return "wait", f"行动规划处理中发生错误，暂时等待: {str(e)}"

src/chat/brain_chat/PFC/chat_observer.py

@@ -0,0 +1,379 @@
+import time
+import asyncio
+import traceback
+from typing import Optional, Dict, Any, List
+from src.common.logger import get_module_logger
+from maim_message import UserInfo
+from ...config.config import global_config
+from .chat_states import NotificationManager, create_new_message_notification, create_cold_chat_notification
+from .message_storage import MongoDBMessageStorage
+from rich.traceback import install
+
+install(extra_lines=3)
+
+logger = get_module_logger("chat_observer")
+
+
+class ChatObserver:
+    """聊天状态观察器"""
+
+    # 类级别的实例管理
+    _instances: Dict[str, "ChatObserver"] = {}
+
+    @classmethod
+    def get_instance(cls, stream_id: str, private_name: str) -> "ChatObserver":
+        """获取或创建观察器实例
+
+        Args:
+            stream_id: 聊天流ID
+            private_name: 私聊名称
+
+        Returns:
+            ChatObserver: 观察器实例
+        """
+        if stream_id not in cls._instances:
+            cls._instances[stream_id] = cls(stream_id, private_name)
+        return cls._instances[stream_id]
+
+    def __init__(self, stream_id: str, private_name: str):
+        """初始化观察器
+
+        Args:
+            stream_id: 聊天流ID
+        """
+        self.last_check_time = None
+        self.last_bot_speak_time = None
+        self.last_user_speak_time = None
+        if stream_id in self._instances:
+            raise RuntimeError(f"ChatObserver for {stream_id} already exists. Use get_instance() instead.")
+
+        self.stream_id = stream_id
+        self.private_name = private_name
+        self.message_storage = MongoDBMessageStorage()
+
+        # self.last_user_speak_time: Optional[float] = None  # 对方上次发言时间
+        # self.last_bot_speak_time: Optional[float] = None  # 机器人上次发言时间
+        # self.last_check_time: float = time.time()  # 上次查看聊天记录时间
+        self.last_message_read: Optional[Dict[str, Any]] = None  # 最后读取的消息ID
+        self.last_message_time: float = time.time()
+
+        self.waiting_start_time: float = time.time()  # 等待开始时间，初始化为当前时间
+
+        # 运行状态
+        self._running: bool = False
+        self._task: Optional[asyncio.Task] = None
+        self._update_event = asyncio.Event()  # 触发更新的事件
+        self._update_complete = asyncio.Event()  # 更新完成的事件
+
+        # 通知管理器
+        self.notification_manager = NotificationManager()
+
+        # 冷场检查配置
+        self.cold_chat_threshold: float = 60.0  # 60秒无消息判定为冷场
+        self.last_cold_chat_check: float = time.time()
+        self.is_cold_chat_state: bool = False
+
+        self.update_event = asyncio.Event()
+        self.update_interval = 2  # 更新间隔（秒）
+        self.message_cache = []
+        self.update_running = False
+
+    async def check(self) -> bool:
+        """检查距离上一次观察之后是否有了新消息
+
+        Returns:
+            bool: 是否有新消息
+        """
+        logger.debug(f"[私聊][{self.private_name}]检查距离上一次观察之后是否有了新消息: {self.last_check_time}")
+
+        new_message_exists = await self.message_storage.has_new_messages(self.stream_id, self.last_check_time)
+
+        if new_message_exists:
+            logger.debug(f"[私聊][{self.private_name}]发现新消息")
+            self.last_check_time = time.time()
+
+        return new_message_exists
+
+    async def _add_message_to_history(self, message: Dict[str, Any]):
+        """添加消息到历史记录并发送通知
+
+        Args:
+            message: 消息数据
+        """
+        try:
+            # 发送新消息通知
+            notification = create_new_message_notification(
+                sender="chat_observer", target="observation_info", message=message
+            )
+            # print(self.notification_manager)
+            await self.notification_manager.send_notification(notification)
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]添加消息到历史记录时出错: {e}")
+            print(traceback.format_exc())
+
+        # 检查并更新冷场状态
+        await self._check_cold_chat()
+
+    async def _check_cold_chat(self):
+        """检查是否处于冷场状态并发送通知"""
+        current_time = time.time()
+
+        # 每10秒检查一次冷场状态
+        if current_time - self.last_cold_chat_check < 10:
+            return
+
+        self.last_cold_chat_check = current_time
+
+        # 判断是否冷场
+        is_cold = (
+            True
+            if self.last_message_time is None
+            else (current_time - self.last_message_time) > self.cold_chat_threshold
+        )
+
+        # 如果冷场状态发生变化，发送通知
+        if is_cold != self.is_cold_chat_state:
+            self.is_cold_chat_state = is_cold
+            notification = create_cold_chat_notification(sender="chat_observer", target="pfc", is_cold=is_cold)
+            await self.notification_manager.send_notification(notification)
+
+    def new_message_after(self, time_point: float) -> bool:
+        """判断是否在指定时间点后有新消息
+
+        Args:
+            time_point: 时间戳
+
+        Returns:
+            bool: 是否有新消息
+        """
+
+        if self.last_message_time is None:
+            logger.debug(f"[私聊][{self.private_name}]没有最后消息时间，返回 False")
+            return False
+
+        has_new = self.last_message_time > time_point
+        logger.debug(
+            f"[私聊][{self.private_name}]判断是否在指定时间点后有新消息: {self.last_message_time} > {time_point} = {has_new}"
+        )
+        return has_new
+
+    def get_message_history(
+        self,
+        start_time: Optional[float] = None,
+        end_time: Optional[float] = None,
+        limit: Optional[int] = None,
+        user_id: Optional[str] = None,
+    ) -> List[Dict[str, Any]]:
+        """获取消息历史
+
+        Args:
+            start_time: 开始时间戳
+            end_time: 结束时间戳
+            limit: 限制返回消息数量
+            user_id: 指定用户ID
+
+        Returns:
+            List[Dict[str, Any]]: 消息列表
+        """
+        filtered_messages = self.message_history
+
+        if start_time is not None:
+            filtered_messages = [m for m in filtered_messages if m["time"] >= start_time]
+
+        if end_time is not None:
+            filtered_messages = [m for m in filtered_messages if m["time"] <= end_time]
+
+        if user_id is not None:
+            filtered_messages = [
+                m for m in filtered_messages if UserInfo.from_dict(m.get("user_info", {})).user_id == user_id
+            ]
+
+        if limit is not None:
+            filtered_messages = filtered_messages[-limit:]
+
+        return filtered_messages
+
+    async def _fetch_new_messages(self) -> List[Dict[str, Any]]:
+        """获取新消息
+
+        Returns:
+            List[Dict[str, Any]]: 新消息列表
+        """
+        new_messages = await self.message_storage.get_messages_after(self.stream_id, self.last_message_time)
+
+        if new_messages:
+            self.last_message_read = new_messages[-1]
+            self.last_message_time = new_messages[-1]["time"]
+
+        # print(f"获取数据库中找到的新消息: {new_messages}")
+
+        return new_messages
+
+    async def _fetch_new_messages_before(self, time_point: float) -> List[Dict[str, Any]]:
+        """获取指定时间点之前的消息
+
+        Args:
+            time_point: 时间戳
+
+        Returns:
+            List[Dict[str, Any]]: 最多5条消息
+        """
+        new_messages = await self.message_storage.get_messages_before(self.stream_id, time_point)
+
+        if new_messages:
+            self.last_message_read = new_messages[-1]["message_id"]
+
+        logger.debug(f"[私聊][{self.private_name}]获取指定时间点111之前的消息: {new_messages}")
+
+        return new_messages
+
+    """主要观察循环"""
+
+    async def _update_loop(self):
+        """更新循环"""
+        # try:
+        #     start_time = time.time()
+        #     messages = await self._fetch_new_messages_before(start_time)
+        #     for message in messages:
+        #         await self._add_message_to_history(message)
+        #     logger.debug(f"[私聊][{self.private_name}]缓冲消息: {messages}")
+        # except Exception as e:
+        #     logger.error(f"[私聊][{self.private_name}]缓冲消息出错: {e}")
+
+        while self._running:
+            try:
+                # 等待事件或超时（1秒）
+                try:
+                    # print("等待事件")
+                    await asyncio.wait_for(self._update_event.wait(), timeout=1)
+
+                except asyncio.TimeoutError:
+                    # print("超时")
+                    pass  # 超时后也执行一次检查
+
+                self._update_event.clear()  # 重置触发事件
+                self._update_complete.clear()  # 重置完成事件
+
+                # 获取新消息
+                new_messages = await self._fetch_new_messages()
+
+                if new_messages:
+                    # 处理新消息
+                    for message in new_messages:
+                        await self._add_message_to_history(message)
+
+                # 设置完成事件
+                self._update_complete.set()
+
+            except Exception as e:
+                logger.error(f"[私聊][{self.private_name}]更新循环出错: {e}")
+                logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
+                self._update_complete.set()  # 即使出错也要设置完成事件
+
+    def trigger_update(self):
+        """触发一次立即更新"""
+        self._update_event.set()
+
+    async def wait_for_update(self, timeout: float = 5.0) -> bool:
+        """等待更新完成
+
+        Args:
+            timeout: 超时时间（秒）
+
+        Returns:
+            bool: 是否成功完成更新（False表示超时）
+        """
+        try:
+            await asyncio.wait_for(self._update_complete.wait(), timeout=timeout)
+            return True
+        except asyncio.TimeoutError:
+            logger.warning(f"[私聊][{self.private_name}]等待更新完成超时（{timeout}秒）")
+            return False
+
+    def start(self):
+        """启动观察器"""
+        if self._running:
+            return
+
+        self._running = True
+        self._task = asyncio.create_task(self._update_loop())
+        logger.debug(f"[私聊][{self.private_name}]ChatObserver for {self.stream_id} started")
+
+    def stop(self):
+        """停止观察器"""
+        self._running = False
+        self._update_event.set()  # 设置事件以解除等待
+        self._update_complete.set()  # 设置完成事件以解除等待
+        if self._task:
+            self._task.cancel()
+        logger.debug(f"[私聊][{self.private_name}]ChatObserver for {self.stream_id} stopped")
+
+    async def process_chat_history(self, messages: list):
+        """处理聊天历史
+
+        Args:
+            messages: 消息列表
+        """
+        self.update_check_time()
+
+        for msg in messages:
+            try:
+                user_info = UserInfo.from_dict(msg.get("user_info", {}))
+                if user_info.user_id == global_config.BOT_QQ:
+                    self.update_bot_speak_time(msg["time"])
+                else:
+                    self.update_user_speak_time(msg["time"])
+            except Exception as e:
+                logger.warning(f"[私聊][{self.private_name}]处理消息时间时出错: {e}")
+                continue
+
+    def update_check_time(self):
+        """更新查看时间"""
+        self.last_check_time = time.time()
+
+    def update_bot_speak_time(self, speak_time: Optional[float] = None):
+        """更新机器人说话时间"""
+        self.last_bot_speak_time = speak_time or time.time()
+
+    def update_user_speak_time(self, speak_time: Optional[float] = None):
+        """更新用户说话时间"""
+        self.last_user_speak_time = speak_time or time.time()
+
+    def get_time_info(self) -> str:
+        """获取时间信息文本"""
+        current_time = time.time()
+        time_info = ""
+
+        if self.last_bot_speak_time:
+            bot_speak_ago = current_time - self.last_bot_speak_time
+            time_info += f"\n距离你上次发言已经过去了{int(bot_speak_ago)}秒"
+
+        if self.last_user_speak_time:
+            user_speak_ago = current_time - self.last_user_speak_time
+            time_info += f"\n距离对方上次发言已经过去了{int(user_speak_ago)}秒"
+
+        return time_info
+
+    def get_cached_messages(self, limit: int = 50) -> List[Dict[str, Any]]:
+        """获取缓存的消息历史
+
+        Args:
+            limit: 获取的最大消息数量，默认50
+
+        Returns:
+            List[Dict[str, Any]]: 缓存的消息历史列表
+        """
+        return self.message_cache[-limit:]
+
+    def get_last_message(self) -> Optional[Dict[str, Any]]:
+        """获取最后一条消息
+
+        Returns:
+            Optional[Dict[str, Any]]: 最后一条消息，如果没有则返回None
+        """
+        if not self.message_cache:
+            return None
+        return self.message_cache[-1]
+
+    def __str__(self):
+        return f"ChatObserver for {self.stream_id}"

src/chat/brain_chat/PFC/chat_states.py

@@ -0,0 +1,290 @@
+from enum import Enum, auto
+from typing import Optional, Dict, Any, List, Set
+from dataclasses import dataclass
+from datetime import datetime
+from abc import ABC, abstractmethod
+
+
+class ChatState(Enum):
+    """聊天状态枚举"""
+
+    NORMAL = auto()  # 正常状态
+    NEW_MESSAGE = auto()  # 有新消息
+    COLD_CHAT = auto()  # 冷场状态
+    ACTIVE_CHAT = auto()  # 活跃状态
+    BOT_SPEAKING = auto()  # 机器人正在说话
+    USER_SPEAKING = auto()  # 用户正在说话
+    SILENT = auto()  # 沉默状态
+    ERROR = auto()  # 错误状态
+
+
+class NotificationType(Enum):
+    """通知类型枚举"""
+
+    NEW_MESSAGE = auto()  # 新消息通知
+    COLD_CHAT = auto()  # 冷场通知
+    ACTIVE_CHAT = auto()  # 活跃通知
+    BOT_SPEAKING = auto()  # 机器人说话通知
+    USER_SPEAKING = auto()  # 用户说话通知
+    MESSAGE_DELETED = auto()  # 消息删除通知
+    USER_JOINED = auto()  # 用户加入通知
+    USER_LEFT = auto()  # 用户离开通知
+    ERROR = auto()  # 错误通知
+
+
+@dataclass
+class ChatStateInfo:
+    """聊天状态信息"""
+
+    state: ChatState
+    last_message_time: Optional[float] = None
+    last_message_content: Optional[str] = None
+    last_speaker: Optional[str] = None
+    message_count: int = 0
+    cold_duration: float = 0.0  # 冷场持续时间（秒）
+    active_duration: float = 0.0  # 活跃持续时间（秒）
+
+
+@dataclass
+class Notification:
+    """通知基类"""
+
+    type: NotificationType
+    timestamp: float
+    sender: str  # 发送者标识
+    target: str  # 接收者标识
+    data: Dict[str, Any]
+
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典格式"""
+        return {"type": self.type.name, "timestamp": self.timestamp, "data": self.data}
+
+
+@dataclass
+class StateNotification(Notification):
+    """持续状态通知"""
+
+    is_active: bool = True
+
+    def to_dict(self) -> Dict[str, Any]:
+        base_dict = super().to_dict()
+        base_dict["is_active"] = self.is_active
+        return base_dict
+
+
+class NotificationHandler(ABC):
+    """通知处理器接口"""
+
+    @abstractmethod
+    async def handle_notification(self, notification: Notification):
+        """处理通知"""
+        pass
+
+
+class NotificationManager:
+    """通知管理器"""
+
+    def __init__(self):
+        # 按接收者和通知类型存储处理器
+        self._handlers: Dict[str, Dict[NotificationType, List[NotificationHandler]]] = {}
+        self._active_states: Set[NotificationType] = set()
+        self._notification_history: List[Notification] = []
+
+    def register_handler(self, target: str, notification_type: NotificationType, handler: NotificationHandler):
+        """注册通知处理器
+
+        Args:
+            target: 接收者标识（例如："pfc"）
+            notification_type: 要处理的通知类型
+            handler: 处理器实例
+        """
+        if target not in self._handlers:
+            self._handlers[target] = {}
+        if notification_type not in self._handlers[target]:
+            self._handlers[target][notification_type] = []
+            # print(self._handlers[target][notification_type])
+        self._handlers[target][notification_type].append(handler)
+        # print(self._handlers[target][notification_type])
+
+    def unregister_handler(self, target: str, notification_type: NotificationType, handler: NotificationHandler):
+        """注销通知处理器
+
+        Args:
+            target: 接收者标识
+            notification_type: 通知类型
+            handler: 要注销的处理器实例
+        """
+        if target in self._handlers and notification_type in self._handlers[target]:
+            handlers = self._handlers[target][notification_type]
+            if handler in handlers:
+                handlers.remove(handler)
+                # 如果该类型的处理器列表为空，删除该类型
+                if not handlers:
+                    del self._handlers[target][notification_type]
+                    # 如果该目标没有任何处理器，删除该目标
+                    if not self._handlers[target]:
+                        del self._handlers[target]
+
+    async def send_notification(self, notification: Notification):
+        """发送通知"""
+        self._notification_history.append(notification)
+
+        # 如果是状态通知，更新活跃状态
+        if isinstance(notification, StateNotification):
+            if notification.is_active:
+                self._active_states.add(notification.type)
+            else:
+                self._active_states.discard(notification.type)
+
+        # 调用目标接收者的处理器
+        target = notification.target
+        if target in self._handlers:
+            handlers = self._handlers[target].get(notification.type, [])
+            # print(handlers)
+            for handler in handlers:
+                # print(f"调用处理器: {handler}")
+                await handler.handle_notification(notification)
+
+    def get_active_states(self) -> Set[NotificationType]:
+        """获取当前活跃的状态"""
+        return self._active_states.copy()
+
+    def is_state_active(self, state_type: NotificationType) -> bool:
+        """检查特定状态是否活跃"""
+        return state_type in self._active_states
+
+    def get_notification_history(
+        self, sender: Optional[str] = None, target: Optional[str] = None, limit: Optional[int] = None
+    ) -> List[Notification]:
+        """获取通知历史
+
+        Args:
+            sender: 过滤特定发送者的通知
+            target: 过滤特定接收者的通知
+            limit: 限制返回数量
+        """
+        history = self._notification_history
+
+        if sender:
+            history = [n for n in history if n.sender == sender]
+        if target:
+            history = [n for n in history if n.target == target]
+
+        if limit is not None:
+            history = history[-limit:]
+
+        return history
+
+    def __str__(self):
+        str = ""
+        for target, handlers in self._handlers.items():
+            for notification_type, handler_list in handlers.items():
+                str += f"NotificationManager for {target} {notification_type} {handler_list}"
+        return str
+
+
+# 一些常用的通知创建函数
+def create_new_message_notification(sender: str, target: str, message: Dict[str, Any]) -> Notification:
+    """创建新消息通知"""
+    return Notification(
+        type=NotificationType.NEW_MESSAGE,
+        timestamp=datetime.now().timestamp(),
+        sender=sender,
+        target=target,
+        data={
+            "message_id": message.get("message_id"),
+            "processed_plain_text": message.get("processed_plain_text"),
+            "detailed_plain_text": message.get("detailed_plain_text"),
+            "user_info": message.get("user_info"),
+            "time": message.get("time"),
+        },
+    )
+
+
+def create_cold_chat_notification(sender: str, target: str, is_cold: bool) -> StateNotification:
+    """创建冷场状态通知"""
+    return StateNotification(
+        type=NotificationType.COLD_CHAT,
+        timestamp=datetime.now().timestamp(),
+        sender=sender,
+        target=target,
+        data={"is_cold": is_cold},
+        is_active=is_cold,
+    )
+
+
+def create_active_chat_notification(sender: str, target: str, is_active: bool) -> StateNotification:
+    """创建活跃状态通知"""
+    return StateNotification(
+        type=NotificationType.ACTIVE_CHAT,
+        timestamp=datetime.now().timestamp(),
+        sender=sender,
+        target=target,
+        data={"is_active": is_active},
+        is_active=is_active,
+    )
+
+
+class ChatStateManager:
+    """聊天状态管理器"""
+
+    def __init__(self):
+        self.current_state = ChatState.NORMAL
+        self.state_info = ChatStateInfo(state=ChatState.NORMAL)
+        self.state_history: list[ChatStateInfo] = []
+
+    def update_state(self, new_state: ChatState, **kwargs):
+        """更新聊天状态
+
+        Args:
+            new_state: 新的状态
+            **kwargs: 其他状态信息
+        """
+        self.current_state = new_state
+        self.state_info.state = new_state
+
+        # 更新其他状态信息
+        for key, value in kwargs.items():
+            if hasattr(self.state_info, key):
+                setattr(self.state_info, key, value)
+
+        # 记录状态历史
+        self.state_history.append(self.state_info)
+
+    def get_current_state_info(self) -> ChatStateInfo:
+        """获取当前状态信息"""
+        return self.state_info
+
+    def get_state_history(self) -> list[ChatStateInfo]:
+        """获取状态历史"""
+        return self.state_history
+
+    def is_cold_chat(self, threshold: float = 60.0) -> bool:
+        """判断是否处于冷场状态
+
+        Args:
+            threshold: 冷场阈值（秒）
+
+        Returns:
+            bool: 是否冷场
+        """
+        if not self.state_info.last_message_time:
+            return True
+
+        current_time = datetime.now().timestamp()
+        return (current_time - self.state_info.last_message_time) > threshold
+
+    def is_active_chat(self, threshold: float = 5.0) -> bool:
+        """判断是否处于活跃状态
+
+        Args:
+            threshold: 活跃阈值（秒）
+
+        Returns:
+            bool: 是否活跃
+        """
+        if not self.state_info.last_message_time:
+            return False
+
+        current_time = datetime.now().timestamp()
+        return (current_time - self.state_info.last_message_time) <= threshold

src/chat/brain_chat/PFC/conversation.py

@@ -0,0 +1,701 @@
+import time
+import asyncio
+import datetime
+
+# from .message_storage import MongoDBMessageStorage
+from src.plugins.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
+
+# from ...config.config import global_config
+from typing import Dict, Any, Optional
+from ..chat.message import Message
+from .pfc_types import ConversationState
+from .pfc import ChatObserver, GoalAnalyzer
+from .message_sender import DirectMessageSender
+from src.common.logger_manager import get_logger
+from .action_planner import ActionPlanner
+from .observation_info import ObservationInfo
+from .conversation_info import ConversationInfo  # 确保导入 ConversationInfo
+from .reply_generator import ReplyGenerator
+from ..chat.chat_stream import ChatStream
+from maim_message import UserInfo
+from src.plugins.chat.chat_stream import chat_manager
+from .pfc_KnowledgeFetcher import KnowledgeFetcher
+from .waiter import Waiter
+
+import traceback
+from rich.traceback import install
+
+install(extra_lines=3)
+
+logger = get_logger("pfc")
+
+
+class Conversation:
+    """对话类，负责管理单个对话的状态和行为"""
+
+    def __init__(self, stream_id: str, private_name: str):
+        """初始化对话实例
+
+        Args:
+            stream_id: 聊天流ID
+        """
+        self.stream_id = stream_id
+        self.private_name = private_name
+        self.state = ConversationState.INIT
+        self.should_continue = False
+        self.ignore_until_timestamp: Optional[float] = None
+
+        # 回复相关
+        self.generated_reply = ""
+
+    async def _initialize(self):
+        """初始化实例，注册所有组件"""
+
+        try:
+            self.action_planner = ActionPlanner(self.stream_id, self.private_name)
+            self.goal_analyzer = GoalAnalyzer(self.stream_id, self.private_name)
+            self.reply_generator = ReplyGenerator(self.stream_id, self.private_name)
+            self.knowledge_fetcher = KnowledgeFetcher(self.private_name)
+            self.waiter = Waiter(self.stream_id, self.private_name)
+            self.direct_sender = DirectMessageSender(self.private_name)
+
+            # 获取聊天流信息
+            self.chat_stream = chat_manager.get_stream(self.stream_id)
+
+            self.stop_action_planner = False
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]初始化对话实例：注册运行组件失败: {e}")
+            logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
+            raise
+
+        try:
+            # 决策所需要的信息，包括自身自信和观察信息两部分
+            # 注册观察器和观测信息
+            self.chat_observer = ChatObserver.get_instance(self.stream_id, self.private_name)
+            self.chat_observer.start()
+            self.observation_info = ObservationInfo(self.private_name)
+            self.observation_info.bind_to_chat_observer(self.chat_observer)
+            # print(self.chat_observer.get_cached_messages(limit=)
+
+            self.conversation_info = ConversationInfo()
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]初始化对话实例：注册信息组件失败: {e}")
+            logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
+            raise
+        try:
+            logger.info(f"[私聊][{self.private_name}]为 {self.stream_id} 加载初始聊天记录...")
+            initial_messages = get_raw_msg_before_timestamp_with_chat(  #
+                chat_id=self.stream_id,
+                timestamp=time.time(),
+                limit=30,  # 加载最近30条作为初始上下文，可以调整
+            )
+            chat_talking_prompt = await build_readable_messages(
+                initial_messages,
+                replace_bot_name=True,
+                merge_messages=False,
+                timestamp_mode="relative",
+                read_mark=0.0,
+            )
+            if initial_messages:
+                # 将加载的消息填充到 ObservationInfo 的 chat_history
+                self.observation_info.chat_history = initial_messages
+                self.observation_info.chat_history_str = chat_talking_prompt + "\n"
+                self.observation_info.chat_history_count = len(initial_messages)
+
+                # 更新 ObservationInfo 中的时间戳等信息
+                last_msg = initial_messages[-1]
+                self.observation_info.last_message_time = last_msg.get("time")
+                last_user_info = UserInfo.from_dict(last_msg.get("user_info", {}))
+                self.observation_info.last_message_sender = last_user_info.user_id
+                self.observation_info.last_message_content = last_msg.get("processed_plain_text", "")
+
+                logger.info(
+                    f"[私聊][{self.private_name}]成功加载 {len(initial_messages)} 条初始聊天记录。最后一条消息时间: {self.observation_info.last_message_time}"
+                )
+
+                # 让 ChatObserver 从加载的最后一条消息之后开始同步
+                self.chat_observer.last_message_time = self.observation_info.last_message_time
+                self.chat_observer.last_message_read = last_msg  # 更新 observer 的最后读取记录
+            else:
+                logger.info(f"[私聊][{self.private_name}]没有找到初始聊天记录。")
+
+        except Exception as load_err:
+            logger.error(f"[私聊][{self.private_name}]加载初始聊天记录时出错: {load_err}")
+            # 出错也要继续，只是没有历史记录而已
+        # 组件准备完成，启动该论对话
+        self.should_continue = True
+        asyncio.create_task(self.start())
+
+    async def start(self):
+        """开始对话流程"""
+        try:
+            logger.info(f"[私聊][{self.private_name}]对话系统启动中...")
+            asyncio.create_task(self._plan_and_action_loop())
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]启动对话系统失败: {e}")
+            raise
+
+    async def _plan_and_action_loop(self):
+        """思考步，PFC核心循环模块"""
+        while self.should_continue:
+            # 忽略逻辑
+            if self.ignore_until_timestamp and time.time() < self.ignore_until_timestamp:
+                await asyncio.sleep(30)
+                continue
+            elif self.ignore_until_timestamp and time.time() >= self.ignore_until_timestamp:
+                logger.info(f"[私聊][{self.private_name}]忽略时间已到 {self.stream_id}，准备结束对话。")
+                self.ignore_until_timestamp = None
+                self.should_continue = False
+                continue
+            try:
+                # --- 在规划前记录当前新消息数量 ---
+                initial_new_message_count = 0
+                if hasattr(self.observation_info, "new_messages_count"):
+                    initial_new_message_count = self.observation_info.new_messages_count + 1  # 算上麦麦自己发的那一条
+                else:
+                    logger.warning(
+                        f"[私聊][{self.private_name}]ObservationInfo missing 'new_messages_count' before planning."
+                    )
+
+                # --- 调用 Action Planner ---
+                # 传递 self.conversation_info.last_successful_reply_action
+                action, reason = await self.action_planner.plan(
+                    self.observation_info, self.conversation_info, self.conversation_info.last_successful_reply_action
+                )
+
+                # --- 规划后检查是否有 *更多* 新消息到达 ---
+                current_new_message_count = 0
+                if hasattr(self.observation_info, "new_messages_count"):
+                    current_new_message_count = self.observation_info.new_messages_count
+                else:
+                    logger.warning(
+                        f"[私聊][{self.private_name}]ObservationInfo missing 'new_messages_count' after planning."
+                    )
+
+                if current_new_message_count > initial_new_message_count + 2:
+                    logger.info(
+                        f"[私聊][{self.private_name}]规划期间发现新增消息 ({initial_new_message_count} -> {current_new_message_count})，跳过本次行动，重新规划"
+                    )
+                    # 如果规划期间有新消息，也应该重置上次回复状态，因为现在要响应新消息了
+                    self.conversation_info.last_successful_reply_action = None
+                    await asyncio.sleep(0.1)
+                    continue
+
+                #  包含 send_new_message
+                if initial_new_message_count > 0 and action in ["direct_reply", "send_new_message"]:
+                    if hasattr(self.observation_info, "clear_unprocessed_messages"):
+                        logger.debug(
+                            f"[私聊][{self.private_name}]准备执行 {action}，清理 {initial_new_message_count} 条规划时已知的新消息。"
+                        )
+                        await self.observation_info.clear_unprocessed_messages()
+                        if hasattr(self.observation_info, "new_messages_count"):
+                            self.observation_info.new_messages_count = 0
+                    else:
+                        logger.error(
+                            f"[私聊][{self.private_name}]无法清理未处理消息: ObservationInfo 缺少 clear_unprocessed_messages 方法！"
+                        )
+
+                await self._handle_action(action, reason, self.observation_info, self.conversation_info)
+
+                # 检查是否需要结束对话 (逻辑不变)
+                goal_ended = False
+                if hasattr(self.conversation_info, "goal_list") and self.conversation_info.goal_list:
+                    for goal_item in self.conversation_info.goal_list:
+                        if isinstance(goal_item, dict):
+                            current_goal = goal_item.get("goal")
+
+                        if current_goal == "结束对话":
+                            goal_ended = True
+                            break
+
+                if goal_ended:
+                    self.should_continue = False
+                    logger.info(f"[私聊][{self.private_name}]检测到'结束对话'目标，停止循环。")
+
+            except Exception as loop_err:
+                logger.error(f"[私聊][{self.private_name}]PFC主循环出错: {loop_err}")
+                logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
+                await asyncio.sleep(1)
+
+            if self.should_continue:
+                await asyncio.sleep(0.1)
+
+        logger.info(f"[私聊][{self.private_name}]PFC 循环结束 for stream_id: {self.stream_id}")
+
+    def _check_new_messages_after_planning(self):
+        """检查在规划后是否有新消息"""
+        # 检查 ObservationInfo 是否已初始化并且有 new_messages_count 属性
+        if not hasattr(self, "observation_info") or not hasattr(self.observation_info, "new_messages_count"):
+            logger.warning(
+                f"[私聊][{self.private_name}]ObservationInfo 未初始化或缺少 'new_messages_count' 属性，无法检查新消息。"
+            )
+            return False  # 或者根据需要抛出错误
+
+        if self.observation_info.new_messages_count > 2:
+            logger.info(
+                f"[私聊][{self.private_name}]生成/执行动作期间收到 {self.observation_info.new_messages_count} 条新消息，取消当前动作并重新规划"
+            )
+            # 如果有新消息，也应该重置上次回复状态
+            if hasattr(self, "conversation_info"):  # 确保 conversation_info 已初始化
+                self.conversation_info.last_successful_reply_action = None
+            else:
+                logger.warning(
+                    f"[私聊][{self.private_name}]ConversationInfo 未初始化，无法重置 last_successful_reply_action。"
+                )
+            return True
+        return False
+
+    def _convert_to_message(self, msg_dict: Dict[str, Any]) -> Message:
+        """将消息字典转换为Message对象"""
+        try:
+            # 尝试从 msg_dict 直接获取 chat_stream，如果失败则从全局 chat_manager 获取
+            chat_info = msg_dict.get("chat_info")
+            if chat_info and isinstance(chat_info, dict):
+                chat_stream = ChatStream.from_dict(chat_info)
+            elif self.chat_stream:  # 使用实例变量中的 chat_stream
+                chat_stream = self.chat_stream
+            else:  # Fallback: 尝试从 manager 获取 (可能需要 stream_id)
+                chat_stream = chat_manager.get_stream(self.stream_id)
+                if not chat_stream:
+                    raise ValueError(f"无法确定 ChatStream for stream_id {self.stream_id}")
+
+            user_info = UserInfo.from_dict(msg_dict.get("user_info", {}))
+
+            return Message(
+                message_id=msg_dict.get("message_id", f"gen_{time.time()}"),  # 提供默认 ID
+                chat_stream=chat_stream,  # 使用确定的 chat_stream
+                time=msg_dict.get("time", time.time()),  # 提供默认时间
+                user_info=user_info,
+                processed_plain_text=msg_dict.get("processed_plain_text", ""),
+                detailed_plain_text=msg_dict.get("detailed_plain_text", ""),
+            )
+        except Exception as e:
+            logger.warning(f"[私聊][{self.private_name}]转换消息时出错: {e}")
+            # 可以选择返回 None 或重新抛出异常，这里选择重新抛出以指示问题
+            raise ValueError(f"无法将字典转换为 Message 对象: {e}") from e
+
+    async def _handle_action(
+        self, action: str, reason: str, observation_info: ObservationInfo, conversation_info: ConversationInfo
+    ):
+        """处理规划的行动"""
+
+        logger.debug(f"[私聊][{self.private_name}]执行行动: {action}, 原因: {reason}")
+
+        # 记录action历史 (逻辑不变)
+        current_action_record = {
+            "action": action,
+            "plan_reason": reason,
+            "status": "start",
+            "time": datetime.datetime.now().strftime("%H:%M:%S"),
+            "final_reason": None,
+        }
+        # 确保 done_action 列表存在
+        if not hasattr(conversation_info, "done_action"):
+            conversation_info.done_action = []
+        conversation_info.done_action.append(current_action_record)
+        action_index = len(conversation_info.done_action) - 1
+
+        action_successful = False  # 用于标记动作是否成功完成
+
+        # --- 根据不同的 action 执行 ---
+
+        # send_new_message 失败后执行 wait
+        if action == "send_new_message":
+            max_reply_attempts = 3
+            reply_attempt_count = 0
+            is_suitable = False
+            need_replan = False
+            check_reason = "未进行尝试"
+            final_reply_to_send = ""
+
+            while reply_attempt_count < max_reply_attempts and not is_suitable:
+                reply_attempt_count += 1
+                logger.info(
+                    f"[私聊][{self.private_name}]尝试生成追问回复 (第 {reply_attempt_count}/{max_reply_attempts} 次)..."
+                )
+                self.state = ConversationState.GENERATING
+
+                # 1. 生成回复 (调用 generate 时传入 action_type)
+                self.generated_reply = await self.reply_generator.generate(
+                    observation_info, conversation_info, action_type="send_new_message"
+                )
+                logger.info(
+                    f"[私聊][{self.private_name}]第 {reply_attempt_count} 次生成的追问回复: {self.generated_reply}"
+                )
+
+                # 2. 检查回复 (逻辑不变)
+                self.state = ConversationState.CHECKING
+                try:
+                    current_goal_str = conversation_info.goal_list[0]["goal"] if conversation_info.goal_list else ""
+                    is_suitable, check_reason, need_replan = await self.reply_generator.check_reply(
+                        reply=self.generated_reply,
+                        goal=current_goal_str,
+                        chat_history=observation_info.chat_history,
+                        chat_history_str=observation_info.chat_history_str,
+                        retry_count=reply_attempt_count - 1,
+                    )
+                    logger.info(
+                        f"[私聊][{self.private_name}]第 {reply_attempt_count} 次追问检查结果: 合适={is_suitable}, 原因='{check_reason}', 需重新规划={need_replan}"
+                    )
+                    if is_suitable:
+                        final_reply_to_send = self.generated_reply
+                        break
+                    elif need_replan:
+                        logger.warning(
+                            f"[私聊][{self.private_name}]第 {reply_attempt_count} 次追问检查建议重新规划，停止尝试。原因: {check_reason}"
+                        )
+                        break
+                except Exception as check_err:
+                    logger.error(
+                        f"[私聊][{self.private_name}]第 {reply_attempt_count} 次调用 ReplyChecker (追问) 时出错: {check_err}"
+                    )
+                    check_reason = f"第 {reply_attempt_count} 次检查过程出错: {check_err}"
+                    break
+
+            # 循环结束，处理最终结果
+            if is_suitable:
+                # 检查是否有新消息
+                if self._check_new_messages_after_planning():
+                    logger.info(f"[私聊][{self.private_name}]生成追问回复期间收到新消息，取消发送，重新规划行动")
+                    conversation_info.done_action[action_index].update(
+                        {"status": "recall", "final_reason": f"有新消息，取消发送追问: {final_reply_to_send}"}
+                    )
+                    return  # 直接返回，重新规划
+
+                # 发送合适的回复
+                self.generated_reply = final_reply_to_send
+                # --- 在这里调用 _send_reply ---
+                await self._send_reply()  # <--- 调用恢复后的函数
+
+                # 更新状态: 标记上次成功是 send_new_message
+                self.conversation_info.last_successful_reply_action = "send_new_message"
+                action_successful = True  # 标记动作成功
+
+            elif need_replan:
+                # 打回动作决策
+                logger.warning(
+                    f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试，追问回复决定打回动作决策。打回原因: {check_reason}"
+                )
+                conversation_info.done_action[action_index].update(
+                    {"status": "recall", "final_reason": f"追问尝试{reply_attempt_count}次后打回: {check_reason}"}
+                )
+
+            else:
+                # 追问失败
+                logger.warning(
+                    f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试，未能生成合适的追问回复。最终原因: {check_reason}"
+                )
+                conversation_info.done_action[action_index].update(
+                    {"status": "recall", "final_reason": f"追问尝试{reply_attempt_count}次后失败: {check_reason}"}
+                )
+                # 重置状态: 追问失败，下次用初始 prompt
+                self.conversation_info.last_successful_reply_action = None
+
+                # 执行 Wait 操作
+                logger.info(f"[私聊][{self.private_name}]由于无法生成合适追问回复，执行 'wait' 操作...")
+                self.state = ConversationState.WAITING
+                await self.waiter.wait(self.conversation_info)
+                wait_action_record = {
+                    "action": "wait",
+                    "plan_reason": "因 send_new_message 多次尝试失败而执行的后备等待",
+                    "status": "done",
+                    "time": datetime.datetime.now().strftime("%H:%M:%S"),
+                    "final_reason": None,
+                }
+                conversation_info.done_action.append(wait_action_record)
+
+        elif action == "direct_reply":
+            max_reply_attempts = 3
+            reply_attempt_count = 0
+            is_suitable = False
+            need_replan = False
+            check_reason = "未进行尝试"
+            final_reply_to_send = ""
+
+            while reply_attempt_count < max_reply_attempts and not is_suitable:
+                reply_attempt_count += 1
+                logger.info(
+                    f"[私聊][{self.private_name}]尝试生成首次回复 (第 {reply_attempt_count}/{max_reply_attempts} 次)..."
+                )
+                self.state = ConversationState.GENERATING
+
+                # 1. 生成回复
+                self.generated_reply = await self.reply_generator.generate(
+                    observation_info, conversation_info, action_type="direct_reply"
+                )
+                logger.info(
+                    f"[私聊][{self.private_name}]第 {reply_attempt_count} 次生成的首次回复: {self.generated_reply}"
+                )
+
+                # 2. 检查回复
+                self.state = ConversationState.CHECKING
+                try:
+                    current_goal_str = conversation_info.goal_list[0]["goal"] if conversation_info.goal_list else ""
+                    is_suitable, check_reason, need_replan = await self.reply_generator.check_reply(
+                        reply=self.generated_reply,
+                        goal=current_goal_str,
+                        chat_history=observation_info.chat_history,
+                        chat_history_str=observation_info.chat_history_str,
+                        retry_count=reply_attempt_count - 1,
+                    )
+                    logger.info(
+                        f"[私聊][{self.private_name}]第 {reply_attempt_count} 次首次回复检查结果: 合适={is_suitable}, 原因='{check_reason}', 需重新规划={need_replan}"
+                    )
+                    if is_suitable:
+                        final_reply_to_send = self.generated_reply
+                        break
+                    elif need_replan:
+                        logger.warning(
+                            f"[私聊][{self.private_name}]第 {reply_attempt_count} 次首次回复检查建议重新规划，停止尝试。原因: {check_reason}"
+                        )
+                        break
+                except Exception as check_err:
+                    logger.error(
+                        f"[私聊][{self.private_name}]第 {reply_attempt_count} 次调用 ReplyChecker (首次回复) 时出错: {check_err}"
+                    )
+                    check_reason = f"第 {reply_attempt_count} 次检查过程出错: {check_err}"
+                    break
+
+            # 循环结束，处理最终结果
+            if is_suitable:
+                # 检查是否有新消息
+                if self._check_new_messages_after_planning():
+                    logger.info(f"[私聊][{self.private_name}]生成首次回复期间收到新消息，取消发送，重新规划行动")
+                    conversation_info.done_action[action_index].update(
+                        {"status": "recall", "final_reason": f"有新消息，取消发送首次回复: {final_reply_to_send}"}
+                    )
+                    return  # 直接返回，重新规划
+
+                # 发送合适的回复
+                self.generated_reply = final_reply_to_send
+                # --- 在这里调用 _send_reply ---
+                await self._send_reply()  # <--- 调用恢复后的函数
+
+                # 更新状态: 标记上次成功是 direct_reply
+                self.conversation_info.last_successful_reply_action = "direct_reply"
+                action_successful = True  # 标记动作成功
+
+            elif need_replan:
+                # 打回动作决策
+                logger.warning(
+                    f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试，首次回复决定打回动作决策。打回原因: {check_reason}"
+                )
+                conversation_info.done_action[action_index].update(
+                    {"status": "recall", "final_reason": f"首次回复尝试{reply_attempt_count}次后打回: {check_reason}"}
+                )
+
+            else:
+                # 首次回复失败
+                logger.warning(
+                    f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试，未能生成合适的首次回复。最终原因: {check_reason}"
+                )
+                conversation_info.done_action[action_index].update(
+                    {"status": "recall", "final_reason": f"首次回复尝试{reply_attempt_count}次后失败: {check_reason}"}
+                )
+                # 重置状态: 首次回复失败，下次还是用初始 prompt
+                self.conversation_info.last_successful_reply_action = None
+
+                # 执行 Wait 操作 (保持原有逻辑)
+                logger.info(f"[私聊][{self.private_name}]由于无法生成合适首次回复，执行 'wait' 操作...")
+                self.state = ConversationState.WAITING
+                await self.waiter.wait(self.conversation_info)
+                wait_action_record = {
+                    "action": "wait",
+                    "plan_reason": "因 direct_reply 多次尝试失败而执行的后备等待",
+                    "status": "done",
+                    "time": datetime.datetime.now().strftime("%H:%M:%S"),
+                    "final_reason": None,
+                }
+                conversation_info.done_action.append(wait_action_record)
+
+        elif action == "fetch_knowledge":
+            self.state = ConversationState.FETCHING
+            knowledge_query = reason
+            try:
+                # 检查 knowledge_fetcher 是否存在
+                if not hasattr(self, "knowledge_fetcher"):
+                    logger.error(f"[私聊][{self.private_name}]KnowledgeFetcher 未初始化，无法获取知识。")
+                    raise AttributeError("KnowledgeFetcher not initialized")
+
+                knowledge, source = await self.knowledge_fetcher.fetch(knowledge_query, observation_info.chat_history)
+                logger.info(f"[私聊][{self.private_name}]获取到知识: {knowledge[:100]}..., 来源: {source}")
+                if knowledge:
+                    # 确保 knowledge_list 存在
+                    if not hasattr(conversation_info, "knowledge_list"):
+                        conversation_info.knowledge_list = []
+                    conversation_info.knowledge_list.append(
+                        {"query": knowledge_query, "knowledge": knowledge, "source": source}
+                    )
+                action_successful = True
+            except Exception as fetch_err:
+                logger.error(f"[私聊][{self.private_name}]获取知识时出错: {str(fetch_err)}")
+                conversation_info.done_action[action_index].update(
+                    {"status": "recall", "final_reason": f"获取知识失败: {str(fetch_err)}"}
+                )
+                self.conversation_info.last_successful_reply_action = None  # 重置状态
+
+        elif action == "rethink_goal":
+            self.state = ConversationState.RETHINKING
+            try:
+                # 检查 goal_analyzer 是否存在
+                if not hasattr(self, "goal_analyzer"):
+                    logger.error(f"[私聊][{self.private_name}]GoalAnalyzer 未初始化，无法重新思考目标。")
+                    raise AttributeError("GoalAnalyzer not initialized")
+                await self.goal_analyzer.analyze_goal(conversation_info, observation_info)
+                action_successful = True
+            except Exception as rethink_err:
+                logger.error(f"[私聊][{self.private_name}]重新思考目标时出错: {rethink_err}")
+                conversation_info.done_action[action_index].update(
+                    {"status": "recall", "final_reason": f"重新思考目标失败: {rethink_err}"}
+                )
+                self.conversation_info.last_successful_reply_action = None  # 重置状态
+
+        elif action == "listening":
+            self.state = ConversationState.LISTENING
+            logger.info(f"[私聊][{self.private_name}]倾听对方发言...")
+            try:
+                # 检查 waiter 是否存在
+                if not hasattr(self, "waiter"):
+                    logger.error(f"[私聊][{self.private_name}]Waiter 未初始化，无法倾听。")
+                    raise AttributeError("Waiter not initialized")
+                await self.waiter.wait_listening(conversation_info)
+                action_successful = True  # Listening 完成就算成功
+            except Exception as listen_err:
+                logger.error(f"[私聊][{self.private_name}]倾听时出错: {listen_err}")
+                conversation_info.done_action[action_index].update(
+                    {"status": "recall", "final_reason": f"倾听失败: {listen_err}"}
+                )
+                self.conversation_info.last_successful_reply_action = None  # 重置状态
+
+        elif action == "say_goodbye":
+            self.state = ConversationState.GENERATING  # 也可以定义一个新的状态，如 ENDING
+            logger.info(f"[私聊][{self.private_name}]执行行动: 生成并发送告别语...")
+            try:
+                # 1. 生成告别语 (使用 'say_goodbye' action_type)
+                self.generated_reply = await self.reply_generator.generate(
+                    observation_info, conversation_info, action_type="say_goodbye"
+                )
+                logger.info(f"[私聊][{self.private_name}]生成的告别语: {self.generated_reply}")
+
+                # 2. 直接发送告别语 (不经过检查)
+                if self.generated_reply:  # 确保生成了内容
+                    await self._send_reply()  # 调用发送方法
+                    # 发送成功后，标记动作成功
+                    action_successful = True
+                    logger.info(f"[私聊][{self.private_name}]告别语已发送。")
+                else:
+                    logger.warning(f"[私聊][{self.private_name}]未能生成告别语内容，无法发送。")
+                    action_successful = False  # 标记动作失败
+                    conversation_info.done_action[action_index].update(
+                        {"status": "recall", "final_reason": "未能生成告别语内容"}
+                    )
+
+                # 3. 无论是否发送成功，都准备结束对话
+                self.should_continue = False
+                logger.info(f"[私聊][{self.private_name}]发送告别语流程结束，即将停止对话实例。")
+
+            except Exception as goodbye_err:
+                logger.error(f"[私聊][{self.private_name}]生成或发送告别语时出错: {goodbye_err}")
+                logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
+                # 即使出错，也结束对话
+                self.should_continue = False
+                action_successful = False  # 标记动作失败
+                conversation_info.done_action[action_index].update(
+                    {"status": "recall", "final_reason": f"生成或发送告别语时出错: {goodbye_err}"}
+                )
+
+        elif action == "end_conversation":
+            # 这个分支现在只会在 action_planner 最终决定不告别时被调用
+            self.should_continue = False
+            logger.info(f"[私聊][{self.private_name}]收到最终结束指令，停止对话...")
+            action_successful = True  # 标记这个指令本身是成功的
+
+        elif action == "block_and_ignore":
+            logger.info(f"[私聊][{self.private_name}]不想再理你了...")
+            ignore_duration_seconds = 10 * 60
+            self.ignore_until_timestamp = time.time() + ignore_duration_seconds
+            logger.info(
+                f"[私聊][{self.private_name}]将忽略此对话直到: {datetime.datetime.fromtimestamp(self.ignore_until_timestamp)}"
+            )
+            self.state = ConversationState.IGNORED
+            action_successful = True  # 标记动作成功
+
+        else:  # 对应 'wait' 动作
+            self.state = ConversationState.WAITING
+            logger.info(f"[私聊][{self.private_name}]等待更多信息...")
+            try:
+                # 检查 waiter 是否存在
+                if not hasattr(self, "waiter"):
+                    logger.error(f"[私聊][{self.private_name}]Waiter 未初始化，无法等待。")
+                    raise AttributeError("Waiter not initialized")
+                _timeout_occurred = await self.waiter.wait(self.conversation_info)
+                action_successful = True  # Wait 完成就算成功
+            except Exception as wait_err:
+                logger.error(f"[私聊][{self.private_name}]等待时出错: {wait_err}")
+                conversation_info.done_action[action_index].update(
+                    {"status": "recall", "final_reason": f"等待失败: {wait_err}"}
+                )
+                self.conversation_info.last_successful_reply_action = None  # 重置状态
+
+        # --- 更新 Action History 状态 ---
+        # 只有当动作本身成功时，才更新状态为 done
+        if action_successful:
+            conversation_info.done_action[action_index].update(
+                {
+                    "status": "done",
+                    "time": datetime.datetime.now().strftime("%H:%M:%S"),
+                }
+            )
+            # 重置状态: 对于非回复类动作的成功，清除上次回复状态
+            if action not in ["direct_reply", "send_new_message"]:
+                self.conversation_info.last_successful_reply_action = None
+                logger.debug(f"[私聊][{self.private_name}]动作 {action} 成功完成，重置 last_successful_reply_action")
+        # 如果动作是 recall 状态，在各自的处理逻辑中已经更新了 done_action
+
+    async def _send_reply(self):
+        """发送回复"""
+        if not self.generated_reply:
+            logger.warning(f"[私聊][{self.private_name}]没有生成回复内容，无法发送。")
+            return
+
+        try:
+            _current_time = time.time()
+            reply_content = self.generated_reply
+
+            # 发送消息 (确保 direct_sender 和 chat_stream 有效)
+            if not hasattr(self, "direct_sender") or not self.direct_sender:
+                logger.error(f"[私聊][{self.private_name}]DirectMessageSender 未初始化，无法发送回复。")
+                return
+            if not self.chat_stream:
+                logger.error(f"[私聊][{self.private_name}]ChatStream 未初始化，无法发送回复。")
+                return
+
+            await self.direct_sender.send_message(chat_stream=self.chat_stream, content=reply_content)
+
+            # 发送成功后，手动触发 observer 更新可能导致重复处理自己发送的消息
+            # 更好的做法是依赖 observer 的自动轮询或数据库触发器（如果支持）
+            # 暂时注释掉，观察是否影响 ObservationInfo 的更新
+            # self.chat_observer.trigger_update()
+            # if not await self.chat_observer.wait_for_update():
+            #     logger.warning(f"[私聊][{self.private_name}]等待 ChatObserver 更新完成超时")
+
+            self.state = ConversationState.ANALYZING  # 更新状态
+
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]发送消息或更新状态时失败: {str(e)}")
+            logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
+            self.state = ConversationState.ANALYZING
+
+    async def _send_timeout_message(self):
+        """发送超时结束消息"""
+        try:
+            messages = self.chat_observer.get_cached_messages(limit=1)
+            if not messages:
+                return
+
+            latest_message = self._convert_to_message(messages[0])
+            await self.direct_sender.send_message(
+                chat_stream=self.chat_stream, content="TODO:超时消息", reply_to_message=latest_message
+            )
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]发送超时消息失败: {str(e)}")

src/chat/brain_chat/PFC/conversation_info.py

@@ -0,0 +1,10 @@
+from typing import Optional
+
+
+class ConversationInfo:
+    def __init__(self):
+        self.done_action = []
+        self.goal_list = []
+        self.knowledge_list = []
+        self.memory_list = []
+        self.last_successful_reply_action: Optional[str] = None

src/chat/brain_chat/PFC/message_sender.py

@@ -0,0 +1,81 @@
+import time
+from typing import Optional
+from src.common.logger import get_module_logger
+from ..chat.chat_stream import ChatStream
+from ..chat.message import Message
+from maim_message import UserInfo, Seg
+from src.plugins.chat.message import MessageSending, MessageSet
+from src.plugins.chat.message_sender import message_manager
+from ..storage.storage import MessageStorage
+from ...config.config import global_config
+from rich.traceback import install
+
+install(extra_lines=3)
+
+
+logger = get_module_logger("message_sender")
+
+
+class DirectMessageSender:
+    """直接消息发送器"""
+
+    def __init__(self, private_name: str):
+        self.private_name = private_name
+        self.storage = MessageStorage()
+
+    async def send_message(
+        self,
+        chat_stream: ChatStream,
+        content: str,
+        reply_to_message: Optional[Message] = None,
+    ) -> None:
+        """发送消息到聊天流
+
+        Args:
+            chat_stream: 聊天流
+            content: 消息内容
+            reply_to_message: 要回复的消息（可选）
+        """
+        try:
+            # 创建消息内容
+            segments = Seg(type="seglist", data=[Seg(type="text", data=content)])
+
+            # 获取麦麦的信息
+            bot_user_info = UserInfo(
+                user_id=global_config.BOT_QQ,
+                user_nickname=global_config.BOT_NICKNAME,
+                platform=chat_stream.platform,
+            )
+
+            # 用当前时间作为message_id，和之前那套sender一样
+            message_id = f"dm{round(time.time(), 2)}"
+
+            # 构建消息对象
+            message = MessageSending(
+                message_id=message_id,
+                chat_stream=chat_stream,
+                bot_user_info=bot_user_info,
+                sender_info=reply_to_message.message_info.user_info if reply_to_message else None,
+                message_segment=segments,
+                reply=reply_to_message,
+                is_head=True,
+                is_emoji=False,
+                thinking_start_time=time.time(),
+            )
+
+            # 处理消息
+            await message.process()
+
+            # 不知道有什么用，先留下来了，和之前那套sender一样
+            _message_json = message.to_dict()
+
+            # 发送消息
+            message_set = MessageSet(chat_stream, message_id)
+            message_set.add_message(message)
+            await message_manager.add_message(message_set)
+            await self.storage.store_message(message, chat_stream)
+            logger.info(f"[私聊][{self.private_name}]PFC消息已发送: {content}")
+
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {str(e)}")
+            raise

src/chat/brain_chat/PFC/message_storage.py

@@ -0,0 +1,119 @@
+from abc import ABC, abstractmethod
+from typing import List, Dict, Any
+from src.common.database import db
+
+
+class MessageStorage(ABC):
+    """消息存储接口"""
+
+    @abstractmethod
+    async def get_messages_after(self, chat_id: str, message: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """获取指定消息ID之后的所有消息
+
+        Args:
+            chat_id: 聊天ID
+            message: 消息
+
+        Returns:
+            List[Dict[str, Any]]: 消息列表
+        """
+        pass
+
+    @abstractmethod
+    async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]:
+        """获取指定时间点之前的消息
+
+        Args:
+            chat_id: 聊天ID
+            time_point: 时间戳
+            limit: 最大消息数量
+
+        Returns:
+            List[Dict[str, Any]]: 消息列表
+        """
+        pass
+
+    @abstractmethod
+    async def has_new_messages(self, chat_id: str, after_time: float) -> bool:
+        """检查是否有新消息
+
+        Args:
+            chat_id: 聊天ID
+            after_time: 时间戳
+
+        Returns:
+            bool: 是否有新消息
+        """
+        pass
+
+
+class MongoDBMessageStorage(MessageStorage):
+    """MongoDB消息存储实现"""
+
+    async def get_messages_after(self, chat_id: str, message_time: float) -> List[Dict[str, Any]]:
+        query = {"chat_id": chat_id, "time": {"$gt": message_time}}
+        # print(f"storage_check_message: {message_time}")
+
+        return list(db.messages.find(query).sort("time", 1))
+
+    async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]:
+        query = {"chat_id": chat_id, "time": {"$lt": time_point}}
+
+        messages = list(db.messages.find(query).sort("time", -1).limit(limit))
+
+        # 将消息按时间正序排列
+        messages.reverse()
+        return messages
+
+    async def has_new_messages(self, chat_id: str, after_time: float) -> bool:
+        query = {"chat_id": chat_id, "time": {"$gt": after_time}}
+
+        return db.messages.find_one(query) is not None
+
+
+# # 创建一个内存消息存储实现，用于测试
+# class InMemoryMessageStorage(MessageStorage):
+#     """内存消息存储实现，主要用于测试"""
+
+#     def __init__(self):
+#         self.messages: Dict[str, List[Dict[str, Any]]] = {}
+
+#     async def get_messages_after(self, chat_id: str, message_id: Optional[str] = None) -> List[Dict[str, Any]]:
+#         if chat_id not in self.messages:
+#             return []
+
+#         messages = self.messages[chat_id]
+#         if not message_id:
+#             return messages
+
+#         # 找到message_id的索引
+#         try:
+#             index = next(i for i, m in enumerate(messages) if m["message_id"] == message_id)
+#             return messages[index + 1:]
+#         except StopIteration:
+#             return []
+
+#     async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]:
+#         if chat_id not in self.messages:
+#             return []
+
+#         messages = [
+#             m for m in self.messages[chat_id]
+#             if m["time"] < time_point
+#         ]
+
+#         return messages[-limit:]
+
+#     async def has_new_messages(self, chat_id: str, after_time: float) -> bool:
+#         if chat_id not in self.messages:
+#             return False
+
+#         return any(m["time"] > after_time for m in self.messages[chat_id])
+
+#     # 测试辅助方法
+#     def add_message(self, chat_id: str, message: Dict[str, Any]):
+#         """添加测试消息"""
+#         if chat_id not in self.messages:
+#             self.messages[chat_id] = []
+#         self.messages[chat_id].append(message)
+#         self.messages[chat_id].sort(key=lambda m: m["time"])

src/chat/brain_chat/PFC/observation_info.py

@@ -0,0 +1,389 @@
+from typing import List, Optional, Dict, Any, Set
+from maim_message import UserInfo
+import time
+from src.common.logger import get_module_logger
+from .chat_observer import ChatObserver
+from .chat_states import NotificationHandler, NotificationType, Notification
+from src.plugins.utils.chat_message_builder import build_readable_messages
+import traceback  # 导入 traceback 用于调试
+
+logger = get_module_logger("observation_info")
+
+
+class ObservationInfoHandler(NotificationHandler):
+    """ObservationInfo的通知处理器"""
+
+    def __init__(self, observation_info: "ObservationInfo", private_name: str):
+        """初始化处理器
+
+        Args:
+            observation_info: 要更新的ObservationInfo实例
+            private_name: 私聊对象的名称，用于日志记录
+        """
+        self.observation_info = observation_info
+        # 将 private_name 存储在 handler 实例中
+        self.private_name = private_name
+
+    async def handle_notification(self, notification: Notification):  # 添加类型提示
+        # 获取通知类型和数据
+        notification_type = notification.type
+        data = notification.data
+
+        try:  # 添加错误处理块
+            if notification_type == NotificationType.NEW_MESSAGE:
+                # 处理新消息通知
+                # logger.debug(f"[私聊][{self.private_name}]收到新消息通知data: {data}") # 可以在需要时取消注释
+                message_id = data.get("message_id")
+                processed_plain_text = data.get("processed_plain_text")
+                detailed_plain_text = data.get("detailed_plain_text")
+                user_info_dict = data.get("user_info")  # 先获取字典
+                time_value = data.get("time")
+
+                # 确保 user_info 是字典类型再创建 UserInfo 对象
+                user_info = None
+                if isinstance(user_info_dict, dict):
+                    try:
+                        user_info = UserInfo.from_dict(user_info_dict)
+                    except Exception as e:
+                        logger.error(
+                            f"[私聊][{self.private_name}]从字典创建 UserInfo 时出错: {e}, 字典内容: {user_info_dict}"
+                        )
+                        # 可以选择在这里返回或记录错误，避免后续代码出错
+                        return
+                elif user_info_dict is not None:
+                    logger.warning(
+                        f"[私聊][{self.private_name}]收到的 user_info 不是预期的字典类型: {type(user_info_dict)}"
+                    )
+                    # 根据需要处理非字典情况，这里暂时返回
+                    return
+
+                message = {
+                    "message_id": message_id,
+                    "processed_plain_text": processed_plain_text,
+                    "detailed_plain_text": detailed_plain_text,
+                    "user_info": user_info_dict,  # 存储原始字典或 UserInfo 对象，取决于你的 update_from_message 如何处理
+                    "time": time_value,
+                }
+                # 传递 UserInfo 对象（如果成功创建）或原始字典
+                await self.observation_info.update_from_message(message, user_info)  # 修改：传递 user_info 对象
+
+            elif notification_type == NotificationType.COLD_CHAT:
+                # 处理冷场通知
+                is_cold = data.get("is_cold", False)
+                await self.observation_info.update_cold_chat_status(is_cold, time.time())  # 修改：改为 await 调用
+
+            elif notification_type == NotificationType.ACTIVE_CHAT:
+                # 处理活跃通知 (通常由 COLD_CHAT 的反向状态处理)
+                is_active = data.get("is_active", False)
+                self.observation_info.is_cold = not is_active
+
+            elif notification_type == NotificationType.BOT_SPEAKING:
+                # 处理机器人说话通知 (按需实现)
+                self.observation_info.is_typing = False
+                self.observation_info.last_bot_speak_time = time.time()
+
+            elif notification_type == NotificationType.USER_SPEAKING:
+                # 处理用户说话通知
+                self.observation_info.is_typing = False
+                self.observation_info.last_user_speak_time = time.time()
+
+            elif notification_type == NotificationType.MESSAGE_DELETED:
+                # 处理消息删除通知
+                message_id = data.get("message_id")
+                # 从 unprocessed_messages 中移除被删除的消息
+                original_count = len(self.observation_info.unprocessed_messages)
+                self.observation_info.unprocessed_messages = [
+                    msg for msg in self.observation_info.unprocessed_messages if msg.get("message_id") != message_id
+                ]
+                if len(self.observation_info.unprocessed_messages) < original_count:
+                    logger.info(f"[私聊][{self.private_name}]移除了未处理的消息 (ID: {message_id})")
+
+            elif notification_type == NotificationType.USER_JOINED:
+                # 处理用户加入通知 (如果适用私聊场景)
+                user_id = data.get("user_id")
+                if user_id:
+                    self.observation_info.active_users.add(str(user_id))  # 确保是字符串
+
+            elif notification_type == NotificationType.USER_LEFT:
+                # 处理用户离开通知 (如果适用私聊场景)
+                user_id = data.get("user_id")
+                if user_id:
+                    self.observation_info.active_users.discard(str(user_id))  # 确保是字符串
+
+            elif notification_type == NotificationType.ERROR:
+                # 处理错误通知
+                error_msg = data.get("error", "未提供错误信息")
+                logger.error(f"[私聊][{self.private_name}]收到错误通知: {error_msg}")
+
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]处理通知时发生错误: {e}")
+            logger.error(traceback.format_exc())  # 打印详细堆栈信息
+
+
+# @dataclass <-- 这个，不需要了（递黄瓜）
+class ObservationInfo:
+    """决策信息类，用于收集和管理来自chat_observer的通知信息 (手动实现 __init__)"""
+
+    # 类型提示保留，可用于文档和静态分析
+    private_name: str
+    chat_history: List[Dict[str, Any]]
+    chat_history_str: str
+    unprocessed_messages: List[Dict[str, Any]]
+    active_users: Set[str]
+    last_bot_speak_time: Optional[float]
+    last_user_speak_time: Optional[float]
+    last_message_time: Optional[float]
+    last_message_id: Optional[str]
+    last_message_content: str
+    last_message_sender: Optional[str]
+    bot_id: Optional[str]
+    chat_history_count: int
+    new_messages_count: int
+    cold_chat_start_time: Optional[float]
+    cold_chat_duration: float
+    is_typing: bool
+    is_cold_chat: bool
+    changed: bool
+    chat_observer: Optional[ChatObserver]
+    handler: Optional[ObservationInfoHandler]
+
+    def __init__(self, private_name: str):
+        """
+        手动初始化 ObservationInfo 的所有实例变量。
+        """
+
+        # 接收的参数
+        self.private_name: str = private_name
+
+        # data_list
+        self.chat_history: List[Dict[str, Any]] = []
+        self.chat_history_str: str = ""
+        self.unprocessed_messages: List[Dict[str, Any]] = []
+        self.active_users: Set[str] = set()
+
+        # data
+        self.last_bot_speak_time: Optional[float] = None
+        self.last_user_speak_time: Optional[float] = None
+        self.last_message_time: Optional[float] = None
+        self.last_message_id: Optional[str] = None
+        self.last_message_content: str = ""
+        self.last_message_sender: Optional[str] = None
+        self.bot_id: Optional[str] = None
+        self.chat_history_count: int = 0
+        self.new_messages_count: int = 0
+        self.cold_chat_start_time: Optional[float] = None
+        self.cold_chat_duration: float = 0.0
+
+        # state
+        self.is_typing: bool = False
+        self.is_cold_chat: bool = False
+        self.changed: bool = False
+
+        # 关联对象
+        self.chat_observer: Optional[ChatObserver] = None
+
+        self.handler: ObservationInfoHandler = ObservationInfoHandler(self, self.private_name)
+
+    def bind_to_chat_observer(self, chat_observer: ChatObserver):
+        """绑定到指定的chat_observer
+
+        Args:
+            chat_observer: 要绑定的 ChatObserver 实例
+        """
+        if self.chat_observer:
+            logger.warning(f"[私聊][{self.private_name}]尝试重复绑定 ChatObserver")
+            return
+
+        self.chat_observer = chat_observer
+        try:
+            if not self.handler:  # 确保 handler 已经被创建
+                logger.error(f"[私聊][{self.private_name}] 尝试绑定时 handler 未初始化！")
+                self.chat_observer = None  # 重置，防止后续错误
+                return
+
+            # 注册关心的通知类型
+            self.chat_observer.notification_manager.register_handler(
+                target="observation_info", notification_type=NotificationType.NEW_MESSAGE, handler=self.handler
+            )
+            self.chat_observer.notification_manager.register_handler(
+                target="observation_info", notification_type=NotificationType.COLD_CHAT, handler=self.handler
+            )
+            # 可以根据需要注册更多通知类型
+            # self.chat_observer.notification_manager.register_handler(
+            #     target="observation_info", notification_type=NotificationType.MESSAGE_DELETED, handler=self.handler
+            # )
+            logger.info(f"[私聊][{self.private_name}]成功绑定到 ChatObserver")
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]绑定到 ChatObserver 时出错: {e}")
+            self.chat_observer = None  # 绑定失败，重置
+
+    def unbind_from_chat_observer(self):
+        """解除与chat_observer的绑定"""
+        if (
+            self.chat_observer and hasattr(self.chat_observer, "notification_manager") and self.handler
+        ):  # 增加 handler 检查
+            try:
+                self.chat_observer.notification_manager.unregister_handler(
+                    target="observation_info", notification_type=NotificationType.NEW_MESSAGE, handler=self.handler
+                )
+                self.chat_observer.notification_manager.unregister_handler(
+                    target="observation_info", notification_type=NotificationType.COLD_CHAT, handler=self.handler
+                )
+                # 如果注册了其他类型，也要在这里注销
+                # self.chat_observer.notification_manager.unregister_handler(
+                #     target="observation_info", notification_type=NotificationType.MESSAGE_DELETED, handler=self.handler
+                # )
+                logger.info(f"[私聊][{self.private_name}]成功从 ChatObserver 解绑")
+            except Exception as e:
+                logger.error(f"[私聊][{self.private_name}]从 ChatObserver 解绑时出错: {e}")
+            finally:  # 确保 chat_observer 被重置
+                self.chat_observer = None
+        else:
+            logger.warning(f"[私聊][{self.private_name}]尝试解绑时 ChatObserver 不存在、无效或 handler 未设置")
+
+    # 修改：update_from_message 接收 UserInfo 对象
+    async def update_from_message(self, message: Dict[str, Any], user_info: Optional[UserInfo]):
+        """从消息更新信息
+
+        Args:
+            message: 消息数据字典
+            user_info: 解析后的 UserInfo 对象 (可能为 None)
+        """
+        message_time = message.get("time")
+        message_id = message.get("message_id")
+        processed_text = message.get("processed_plain_text", "")
+
+        # 只有在新消息到达时才更新 last_message 相关信息
+        if message_time and message_time > (self.last_message_time or 0):
+            self.last_message_time = message_time
+            self.last_message_id = message_id
+            self.last_message_content = processed_text
+            # 重置冷场计时器
+            self.is_cold_chat = False
+            self.cold_chat_start_time = None
+            self.cold_chat_duration = 0.0
+
+            if user_info:
+                sender_id = str(user_info.user_id)  # 确保是字符串
+                self.last_message_sender = sender_id
+                # 更新发言时间
+                if sender_id == self.bot_id:
+                    self.last_bot_speak_time = message_time
+                else:
+                    self.last_user_speak_time = message_time
+                    self.active_users.add(sender_id)  # 用户发言则认为其活跃
+            else:
+                logger.warning(
+                    f"[私聊][{self.private_name}]处理消息更新时缺少有效的 UserInfo 对象, message_id: {message_id}"
+                )
+                self.last_message_sender = None  # 发送者未知
+
+            # 将原始消息字典添加到未处理列表
+            self.unprocessed_messages.append(message)
+            self.new_messages_count = len(self.unprocessed_messages)  # 直接用列表长度
+
+            # logger.debug(f"[私聊][{self.private_name}]消息更新: last_time={self.last_message_time}, new_count={self.new_messages_count}")
+            self.update_changed()  # 标记状态已改变
+        else:
+            # 如果消息时间戳不是最新的，可能不需要处理，或者记录一个警告
+            pass
+            # logger.warning(f"[私聊][{self.private_name}]收到过时或无效时间戳的消息: ID={message_id}, time={message_time}")
+
+    def update_changed(self):
+        """标记状态已改变，并重置标记"""
+        # logger.debug(f"[私聊][{self.private_name}]状态标记为已改变 (changed=True)")
+        self.changed = True
+
+    async def update_cold_chat_status(self, is_cold: bool, current_time: float):
+        """更新冷场状态
+
+        Args:
+            is_cold: 是否处于冷场状态
+            current_time: 当前时间戳
+        """
+        if is_cold != self.is_cold_chat:  # 仅在状态变化时更新
+            self.is_cold_chat = is_cold
+            if is_cold:
+                # 进入冷场状态
+                self.cold_chat_start_time = (
+                    self.last_message_time or current_time
+                )  # 从最后消息时间开始算，或从当前时间开始
+                logger.info(f"[私聊][{self.private_name}]进入冷场状态，开始时间: {self.cold_chat_start_time}")
+            else:
+                # 结束冷场状态
+                if self.cold_chat_start_time:
+                    self.cold_chat_duration = current_time - self.cold_chat_start_time
+                    logger.info(f"[私聊][{self.private_name}]结束冷场状态，持续时间: {self.cold_chat_duration:.2f} 秒")
+                self.cold_chat_start_time = None  # 重置开始时间
+            self.update_changed()  # 状态变化，标记改变
+
+        # 即使状态没变，如果是冷场状态，也更新持续时间
+        if self.is_cold_chat and self.cold_chat_start_time:
+            self.cold_chat_duration = current_time - self.cold_chat_start_time
+
+    def get_active_duration(self) -> float:
+        """获取当前活跃时长 (距离最后一条消息的时间)
+
+        Returns:
+            float: 最后一条消息到现在的时长（秒）
+        """
+        if not self.last_message_time:
+            return 0.0
+        return time.time() - self.last_message_time
+
+    def get_user_response_time(self) -> Optional[float]:
+        """获取用户最后响应时间 (距离用户最后发言的时间)
+
+        Returns:
+            Optional[float]: 用户最后发言到现在的时长（秒），如果没有用户发言则返回None
+        """
+        if not self.last_user_speak_time:
+            return None
+        return time.time() - self.last_user_speak_time
+
+    def get_bot_response_time(self) -> Optional[float]:
+        """获取机器人最后响应时间 (距离机器人最后发言的时间)
+
+        Returns:
+            Optional[float]: 机器人最后发言到现在的时长（秒），如果没有机器人发言则返回None
+        """
+        if not self.last_bot_speak_time:
+            return None
+        return time.time() - self.last_bot_speak_time
+
+    async def clear_unprocessed_messages(self):
+        """将未处理消息移入历史记录，并更新相关状态"""
+        if not self.unprocessed_messages:
+            return  # 没有未处理消息，直接返回
+
+        # logger.debug(f"[私聊][{self.private_name}]处理 {len(self.unprocessed_messages)} 条未处理消息...")
+        # 将未处理消息添加到历史记录中 (确保历史记录有长度限制，避免无限增长)
+        max_history_len = 100  # 示例：最多保留100条历史记录
+        self.chat_history.extend(self.unprocessed_messages)
+        if len(self.chat_history) > max_history_len:
+            self.chat_history = self.chat_history[-max_history_len:]
+
+        # 更新历史记录字符串 (只使用最近一部分生成，例如20条)
+        history_slice_for_str = self.chat_history[-20:]
+        try:
+            self.chat_history_str = await build_readable_messages(
+                history_slice_for_str,
+                replace_bot_name=True,
+                merge_messages=False,
+                timestamp_mode="relative",
+                read_mark=0.0,  # read_mark 可能需要根据逻辑调整
+            )
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]构建聊天记录字符串时出错: {e}")
+            self.chat_history_str = "[构建聊天记录出错]"  # 提供错误提示
+
+        # 清空未处理消息列表和计数
+        # cleared_count = len(self.unprocessed_messages)
+        self.unprocessed_messages.clear()
+        self.new_messages_count = 0
+        # self.has_unread_messages = False # 这个状态可以通过 new_messages_count 判断
+
+        self.chat_history_count = len(self.chat_history)  # 更新历史记录总数
+        # logger.debug(f"[私聊][{self.private_name}]已处理 {cleared_count} 条消息，当前历史记录 {self.chat_history_count} 条。")
+
+        self.update_changed()  # 状态改变

src/chat/brain_chat/PFC/pfc.py

@@ -0,0 +1,345 @@
+from typing import List, Tuple, TYPE_CHECKING
+from src.common.logger import get_module_logger
+from ..models.utils_model import LLMRequest
+from ...config.config import global_config
+from .chat_observer import ChatObserver
+from .pfc_utils import get_items_from_json
+from src.individuality.individuality import Individuality
+from .conversation_info import ConversationInfo
+from .observation_info import ObservationInfo
+from src.plugins.utils.chat_message_builder import build_readable_messages
+from rich.traceback import install
+
+install(extra_lines=3)
+
+if TYPE_CHECKING:
+    pass
+
+logger = get_module_logger("pfc")
+
+
+def _calculate_similarity(goal1: str, goal2: str) -> float:
+    """简单计算两个目标之间的相似度
+
+    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
+
+    Args:
+        goal1: 第一个目标
+        goal2: 第二个目标
+
+    Returns:
+        float: 相似度得分 (0-1)
+    """
+    # 简单实现：检查重叠字数比例
+    words1 = set(goal1)
+    words2 = set(goal2)
+    overlap = len(words1.intersection(words2))
+    total = len(words1.union(words2))
+    return overlap / total if total > 0 else 0
+
+
+class GoalAnalyzer:
+    """对话目标分析器"""
+
+    def __init__(self, stream_id: str, private_name: str):
+        self.llm = LLMRequest(
+            model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal"
+        )
+
+        self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
+        self.name = global_config.BOT_NICKNAME
+        self.nick_name = global_config.BOT_ALIAS_NAMES
+        self.private_name = private_name
+        self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
+
+        # 多目标存储结构
+        self.goals = []  # 存储多个目标
+        self.max_goals = 3  # 同时保持的最大目标数量
+        self.current_goal_and_reason = None
+
+    async def analyze_goal(self, conversation_info: ConversationInfo, observation_info: ObservationInfo):
+        """分析对话历史并设定目标
+
+        Args:
+            conversation_info: 对话信息
+            observation_info: 观察信息
+
+        Returns:
+            Tuple[str, str, str]: (目标, 方法, 原因)
+        """
+        # 构建对话目标
+        goals_str = ""
+        if conversation_info.goal_list:
+            for goal_reason in conversation_info.goal_list:
+                if isinstance(goal_reason, dict):
+                    goal = goal_reason.get("goal", "目标内容缺失")
+                    reasoning = goal_reason.get("reasoning", "没有明确原因")
+                else:
+                    goal = str(goal_reason)
+                    reasoning = "没有明确原因"
+
+                goal_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
+                goals_str += goal_str
+        else:
+            goal = "目前没有明确对话目标"
+            reasoning = "目前没有明确对话目标，最好思考一个对话目标"
+            goals_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
+
+        # 获取聊天历史记录
+        chat_history_text = observation_info.chat_history_str
+
+        if observation_info.new_messages_count > 0:
+            new_messages_list = observation_info.unprocessed_messages
+            new_messages_str = await build_readable_messages(
+                new_messages_list,
+                replace_bot_name=True,
+                merge_messages=False,
+                timestamp_mode="relative",
+                read_mark=0.0,
+            )
+            chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}"
+
+            # await observation_info.clear_unprocessed_messages()
+
+        persona_text = f"你的名字是{self.name}，{self.personality_info}。"
+        # 构建action历史文本
+        action_history_list = conversation_info.done_action
+        action_history_text = "你之前做的事情是："
+        for action in action_history_list:
+            action_history_text += f"{action}\n"
+
+        prompt = f"""{persona_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。
+这些目标应该反映出对话的不同方面和意图。
+
+{action_history_text}
+当前对话目标：
+{goals_str}
+
+聊天记录：
+{chat_history_text}
+
+请分析当前对话并确定最适合的对话目标。你可以：
+1. 保持现有目标不变
+2. 修改现有目标
+3. 添加新目标
+4. 删除不再相关的目标
+5. 如果你想结束对话，请设置一个目标，目标goal为"结束对话"，原因reasoning为你希望结束对话
+
+请以JSON数组格式输出当前的所有对话目标，每个目标包含以下字段：
+1. goal: 对话目标（简短的一句话）
+2. reasoning: 对话原因，为什么设定这个目标（简要解释）
+
+输出格式示例：
+[
+{{
+    "goal": "回答用户关于Python编程的具体问题",
+    "reasoning": "用户提出了关于Python的技术问题，需要专业且准确的解答"
+}},
+{{
+    "goal": "回答用户关于python安装的具体问题",
+    "reasoning": "用户提出了关于Python的技术问题，需要专业且准确的解答"
+}}
+]"""
+
+        logger.debug(f"[私聊][{self.private_name}]发送到LLM的提示词: {prompt}")
+        try:
+            content, _ = await self.llm.generate_response_async(prompt)
+            logger.debug(f"[私聊][{self.private_name}]LLM原始返回内容: {content}")
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]分析对话目标时出错: {str(e)}")
+            content = ""
+
+        # 使用改进后的get_items_from_json函数处理JSON数组
+        success, result = get_items_from_json(
+            content,
+            self.private_name,
+            "goal",
+            "reasoning",
+            required_types={"goal": str, "reasoning": str},
+            allow_array=True,
+        )
+
+        if success:
+            # 判断结果是单个字典还是字典列表
+            if isinstance(result, list):
+                # 清空现有目标列表并添加新目标
+                conversation_info.goal_list = []
+                for item in result:
+                    conversation_info.goal_list.append(item)
+
+                # 返回第一个目标作为当前主要目标（如果有）
+                if result:
+                    first_goal = result[0]
+                    return first_goal.get("goal", ""), "", first_goal.get("reasoning", "")
+            else:
+                # 单个目标的情况
+                conversation_info.goal_list.append(result)
+                return goal, "", reasoning
+
+        # 如果解析失败，返回默认值
+        return "", "", ""
+
+    async def _update_goals(self, new_goal: str, method: str, reasoning: str):
+        """更新目标列表
+
+        Args:
+            new_goal: 新的目标
+            method: 实现目标的方法
+            reasoning: 目标的原因
+        """
+        # 检查新目标是否与现有目标相似
+        for i, (existing_goal, _, _) in enumerate(self.goals):
+            if _calculate_similarity(new_goal, existing_goal) > 0.7:  # 相似度阈值
+                # 更新现有目标
+                self.goals[i] = (new_goal, method, reasoning)
+                # 将此目标移到列表前面（最主要的位置）
+                self.goals.insert(0, self.goals.pop(i))
+                return
+
+        # 添加新目标到列表前面
+        self.goals.insert(0, (new_goal, method, reasoning))
+
+        # 限制目标数量
+        if len(self.goals) > self.max_goals:
+            self.goals.pop()  # 移除最老的目标
+
+    async def get_all_goals(self) -> List[Tuple[str, str, str]]:
+        """获取所有当前目标
+
+        Returns:
+            List[Tuple[str, str, str]]: 目标列表，每项为(目标, 方法, 原因)
+        """
+        return self.goals.copy()
+
+    async def get_alternative_goals(self) -> List[Tuple[str, str, str]]:
+        """获取除了当前主要目标外的其他备选目标
+
+        Returns:
+            List[Tuple[str, str, str]]: 备选目标列表
+        """
+        if len(self.goals) <= 1:
+            return []
+        return self.goals[1:].copy()
+
+    async def analyze_conversation(self, goal, reasoning):
+        messages = self.chat_observer.get_cached_messages()
+        chat_history_text = await build_readable_messages(
+            messages,
+            replace_bot_name=True,
+            merge_messages=False,
+            timestamp_mode="relative",
+            read_mark=0.0,
+        )
+
+        persona_text = f"你的名字是{self.name}，{self.personality_info}。"
+        # ===> Persona 文本构建结束 <===
+
+        # --- 修改 Prompt 字符串，使用 persona_text ---
+        prompt = f"""{persona_text}。现在你在参与一场QQ聊天，
+        当前对话目标：{goal}
+        产生该对话目标的原因：{reasoning}
+        
+        请分析以下聊天记录，并根据你的性格特征评估该目标是否已经达到，或者你是否希望停止该次对话。
+        聊天记录：
+        {chat_history_text}
+        请以JSON格式输出，包含以下字段：
+        1. goal_achieved: 对话目标是否已经达到（true/false）
+        2. stop_conversation: 是否希望停止该次对话（true/false）
+        3. reason: 为什么希望停止该次对话（简要解释）   
+
+输出格式示例：
+{{
+    "goal_achieved": true,
+    "stop_conversation": false,
+    "reason": "虽然目标已达成，但对话仍然有继续的价值"
+}}"""
+
+        try:
+            content, _ = await self.llm.generate_response_async(prompt)
+            logger.debug(f"[私聊][{self.private_name}]LLM原始返回内容: {content}")
+
+            # 尝试解析JSON
+            success, result = get_items_from_json(
+                content,
+                self.private_name,
+                "goal_achieved",
+                "stop_conversation",
+                "reason",
+                required_types={"goal_achieved": bool, "stop_conversation": bool, "reason": str},
+            )
+
+            if not success:
+                logger.error(f"[私聊][{self.private_name}]无法解析对话分析结果JSON")
+                return False, False, "解析结果失败"
+
+            goal_achieved = result["goal_achieved"]
+            stop_conversation = result["stop_conversation"]
+            reason = result["reason"]
+
+            return goal_achieved, stop_conversation, reason
+
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]分析对话状态时出错: {str(e)}")
+            return False, False, f"分析出错: {str(e)}"
+
+
+# 先注释掉，万一以后出问题了还能开回来（（（
+# class DirectMessageSender:
+#     """直接发送消息到平台的发送器"""
+
+#     def __init__(self, private_name: str):
+#         self.logger = get_module_logger("direct_sender")
+#         self.storage = MessageStorage()
+#         self.private_name = private_name
+
+#     async def send_via_ws(self, message: MessageSending) -> None:
+#         try:
+#             await global_api.send_message(message)
+#         except Exception as e:
+#             raise ValueError(f"未找到平台：{message.message_info.platform} 的url配置，请检查配置文件") from e
+
+#     async def send_message(
+#         self,
+#         chat_stream: ChatStream,
+#         content: str,
+#         reply_to_message: Optional[Message] = None,
+#     ) -> None:
+#         """直接发送消息到平台
+
+#         Args:
+#             chat_stream: 聊天流
+#             content: 消息内容
+#             reply_to_message: 要回复的消息
+#         """
+#         # 构建消息对象
+#         message_segment = Seg(type="text", data=content)
+#         bot_user_info = UserInfo(
+#             user_id=global_config.BOT_QQ,
+#             user_nickname=global_config.BOT_NICKNAME,
+#             platform=chat_stream.platform,
+#         )
+
+#         message = MessageSending(
+#             message_id=f"dm{round(time.time(), 2)}",
+#             chat_stream=chat_stream,
+#             bot_user_info=bot_user_info,
+#             sender_info=reply_to_message.message_info.user_info if reply_to_message else None,
+#             message_segment=message_segment,
+#             reply=reply_to_message,
+#             is_head=True,
+#             is_emoji=False,
+#             thinking_start_time=time.time(),
+#         )
+
+#         # 处理消息
+#         await message.process()
+
+#         _message_json = message.to_dict()
+
+#         # 发送消息
+#         try:
+#             await self.send_via_ws(message)
+#             await self.storage.store_message(message, chat_stream)
+#             logger.success(f"[私聊][{self.private_name}]PFC消息已发送: {content}")
+#         except Exception as e:
+#             logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {str(e)}")

src/chat/brain_chat/PFC/pfc_KnowledgeFetcher.py

@@ -0,0 +1,85 @@
+from typing import List, Tuple
+from src.common.logger import get_module_logger
+from src.plugins.memory_system.Hippocampus import HippocampusManager
+from ..models.utils_model import LLMRequest
+from ...config.config import global_config
+from ..chat.message import Message
+from ..knowledge.knowledge_lib import qa_manager
+from ..utils.chat_message_builder import build_readable_messages
+
+logger = get_module_logger("knowledge_fetcher")
+
+
+class KnowledgeFetcher:
+    """知识调取器"""
+
+    def __init__(self, private_name: str):
+        self.llm = LLMRequest(
+            model=global_config.llm_normal,
+            temperature=global_config.llm_normal["temp"],
+            max_tokens=1000,
+            request_type="knowledge_fetch",
+        )
+        self.private_name = private_name
+
+    def _lpmm_get_knowledge(self, query: str) -> str:
+        """获取相关知识
+
+        Args:
+            query: 查询内容
+
+        Returns:
+            str: 构造好的,带相关度的知识
+        """
+
+        logger.debug(f"[私聊][{self.private_name}]正在从LPMM知识库中获取知识")
+        try:
+            knowledge_info = qa_manager.get_knowledge(query)
+            logger.debug(f"[私聊][{self.private_name}]LPMM知识库查询结果: {knowledge_info:150}")
+            return knowledge_info
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]LPMM知识库搜索工具执行失败: {str(e)}")
+            return "未找到匹配的知识"
+
+    async def fetch(self, query: str, chat_history: List[Message]) -> Tuple[str, str]:
+        """获取相关知识
+
+        Args:
+            query: 查询内容
+            chat_history: 聊天历史
+
+        Returns:
+            Tuple[str, str]: (获取的知识, 知识来源)
+        """
+        # 构建查询上下文
+        chat_history_text = await build_readable_messages(
+            chat_history,
+            replace_bot_name=True,
+            merge_messages=False,
+            timestamp_mode="relative",
+            read_mark=0.0,
+        )
+
+        # 从记忆中获取相关知识
+        related_memory = await HippocampusManager.get_instance().get_memory_from_text(
+            text=f"{query}\n{chat_history_text}",
+            max_memory_num=3,
+            max_memory_length=2,
+            max_depth=3,
+            fast_retrieval=False,
+        )
+        knowledge_text = ""
+        sources_text = "无记忆匹配"  # 默认值
+        if related_memory:
+            sources = []
+            for memory in related_memory:
+                knowledge_text += memory[1] + "\n"
+                sources.append(f"记忆片段{memory[0]}")
+            knowledge_text = knowledge_text.strip()
+            sources_text = "，".join(sources)
+
+        knowledge_text += "\n现在有以下**知识**可供参考：\n "
+        knowledge_text += self._lpmm_get_knowledge(query)
+        knowledge_text += "\n请记住这些**知识**，并根据**知识**回答问题。\n"
+
+        return knowledge_text or "未找到相关知识", sources_text or "无记忆匹配"

src/chat/brain_chat/PFC/pfc_manager.py

@@ -0,0 +1,115 @@
+import time
+from typing import Dict, Optional
+from src.common.logger import get_module_logger
+from .conversation import Conversation
+import traceback
+
+logger = get_module_logger("pfc_manager")
+
+
+class PFCManager:
+    """PFC对话管理器，负责管理所有对话实例"""
+
+    # 单例模式
+    _instance = None
+
+    # 会话实例管理
+    _instances: Dict[str, Conversation] = {}
+    _initializing: Dict[str, bool] = {}
+
+    @classmethod
+    def get_instance(cls) -> "PFCManager":
+        """获取管理器单例
+
+        Returns:
+            PFCManager: 管理器实例
+        """
+        if cls._instance is None:
+            cls._instance = PFCManager()
+        return cls._instance
+
+    async def get_or_create_conversation(self, stream_id: str, private_name: str) -> Optional[Conversation]:
+        """获取或创建对话实例
+
+        Args:
+            stream_id: 聊天流ID
+            private_name: 私聊名称
+
+        Returns:
+            Optional[Conversation]: 对话实例，创建失败则返回None
+        """
+        # 检查是否已经有实例
+        if stream_id in self._initializing and self._initializing[stream_id]:
+            logger.debug(f"[私聊][{private_name}]会话实例正在初始化中: {stream_id}")
+            return None
+
+        if stream_id in self._instances and self._instances[stream_id].should_continue:
+            logger.debug(f"[私聊][{private_name}]使用现有会话实例: {stream_id}")
+            return self._instances[stream_id]
+        if stream_id in self._instances:
+            instance = self._instances[stream_id]
+            if (
+                hasattr(instance, "ignore_until_timestamp")
+                and instance.ignore_until_timestamp
+                and time.time() < instance.ignore_until_timestamp
+            ):
+                logger.debug(f"[私聊][{private_name}]会话实例当前处于忽略状态: {stream_id}")
+                # 返回 None 阻止交互。或者可以返回实例但标记它被忽略了喵？
+                # 还是返回 None 吧喵。
+                return None
+
+            # 检查 should_continue 状态
+            if instance.should_continue:
+                logger.debug(f"[私聊][{private_name}]使用现有会话实例: {stream_id}")
+                return instance
+        # else: 实例存在但不应继续
+        try:
+            # 创建新实例
+            logger.info(f"[私聊][{private_name}]创建新的对话实例: {stream_id}")
+            self._initializing[stream_id] = True
+            # 创建实例
+            conversation_instance = Conversation(stream_id, private_name)
+            self._instances[stream_id] = conversation_instance
+
+            # 启动实例初始化
+            await self._initialize_conversation(conversation_instance)
+        except Exception as e:
+            logger.error(f"[私聊][{private_name}]创建会话实例失败: {stream_id}, 错误: {e}")
+            return None
+
+        return conversation_instance
+
+    async def _initialize_conversation(self, conversation: Conversation):
+        """初始化会话实例
+
+        Args:
+            conversation: 要初始化的会话实例
+        """
+        stream_id = conversation.stream_id
+        private_name = conversation.private_name
+
+        try:
+            logger.info(f"[私聊][{private_name}]开始初始化会话实例: {stream_id}")
+            # 启动初始化流程
+            await conversation._initialize()
+
+            # 标记初始化完成
+            self._initializing[stream_id] = False
+
+            logger.info(f"[私聊][{private_name}]会话实例 {stream_id} 初始化完成")
+
+        except Exception as e:
+            logger.error(f"[私聊][{private_name}]管理器初始化会话实例失败: {stream_id}, 错误: {e}")
+            logger.error(f"[私聊][{private_name}]{traceback.format_exc()}")
+            # 清理失败的初始化
+
+    async def get_conversation(self, stream_id: str) -> Optional[Conversation]:
+        """获取已存在的会话实例
+
+        Args:
+            stream_id: 聊天流ID
+
+        Returns:
+            Optional[Conversation]: 会话实例，不存在则返回None
+        """
+        return self._instances.get(stream_id)

src/chat/brain_chat/PFC/pfc_types.py

@@ -0,0 +1,23 @@
+from enum import Enum
+from typing import Literal
+
+
+class ConversationState(Enum):
+    """对话状态"""
+
+    INIT = "初始化"
+    RETHINKING = "重新思考"
+    ANALYZING = "分析历史"
+    PLANNING = "规划目标"
+    GENERATING = "生成回复"
+    CHECKING = "检查回复"
+    SENDING = "发送消息"
+    FETCHING = "获取知识"
+    WAITING = "等待"
+    LISTENING = "倾听"
+    ENDED = "结束"
+    JUDGING = "判断"
+    IGNORED = "屏蔽"
+
+
+ActionType = Literal["direct_reply", "fetch_knowledge", "wait"]

src/chat/brain_chat/PFC/pfc_utils.py

@@ -0,0 +1,127 @@
+import json
+import re
+from typing import Dict, Any, Optional, Tuple, List, Union
+from src.common.logger import get_module_logger
+
+logger = get_module_logger("pfc_utils")
+
+
+def get_items_from_json(
+    content: str,
+    private_name: str,
+    *items: str,
+    default_values: Optional[Dict[str, Any]] = None,
+    required_types: Optional[Dict[str, type]] = None,
+    allow_array: bool = True,
+) -> Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]:
+    """从文本中提取JSON内容并获取指定字段
+
+    Args:
+        content: 包含JSON的文本
+        private_name: 私聊名称
+        *items: 要提取的字段名
+        default_values: 字段的默认值，格式为 {字段名: 默认值}
+        required_types: 字段的必需类型，格式为 {字段名: 类型}
+        allow_array: 是否允许解析JSON数组
+
+    Returns:
+        Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]: (是否成功, 提取的字段字典或字典列表)
+    """
+    content = content.strip()
+    result = {}
+
+    # 设置默认值
+    if default_values:
+        result.update(default_values)
+
+    # 首先尝试解析为JSON数组
+    if allow_array:
+        try:
+            # 尝试找到文本中的JSON数组
+            array_pattern = r"\[[\s\S]*\]"
+            array_match = re.search(array_pattern, content)
+            if array_match:
+                array_content = array_match.group()
+                json_array = json.loads(array_content)
+
+                # 确认是数组类型
+                if isinstance(json_array, list):
+                    # 验证数组中的每个项目是否包含所有必需字段
+                    valid_items = []
+                    for item in json_array:
+                        if not isinstance(item, dict):
+                            continue
+
+                        # 检查是否有所有必需字段
+                        if all(field in item for field in items):
+                            # 验证字段类型
+                            if required_types:
+                                type_valid = True
+                                for field, expected_type in required_types.items():
+                                    if field in item and not isinstance(item[field], expected_type):
+                                        type_valid = False
+                                        break
+
+                                if not type_valid:
+                                    continue
+
+                            # 验证字符串字段不为空
+                            string_valid = True
+                            for field in items:
+                                if isinstance(item[field], str) and not item[field].strip():
+                                    string_valid = False
+                                    break
+
+                            if not string_valid:
+                                continue
+
+                            valid_items.append(item)
+
+                    if valid_items:
+                        return True, valid_items
+        except json.JSONDecodeError:
+            logger.debug(f"[私聊][{private_name}]JSON数组解析失败，尝试解析单个JSON对象")
+        except Exception as e:
+            logger.debug(f"[私聊][{private_name}]尝试解析JSON数组时出错: {str(e)}")
+
+    # 尝试解析JSON对象
+    try:
+        json_data = json.loads(content)
+    except json.JSONDecodeError:
+        # 如果直接解析失败，尝试查找和提取JSON部分
+        json_pattern = r"\{[^{}]*\}"
+        json_match = re.search(json_pattern, content)
+        if json_match:
+            try:
+                json_data = json.loads(json_match.group())
+            except json.JSONDecodeError:
+                logger.error(f"[私聊][{private_name}]提取的JSON内容解析失败")
+                return False, result
+        else:
+            logger.error(f"[私聊][{private_name}]无法在返回内容中找到有效的JSON")
+            return False, result
+
+    # 提取字段
+    for item in items:
+        if item in json_data:
+            result[item] = json_data[item]
+
+    # 验证必需字段
+    if not all(item in result for item in items):
+        logger.error(f"[私聊][{private_name}]JSON缺少必要字段，实际内容: {json_data}")
+        return False, result
+
+    # 验证字段类型
+    if required_types:
+        for field, expected_type in required_types.items():
+            if field in result and not isinstance(result[field], expected_type):
+                logger.error(f"[私聊][{private_name}]{field} 必须是 {expected_type.__name__} 类型")
+                return False, result
+
+    # 验证字符串字段不为空
+    for field in items:
+        if isinstance(result[field], str) and not result[field].strip():
+            logger.error(f"[私聊][{private_name}]{field} 不能为空")
+            return False, result
+
+    return True, result

src/chat/brain_chat/PFC/reply_checker.py

@@ -0,0 +1,183 @@
+import json
+from typing import Tuple, List, Dict, Any
+from src.common.logger import get_module_logger
+from ..models.utils_model import LLMRequest
+from ...config.config import global_config
+from .chat_observer import ChatObserver
+from maim_message import UserInfo
+
+logger = get_module_logger("reply_checker")
+
+
+class ReplyChecker:
+    """回复检查器"""
+
+    def __init__(self, stream_id: str, private_name: str):
+        self.llm = LLMRequest(
+            model=global_config.llm_PFC_reply_checker, temperature=0.50, max_tokens=1000, request_type="reply_check"
+        )
+        self.name = global_config.BOT_NICKNAME
+        self.private_name = private_name
+        self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
+        self.max_retries = 3  # 最大重试次数
+
+    async def check(
+        self, reply: str, goal: str, chat_history: List[Dict[str, Any]], chat_history_text: str, retry_count: int = 0
+    ) -> Tuple[bool, str, bool]:
+        """检查生成的回复是否合适
+
+        Args:
+            reply: 生成的回复
+            goal: 对话目标
+            chat_history: 对话历史记录
+            chat_history_text: 对话历史记录文本
+            retry_count: 当前重试次数
+
+        Returns:
+            Tuple[bool, str, bool]: (是否合适, 原因, 是否需要重新规划)
+        """
+        # 不再从 observer 获取，直接使用传入的 chat_history
+        # messages = self.chat_observer.get_cached_messages(limit=20)
+        try:
+            # 筛选出最近由 Bot 自己发送的消息
+            bot_messages = []
+            for msg in reversed(chat_history):
+                user_info = UserInfo.from_dict(msg.get("user_info", {}))
+                if str(user_info.user_id) == str(global_config.BOT_QQ):  # 确保比较的是字符串
+                    bot_messages.append(msg.get("processed_plain_text", ""))
+                if len(bot_messages) >= 2:  # 只和最近的两条比较
+                    break
+                # 进行比较
+            if bot_messages:
+                # 可以用简单比较，或者更复杂的相似度库 (如 difflib)
+                # 简单比较：是否完全相同
+                if reply == bot_messages[0]:  # 和最近一条完全一样
+                    logger.warning(
+                        f"[私聊][{self.private_name}]ReplyChecker 检测到回复与上一条 Bot 消息完全相同: '{reply}'"
+                    )
+                    return (
+                        False,
+                        "被逻辑检查拒绝：回复内容与你上一条发言完全相同，可以选择深入话题或寻找其它话题或等待",
+                        True,
+                    )  # 不合适，需要返回至决策层
+                # 2. 相似度检查 (如果精确匹配未通过)
+                import difflib  # 导入 difflib 库
+
+                # 计算编辑距离相似度，ratio() 返回 0 到 1 之间的浮点数
+                similarity_ratio = difflib.SequenceMatcher(None, reply, bot_messages[0]).ratio()
+                logger.debug(f"[私聊][{self.private_name}]ReplyChecker - 相似度: {similarity_ratio:.2f}")
+
+                # 设置一个相似度阈值
+                similarity_threshold = 0.9
+                if similarity_ratio > similarity_threshold:
+                    logger.warning(
+                        f"[私聊][{self.private_name}]ReplyChecker 检测到回复与上一条 Bot 消息高度相似 (相似度 {similarity_ratio:.2f}): '{reply}'"
+                    )
+                    return (
+                        False,
+                        f"被逻辑检查拒绝：回复内容与你上一条发言高度相似 (相似度 {similarity_ratio:.2f})，可以选择深入话题或寻找其它话题或等待。",
+                        True,
+                    )
+
+        except Exception as e:
+            import traceback
+
+            logger.error(f"[私聊][{self.private_name}]检查回复时出错: 类型={type(e)}, 值={e}")
+            logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")  # 打印详细的回溯信息
+
+        prompt = f"""你是一个聊天逻辑检查器，请检查以下回复或消息是否合适：
+
+当前对话目标：{goal}
+最新的对话记录：
+{chat_history_text}
+
+待检查的消息：
+{reply}
+
+请结合聊天记录检查以下几点：
+1. 这条消息是否依然符合当前对话目标和实现方式
+2. 这条消息是否与最新的对话记录保持一致性
+3. 是否存在重复发言，或重复表达同质内容（尤其是只是换一种方式表达了相同的含义）
+4. 这条消息是否包含违规内容（例如血腥暴力，政治敏感等）
+5. 这条消息是否以发送者的角度发言（不要让发送者自己回复自己的消息）
+6. 这条消息是否通俗易懂
+7. 这条消息是否有些多余，例如在对方没有回复的情况下，依然连续多次“消息轰炸”（尤其是已经连续发送3条信息的情况，这很可能不合理，需要着重判断）
+8. 这条消息是否使用了完全没必要的修辞
+9. 这条消息是否逻辑通顺
+10. 这条消息是否太过冗长了（通常私聊的每条消息长度在20字以内，除非特殊情况）
+11. 在连续多次发送消息的情况下，这条消息是否衔接自然，会不会显得奇怪（例如连续两条消息中部分内容重叠）
+
+请以JSON格式输出，包含以下字段：
+1. suitable: 是否合适 (true/false)
+2. reason: 原因说明
+3. need_replan: 是否需要重新决策 (true/false)，当你认为此时已经不适合发消息，需要规划其它行动时，设为true
+
+输出格式示例：
+{{
+    "suitable": true,
+    "reason": "回复符合要求，虽然有可能略微偏离目标，但是整体内容流畅得体",
+    "need_replan": false
+}}
+
+注意：请严格按照JSON格式输出，不要包含任何其他内容。"""
+
+        try:
+            content, _ = await self.llm.generate_response_async(prompt)
+            logger.debug(f"[私聊][{self.private_name}]检查回复的原始返回: {content}")
+
+            # 清理内容，尝试提取JSON部分
+            content = content.strip()
+            try:
+                # 尝试直接解析
+                result = json.loads(content)
+            except json.JSONDecodeError:
+                # 如果直接解析失败，尝试查找和提取JSON部分
+                import re
+
+                json_pattern = r"\{[^{}]*\}"
+                json_match = re.search(json_pattern, content)
+                if json_match:
+                    try:
+                        result = json.loads(json_match.group())
+                    except json.JSONDecodeError:
+                        # 如果JSON解析失败，尝试从文本中提取结果
+                        is_suitable = "不合适" not in content.lower() and "违规" not in content.lower()
+                        reason = content[:100] if content else "无法解析响应"
+                        need_replan = "重新规划" in content.lower() or "目标不适合" in content.lower()
+                        return is_suitable, reason, need_replan
+                else:
+                    # 如果找不到JSON，从文本中判断
+                    is_suitable = "不合适" not in content.lower() and "违规" not in content.lower()
+                    reason = content[:100] if content else "无法解析响应"
+                    need_replan = "重新规划" in content.lower() or "目标不适合" in content.lower()
+                    return is_suitable, reason, need_replan
+
+            # 验证JSON字段
+            suitable = result.get("suitable", None)
+            reason = result.get("reason", "未提供原因")
+            need_replan = result.get("need_replan", False)
+
+            # 如果suitable字段是字符串，转换为布尔值
+            if isinstance(suitable, str):
+                suitable = suitable.lower() == "true"
+
+            # 如果suitable字段不存在或不是布尔值，从reason中判断
+            if suitable is None:
+                suitable = "不合适" not in reason.lower() and "违规" not in reason.lower()
+
+            # 如果不合适且未达到最大重试次数，返回需要重试
+            if not suitable and retry_count < self.max_retries:
+                return False, reason, False
+
+            # 如果不合适且已达到最大重试次数，返回需要重新规划
+            if not suitable and retry_count >= self.max_retries:
+                return False, f"多次重试后仍不合适: {reason}", True
+
+            return suitable, reason, need_replan
+
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]检查回复时出错: {e}")
+            # 如果出错且已达到最大重试次数，建议重新规划
+            if retry_count >= self.max_retries:
+                return False, "多次检查失败，建议重新规划", True
+            return False, f"检查过程出错，建议重试: {str(e)}", False

src/chat/brain_chat/PFC/reply_generator.py

@@ -0,0 +1,228 @@
+from typing import Tuple, List, Dict, Any
+from src.common.logger import get_module_logger
+from ..models.utils_model import LLMRequest
+from ...config.config import global_config
+from .chat_observer import ChatObserver
+from .reply_checker import ReplyChecker
+from src.individuality.individuality import Individuality
+from .observation_info import ObservationInfo
+from .conversation_info import ConversationInfo
+from src.plugins.utils.chat_message_builder import build_readable_messages
+
+logger = get_module_logger("reply_generator")
+
+# --- 定义 Prompt 模板 ---
+
+# Prompt for direct_reply (首次回复)
+PROMPT_DIRECT_REPLY = """{persona_text}。现在你在参与一场QQ私聊，请根据以下信息生成一条回复：
+
+当前对话目标：{goals_str}
+
+{knowledge_info_str}
+
+最近的聊天记录：
+{chat_history_text}
+
+
+请根据上述信息，结合聊天记录，回复对方。该回复应该：
+1. 符合对话目标，以"你"的角度发言（不要自己与自己对话！）
+2. 符合你的性格特征和身份细节
+3. 通俗易懂，自然流畅，像正常聊天一样，简短（通常20字以内，除非特殊情况）
+4. 可以适当利用相关知识，但不要生硬引用
+5. 自然、得体，结合聊天记录逻辑合理，且没有重复表达同质内容
+
+请注意把握聊天内容，不要回复的太有条理，可以有个性。请分清"你"和对方说的话，不要把"你"说的话当做对方说的话，这是你自己说的话。
+可以回复得自然随意自然一些，就像真人一样，注意把握聊天内容，整体风格可以平和、简短，不要刻意突出自身学科背景，不要说你说过的话，可以简短，多简短都可以，但是避免冗长。
+请你注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
+不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。
+
+请直接输出回复内容，不需要任何额外格式。"""
+
+# Prompt for send_new_message (追问/补充)
+PROMPT_SEND_NEW_MESSAGE = """{persona_text}。现在你在参与一场QQ私聊，**刚刚你已经发送了一条或多条消息**，现在请根据以下信息再发一条新消息： 
+
+当前对话目标：{goals_str}
+
+{knowledge_info_str}
+
+最近的聊天记录：
+{chat_history_text}
+
+
+请根据上述信息，结合聊天记录，继续发一条新消息（例如对之前消息的补充，深入话题，或追问等等）。该消息应该： 
+1. 符合对话目标，以"你"的角度发言（不要自己与自己对话！）
+2. 符合你的性格特征和身份细节
+3. 通俗易懂，自然流畅，像正常聊天一样，简短（通常20字以内，除非特殊情况）
+4. 可以适当利用相关知识，但不要生硬引用
+5. 跟之前你发的消息自然的衔接，逻辑合理，且没有重复表达同质内容或部分重叠内容
+
+请注意把握聊天内容，不用太有条理，可以有个性。请分清"你"和对方说的话，不要把"你"说的话当做对方说的话，这是你自己说的话。
+这条消息可以自然随意自然一些，就像真人一样，注意把握聊天内容，整体风格可以平和、简短，不要刻意突出自身学科背景，不要说你说过的话，可以简短，多简短都可以，但是避免冗长。
+请你注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出消息内容。
+不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。
+
+请直接输出回复内容，不需要任何额外格式。"""
+
+# Prompt for say_goodbye (告别语生成)
+PROMPT_FAREWELL = """{persona_text}。你在参与一场 QQ 私聊，现在对话似乎已经结束，你决定再发一条最后的消息来圆满结束。
+
+最近的聊天记录：
+{chat_history_text}
+
+请根据上述信息，结合聊天记录，构思一条**简短、自然、符合你人设**的最后的消息。
+这条消息应该：
+1. 从你自己的角度发言。
+2. 符合你的性格特征和身份细节。
+3. 通俗易懂，自然流畅，通常很简短。
+4. 自然地为这场对话画上句号，避免开启新话题或显得冗长、刻意。
+
+请像真人一样随意自然，**简洁是关键**。
+不要输出多余内容（包括前后缀、冒号、引号、括号、表情包、at或@等）。
+
+请直接输出最终的告别消息内容，不需要任何额外格式。"""
+
+
+class ReplyGenerator:
+    """回复生成器"""
+
+    def __init__(self, stream_id: str, private_name: str):
+        self.llm = LLMRequest(
+            model=global_config.llm_PFC_chat,
+            temperature=global_config.llm_PFC_chat["temp"],
+            max_tokens=300,
+            request_type="reply_generation",
+        )
+        self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
+        self.name = global_config.BOT_NICKNAME
+        self.private_name = private_name
+        self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
+        self.reply_checker = ReplyChecker(stream_id, private_name)
+
+    # 修改 generate 方法签名，增加 action_type 参数
+    async def generate(
+        self, observation_info: ObservationInfo, conversation_info: ConversationInfo, action_type: str
+    ) -> str:
+        """生成回复
+
+        Args:
+            observation_info: 观察信息
+            conversation_info: 对话信息
+            action_type: 当前执行的动作类型 ('direct_reply' 或 'send_new_message')
+
+        Returns:
+            str: 生成的回复
+        """
+        # 构建提示词
+        logger.debug(
+            f"[私聊][{self.private_name}]开始生成回复 (动作类型: {action_type})：当前目标: {conversation_info.goal_list}"
+        )
+
+        # --- 构建通用 Prompt 参数 ---
+        # (这部分逻辑基本不变)
+
+        # 构建对话目标 (goals_str)
+        goals_str = ""
+        if conversation_info.goal_list:
+            for goal_reason in conversation_info.goal_list:
+                if isinstance(goal_reason, dict):
+                    goal = goal_reason.get("goal", "目标内容缺失")
+                    reasoning = goal_reason.get("reasoning", "没有明确原因")
+                else:
+                    goal = str(goal_reason)
+                    reasoning = "没有明确原因"
+
+                goal = str(goal) if goal is not None else "目标内容缺失"
+                reasoning = str(reasoning) if reasoning is not None else "没有明确原因"
+                goals_str += f"- 目标：{goal}\n  原因：{reasoning}\n"
+        else:
+            goals_str = "- 目前没有明确对话目标\n"  # 简化无目标情况
+
+        # --- 新增：构建知识信息字符串 ---
+        knowledge_info_str = "【供参考的相关知识和记忆】\n"  # 稍微改下标题，表明是供参考
+        try:
+            # 检查 conversation_info 是否有 knowledge_list 并且不为空
+            if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list:
+                # 最多只显示最近的 5 条知识
+                recent_knowledge = conversation_info.knowledge_list[-5:]
+                for i, knowledge_item in enumerate(recent_knowledge):
+                    if isinstance(knowledge_item, dict):
+                        query = knowledge_item.get("query", "未知查询")
+                        knowledge = knowledge_item.get("knowledge", "无知识内容")
+                        source = knowledge_item.get("source", "未知来源")
+                        # 只取知识内容的前 2000 个字
+                        knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge
+                        knowledge_info_str += (
+                            f"{i + 1}. 关于 '{query}' (来源: {source}): {knowledge_snippet}\n"  # 格式微调，更简洁
+                        )
+                    else:
+                        knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n"
+
+                if not recent_knowledge:
+                    knowledge_info_str += "- 暂无。\n"  # 更简洁的提示
+
+            else:
+                knowledge_info_str += "- 暂无。\n"
+        except AttributeError:
+            logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。")
+            knowledge_info_str += "- 获取知识列表时出错。\n"
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}")
+            knowledge_info_str += "- 处理知识列表时出错。\n"
+
+        # 获取聊天历史记录 (chat_history_text)
+        chat_history_text = observation_info.chat_history_str
+        if observation_info.new_messages_count > 0 and observation_info.unprocessed_messages:
+            new_messages_list = observation_info.unprocessed_messages
+            new_messages_str = await build_readable_messages(
+                new_messages_list,
+                replace_bot_name=True,
+                merge_messages=False,
+                timestamp_mode="relative",
+                read_mark=0.0,
+            )
+            chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}"
+        elif not chat_history_text:
+            chat_history_text = "还没有聊天记录。"
+
+        # 构建 Persona 文本 (persona_text)
+        persona_text = f"你的名字是{self.name}，{self.personality_info}。"
+
+        # --- 选择 Prompt ---
+        if action_type == "send_new_message":
+            prompt_template = PROMPT_SEND_NEW_MESSAGE
+            logger.info(f"[私聊][{self.private_name}]使用 PROMPT_SEND_NEW_MESSAGE (追问生成)")
+        elif action_type == "say_goodbye":  # 处理告别动作
+            prompt_template = PROMPT_FAREWELL
+            logger.info(f"[私聊][{self.private_name}]使用 PROMPT_FAREWELL (告别语生成)")
+        else:  # 默认使用 direct_reply 的 prompt (包括 'direct_reply' 或其他未明确处理的类型)
+            prompt_template = PROMPT_DIRECT_REPLY
+            logger.info(f"[私聊][{self.private_name}]使用 PROMPT_DIRECT_REPLY (首次/非连续回复生成)")
+
+        # --- 格式化最终的 Prompt ---
+        prompt = prompt_template.format(
+            persona_text=persona_text,
+            goals_str=goals_str,
+            chat_history_text=chat_history_text,
+            knowledge_info_str=knowledge_info_str,
+        )
+
+        # --- 调用 LLM 生成 ---
+        logger.debug(f"[私聊][{self.private_name}]发送到LLM的生成提示词:\n------\n{prompt}\n------")
+        try:
+            content, _ = await self.llm.generate_response_async(prompt)
+            logger.debug(f"[私聊][{self.private_name}]生成的回复: {content}")
+            # 移除旧的检查新消息逻辑，这应该由 conversation 控制流处理
+            return content
+
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]生成回复时出错: {e}")
+            return "抱歉，我现在有点混乱，让我重新思考一下..."
+
+    # check_reply 方法保持不变
+    async def check_reply(
+        self, reply: str, goal: str, chat_history: List[Dict[str, Any]], chat_history_str: str, retry_count: int = 0
+    ) -> Tuple[bool, str, bool]:
+        """检查回复是否合适
+        (此方法逻辑保持不变)
+        """
+        return await self.reply_checker.check(reply, goal, chat_history, chat_history_str, retry_count)

src/chat/brain_chat/PFC/waiter.py

@@ -0,0 +1,79 @@
+from src.common.logger import get_module_logger
+from .chat_observer import ChatObserver
+from .conversation_info import ConversationInfo
+
+# from src.individuality.individuality import Individuality # 不再需要
+from ...config.config import global_config
+import time
+import asyncio
+
+logger = get_module_logger("waiter")
+
+# --- 在这里设定你想要的超时时间（秒） ---
+# 例如： 120 秒 = 2 分钟
+DESIRED_TIMEOUT_SECONDS = 300
+
+
+class Waiter:
+    """等待处理类"""
+
+    def __init__(self, stream_id: str, private_name: str):
+        self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
+        self.name = global_config.BOT_NICKNAME
+        self.private_name = private_name
+        # self.wait_accumulated_time = 0 # 不再需要累加计时
+
+    async def wait(self, conversation_info: ConversationInfo) -> bool:
+        """等待用户新消息或超时"""
+        wait_start_time = time.time()
+        logger.info(f"[私聊][{self.private_name}]进入常规等待状态 (超时: {DESIRED_TIMEOUT_SECONDS} 秒)...")
+
+        while True:
+            # 检查是否有新消息
+            if self.chat_observer.new_message_after(wait_start_time):
+                logger.info(f"[私聊][{self.private_name}]等待结束，收到新消息")
+                return False  # 返回 False 表示不是超时
+
+            # 检查是否超时
+            elapsed_time = time.time() - wait_start_time
+            if elapsed_time > DESIRED_TIMEOUT_SECONDS:
+                logger.info(f"[私聊][{self.private_name}]等待超过 {DESIRED_TIMEOUT_SECONDS} 秒...添加思考目标。")
+                wait_goal = {
+                    "goal": f"你等待了{elapsed_time / 60:.1f}分钟，注意可能在对方看来聊天已经结束，思考接下来要做什么",
+                    "reasoning": "对方很久没有回复你的消息了",
+                }
+                conversation_info.goal_list.append(wait_goal)
+                logger.info(f"[私聊][{self.private_name}]添加目标: {wait_goal}")
+                return True  # 返回 True 表示超时
+
+            await asyncio.sleep(5)  # 每 5 秒检查一次
+            logger.debug(
+                f"[私聊][{self.private_name}]等待中..."
+            )  # 可以考虑把这个频繁日志注释掉，只在超时或收到消息时输出
+
+    async def wait_listening(self, conversation_info: ConversationInfo) -> bool:
+        """倾听用户发言或超时"""
+        wait_start_time = time.time()
+        logger.info(f"[私聊][{self.private_name}]进入倾听等待状态 (超时: {DESIRED_TIMEOUT_SECONDS} 秒)...")
+
+        while True:
+            # 检查是否有新消息
+            if self.chat_observer.new_message_after(wait_start_time):
+                logger.info(f"[私聊][{self.private_name}]倾听等待结束，收到新消息")
+                return False  # 返回 False 表示不是超时
+
+            # 检查是否超时
+            elapsed_time = time.time() - wait_start_time
+            if elapsed_time > DESIRED_TIMEOUT_SECONDS:
+                logger.info(f"[私聊][{self.private_name}]倾听等待超过 {DESIRED_TIMEOUT_SECONDS} 秒...添加思考目标。")
+                wait_goal = {
+                    # 保持 goal 文本一致
+                    "goal": f"你等待了{elapsed_time / 60:.1f}分钟，对方似乎话说一半突然消失了，可能忙去了？也可能忘记了回复？要问问吗？还是结束对话？或继续等待？思考接下来要做什么",
+                    "reasoning": "对方话说一半消失了，很久没有回复",
+                }
+                conversation_info.goal_list.append(wait_goal)
+                logger.info(f"[私聊][{self.private_name}]添加目标: {wait_goal}")
+                return True  # 返回 True 表示超时
+
+            await asyncio.sleep(5)  # 每 5 秒检查一次
+            logger.debug(f"[私聊][{self.private_name}]倾听等待中...")  # 同上，可以考虑注释掉

src/chat/brain_chat/brain_chat.py

@@ -96,6 +96,9 @@ class BrainChatting:
 
         self.more_plan = False
 
+        # 最近一次是否成功进行了 reply，用于选择 BrainPlanner 的 Prompt
+        self._last_successful_reply: bool = False
+
     async def start(self):
         """检查是否需要启动主循环，如果未激活则启动。"""
 
@@ -157,6 +160,7 @@ class BrainChatting:
         )
 
     async def _loopbody(self):  # sourcery skip: hoist-if-from-if
+        # 获取最新消息（用于上下文，但不影响是否调用 observe）
         recent_messages_list = message_api.get_messages_by_time_in_chat(
             chat_id=self.stream_id,
             start_time=self.last_read_time,
@@ -165,17 +169,25 @@ class BrainChatting:
             limit_mode="latest",
             filter_mai=True,
             filter_command=False,
-            filter_no_read_command=True,
+            filter_intercept_message_level=1,
         )
 
+        # 如果有新消息，更新 last_read_time
         if len(recent_messages_list) >= 1:
             self.last_read_time = time.time()
-            await self._observe(recent_messages_list=recent_messages_list)
-
-        else:
-            # Normal模式：消息数量不足，等待
-            await asyncio.sleep(0.2)
-            return True
+        
+        # 总是执行一次思考迭代（不管有没有新消息）
+        # wait 动作会在其内部等待，不需要在这里处理
+        should_continue = await self._observe(recent_messages_list=recent_messages_list)
+        
+        if not should_continue:
+            # 选择了 complete_talk，返回 False 表示需要等待新消息
+            return False
+        
+        # 继续下一次迭代（除非选择了 complete_talk）
+        # 短暂等待后再继续，避免过于频繁的循环
+        await asyncio.sleep(0.1)
+        
         return True
 
     async def _send_and_store_reply(
@@ -272,14 +284,16 @@ class BrainChatting:
             except Exception as e:
                 logger.error(f"{self.log_prefix} 动作修改失败: {e}")
 
-            # 执行planner
+            # 获取必要信息
             is_group_chat, chat_target_info, _ = self.action_planner.get_necessary_info()
 
+            # 一次思考迭代：Think - Act - Observe
+            # 获取聊天上下文
             message_list_before_now = get_raw_msg_before_timestamp_with_chat(
                 chat_id=self.stream_id,
                 timestamp=time.time(),
                 limit=int(global_config.chat.max_context_size * 0.6),
-                filter_no_read_command=True,
+                filter_intercept_message_level=1,
             )
             chat_content_block, message_id_list = build_readable_messages_with_id(
                 messages=message_list_before_now,
@@ -290,12 +304,12 @@ class BrainChatting:
             )
 
             prompt_info = await self.action_planner.build_planner_prompt(
-                is_group_chat=is_group_chat,
                 chat_target_info=chat_target_info,
                 current_available_actions=available_actions,
                 chat_content_block=chat_content_block,
                 message_id_list=message_id_list,
                 interest=global_config.personality.interest,
+                prompt_key="brain_planner_prompt_react",
             )
             continue_flag, modified_message = await events_manager.handle_mai_events(
                 EventType.ON_PLAN, None, prompt_info[0], None, self.chat_stream.stream_id
@@ -311,7 +325,12 @@ class BrainChatting:
                     available_actions=available_actions,
                 )
 
-            # 3. 并行执行所有动作
+            # 检查是否有 complete_talk 动作（会停止后续迭代）
+            has_complete_talk = any(
+                action.action_type == "complete_talk" for action in action_to_use_info
+            )
+
+            # 并行执行所有动作
             action_tasks = [
                 asyncio.create_task(
                     self._execute_action(action, action_to_use_info, thinking_id, available_actions, cycle_timers)
@@ -343,7 +362,14 @@ class BrainChatting:
                     else:
                         logger.warning(f"{self.log_prefix} 回复动作执行失败")
 
-            # 构建最终的循环信息
+            # 更新观察时间标记
+            self.action_planner.last_obs_time_mark = time.time()
+
+            # 如果选择了 complete_talk，标记为完成，不再继续迭代
+            if has_complete_talk:
+                logger.info(f"{self.log_prefix} 检测到 complete_talk 动作，本次思考完成")
+
+            # 构建循环信息
             if reply_loop_info:
                 # 如果有回复信息，使用回复的loop_info作为基础
                 loop_info = reply_loop_info
@@ -369,10 +395,16 @@ class BrainChatting:
                 }
                 _reply_text = action_reply_text
 
+            # 如果选择了 complete_talk，返回 False 以停止 _loopbody 的循环
+            # 否则返回 True，让 _loopbody 继续下一次迭代
+            should_continue = not has_complete_talk
+
             self.end_cycle(loop_info, cycle_timers)
             self.print_cycle_info(cycle_timers)
 
-            return True
+            # 如果选择了 complete_talk，返回 False 停止循环
+            # 否则返回 True，继续下一次思考迭代
+            return should_continue
 
     async def _main_chat_loop(self):
         """主循环，持续进行计划并可能回复消息，直到被外部取消。"""
@@ -380,9 +412,13 @@ class BrainChatting:
             while self.running:
                 # 主循环
                 success = await self._loopbody()
-                await asyncio.sleep(0.1)
                 if not success:
-                    break
+                    # 选择了 complete，等待新消息
+                    logger.info(f"{self.log_prefix} 选择了 complete，等待新消息...")
+                    await self._wait_for_new_message()
+                    # 有新消息后继续循环
+                    continue
+                await asyncio.sleep(0.1)
         except asyncio.CancelledError:
             # 设置了关闭标志位后被取消是正常流程
             logger.info(f"{self.log_prefix} 麦麦已关闭聊天")
@@ -392,6 +428,33 @@ class BrainChatting:
             await asyncio.sleep(3)
             self._loop_task = asyncio.create_task(self._main_chat_loop())
         logger.error(f"{self.log_prefix} 结束了当前聊天循环")
+    
+    async def _wait_for_new_message(self):
+        """等待新消息到达"""
+        last_check_time = self.last_read_time
+        check_interval = 1.0  # 每秒检查一次
+        
+        while self.running:
+            # 检查是否有新消息
+            recent_messages_list = message_api.get_messages_by_time_in_chat(
+                chat_id=self.stream_id,
+                start_time=last_check_time,
+                end_time=time.time(),
+                limit=20,
+                limit_mode="latest",
+                filter_mai=True,
+                filter_command=False,
+                filter_intercept_message_level=1,
+            )
+            
+            # 如果有新消息，更新 last_read_time 并返回
+            if len(recent_messages_list) >= 1:
+                self.last_read_time = time.time()
+                logger.info(f"{self.log_prefix} 检测到新消息，恢复循环")
+                return
+            
+            # 等待一段时间后再次检查
+            await asyncio.sleep(check_interval)
 
     async def _handle_action(
         self,
@@ -506,12 +569,12 @@ class BrainChatting:
         """执行单个动作的通用函数"""
         try:
             with Timer(f"动作{action_planner_info.action_type}", cycle_timers):
-                if action_planner_info.action_type == "no_reply":
-                    # 直接处理no_reply逻辑，不再通过动作系统
-                    reason = action_planner_info.reasoning or "选择不回复"
-                    # logger.info(f"{self.log_prefix} 选择不回复，原因: {reason}")
+                if action_planner_info.action_type == "complete_talk":
+                    # 直接处理complete_talk逻辑，不再通过动作系统
+                    reason = action_planner_info.reasoning or "选择完成对话"
+                    logger.info(f"{self.log_prefix} 选择完成对话，原因: {reason}")
 
-                    # 存储no_reply信息到数据库
+                    # 存储complete_talk信息到数据库
                     await database_api.store_action_info(
                         chat_stream=self.chat_stream,
                         action_build_into_prompt=False,
@@ -519,9 +582,9 @@ class BrainChatting:
                         action_done=True,
                         thinking_id=thinking_id,
                         action_data={"reason": reason},
-                        action_name="no_reply",
+                        action_name="complete_talk",
                     )
-                    return {"action_type": "no_reply", "success": True, "reply_text": "", "command": ""}
+                    return {"action_type": "complete_talk", "success": True, "reply_text": "", "command": ""}
 
                 elif action_planner_info.action_type == "reply":
                     try:
@@ -543,11 +606,17 @@ class BrainChatting:
                                 )
                             else:
                                 logger.info("回复生成失败")
-                            return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
+                            return {
+                                "action_type": "reply",
+                                "success": False,
+                                "reply_text": "",
+                                "loop_info": None,
+                            }
 
                     except asyncio.CancelledError:
                         logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消")
                         return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
+
                     response_set = llm_response.reply_set
                     selected_expressions = llm_response.selected_expressions
                     loop_info, reply_text, _ = await self._send_and_store_reply(
@@ -558,6 +627,8 @@ class BrainChatting:
                         actions=chosen_action_plan_infos,
                         selected_expressions=selected_expressions,
                     )
+                    # 标记这次循环已经成功进行了回复
+                    self._last_successful_reply = True
                     return {
                         "action_type": "reply",
                         "success": True,
@@ -567,7 +638,88 @@ class BrainChatting:
 
                 # 其他动作
                 else:
-                    # 执行普通动作
+                    # 内建 wait / listening：不通过插件系统，直接在这里处理
+                    if action_planner_info.action_type in ["wait", "listening"]:
+                        reason = action_planner_info.reasoning or ""
+                        action_data = action_planner_info.action_data or {}
+
+                        if action_planner_info.action_type == "wait":
+                            # 获取等待时间（必填）
+                            wait_seconds = action_data.get("wait_seconds")
+                            if wait_seconds is None:
+                                logger.warning(f"{self.log_prefix} wait 动作缺少 wait_seconds 参数，使用默认值 5 秒")
+                                wait_seconds = 5
+                            else:
+                                try:
+                                    wait_seconds = float(wait_seconds)
+                                    if wait_seconds < 0:
+                                        logger.warning(f"{self.log_prefix} wait_seconds 不能为负数，使用默认值 5 秒")
+                                        wait_seconds = 5
+                                except (ValueError, TypeError):
+                                    logger.warning(f"{self.log_prefix} wait_seconds 参数格式错误，使用默认值 5 秒")
+                                    wait_seconds = 5
+                            
+                            logger.info(f"{self.log_prefix} 执行 wait 动作，等待 {wait_seconds} 秒")
+                            
+                            # 记录动作信息
+                            await database_api.store_action_info(
+                                chat_stream=self.chat_stream,
+                                action_build_into_prompt=False,
+                                action_prompt_display=reason or f"等待 {wait_seconds} 秒",
+                                action_done=True,
+                                thinking_id=thinking_id,
+                                action_data={"reason": reason, "wait_seconds": wait_seconds},
+                                action_name="wait",
+                            )
+                            
+                            # 等待指定时间
+                            await asyncio.sleep(wait_seconds)
+                            
+                            logger.info(f"{self.log_prefix} wait 动作完成，继续下一次思考")
+                            
+                            # 这些动作本身不产生文本回复
+                            self._last_successful_reply = False
+                            return {
+                                "action_type": "wait",
+                                "success": True,
+                                "reply_text": "",
+                                "command": "",
+                            }
+
+                        # listening 已合并到 wait，如果遇到则转换为 wait（向后兼容）
+                        elif action_planner_info.action_type == "listening":
+                            logger.debug(f"{self.log_prefix} 检测到 listening 动作，已合并到 wait，自动转换")
+                            # 使用默认等待时间
+                            wait_seconds = 3
+                            
+                            logger.info(f"{self.log_prefix} 执行 listening（转换为 wait）动作，等待 {wait_seconds} 秒")
+                            
+                            # 记录动作信息
+                            await database_api.store_action_info(
+                                chat_stream=self.chat_stream,
+                                action_build_into_prompt=False,
+                                action_prompt_display=reason or f"倾听并等待 {wait_seconds} 秒",
+                                action_done=True,
+                                thinking_id=thinking_id,
+                                action_data={"reason": reason, "wait_seconds": wait_seconds},
+                                action_name="listening",
+                            )
+                            
+                            # 等待指定时间
+                            await asyncio.sleep(wait_seconds)
+                            
+                            logger.info(f"{self.log_prefix} listening 动作完成，继续下一次思考")
+                            
+                            # 这些动作本身不产生文本回复
+                            self._last_successful_reply = False
+                            return {
+                                "action_type": "listening",
+                                "success": True,
+                                "reply_text": "",
+                                "command": "",
+                            }
+
+                    # 其余动作：走原有插件 Action 体系
                     with Timer("动作执行", cycle_timers):
                         success, reply_text, command = await self._handle_action(
                             action_planner_info.action_type,
@@ -577,6 +729,10 @@ class BrainChatting:
                             thinking_id,
                             action_planner_info.action_message,
                         )
+                    # 非 reply 类动作执行成功时，清空最近成功回复标记，让下一轮回到 initial Prompt
+                    if success and action_planner_info.action_type != "reply":
+                        self._last_successful_reply = False
+
                     return {
                         "action_type": action_planner_info.action_type,
                         "success": success,

src/chat/brain_chat/brain_planner.py

@@ -35,12 +35,14 @@ install(extra_lines=3)
 
 
 def init_prompt():
+    # ReAct 形式的 Planner Prompt
     Prompt(
         """
 {time_block}
 {name_block}
 你的兴趣是：{interest}
 {chat_context_description}，以下是具体的聊天内容
+
 **聊天内容**
 {chat_content_block}
 
@@ -57,11 +59,35 @@ reply
     "reason":"回复的原因"
 }}
 
-no_reply
+wait
 动作描述：
-等待，保持沉默，等待对方发言
+暂时不再发言，等待指定时间。适用于以下情况：
+- 你已经表达清楚一轮，想给对方留出空间
+- 你感觉对方的话还没说完，或者自己刚刚发了好几条连续消息
+- 你想要等待一定时间来让对方把话说完，或者等待对方反应
+- 你想保持安静，专注"听"而不是马上回复
+请你根据上下文来判断要等待多久，请你灵活判断：
+- 如果你们交流间隔时间很短，聊的很频繁，不宜等待太久
+- 如果你们交流间隔时间很长，聊的很少，可以等待较长时间
 {{
-    "action": "no_reply",
+    "action": "wait",
+    "target_message_id":"想要作为这次等待依据的消息id（通常是对方的最新消息）",
+    "wait_seconds": 等待的秒数（必填，例如：5 表示等待5秒）,
+    "reason":"选择等待的原因"
+}}
+
+complete_talk
+动作描述：
+当前聊天暂时结束了，对方离开，没有更多话题了
+你可以使用该动作来暂时休息，等待对方有新发言再继续：
+- 多次wait之后，对方迟迟不回复消息才用
+- 如果对方只是短暂不回复，应该使用wait而不是complete_talk
+- 聊天内容显示当前聊天已经结束或者没有新内容时候，选择complete_talk
+选择此动作后，将不再继续循环思考，直到收到对方的新消息
+{{
+    "action": "complete_talk",
+    "target_message_id":"触发完成对话的消息id（通常是对方的最新消息）",
+    "reason":"选择完成对话的原因"
 }}
 
 {action_options_text}
@@ -92,7 +118,7 @@ no_reply
 ```
 
 """,
-        "brain_planner_prompt",
+        "brain_planner_prompt_react",
     )
 
     Prompt(
@@ -122,6 +148,9 @@ class BrainPlanner:
         )  # 用于动作规划
 
         self.last_obs_time_mark = 0.0
+        
+        # 计划日志记录
+        self.plan_log: List[Tuple[str, float, List[ActionPlannerInfo]]] = []
 
     def find_message_by_id(
         self, message_id: str, message_id_list: List[Tuple[str, "DatabaseMessages"]]
@@ -152,10 +181,11 @@ class BrainPlanner:
         action_planner_infos = []
 
         try:
-            action = action_json.get("action", "no_reply")
+            action = action_json.get("action", "complete_talk")
+            logger.debug(f"{self.log_prefix}解析动作JSON: action={action}, json={action_json}")
             reasoning = action_json.get("reason", "未提供原因")
             action_data = {key: value for key, value in action_json.items() if key not in ["action", "reason"]}
-            # 非no_reply动作需要target_message_id
+            # 非complete_talk动作需要target_message_id
             target_message = None
 
             if target_message_id := action_json.get("target_message_id"):
@@ -171,16 +201,26 @@ class BrainPlanner:
 
             # 验证action是否可用
             available_action_names = [action_name for action_name, _ in current_available_actions]
-            internal_action_names = ["no_reply", "reply", "wait_time"]
+            # 内部保留动作（不依赖插件系统）
+            # 注意：listening 已合并到 wait 中，如果遇到 listening 则转换为 wait
+            internal_action_names = ["complete_talk", "reply", "wait_time", "wait", "listening"]
+            
+            logger.debug(f"{self.log_prefix}动作验证: action={action}, internal={internal_action_names}, available={available_action_names}")
+            
+            # 将 listening 转换为 wait（向后兼容）
+            if action == "listening":
+                logger.debug(f"{self.log_prefix}检测到 listening 动作，已合并到 wait，自动转换")
+                action = "wait"
 
             if action not in internal_action_names and action not in available_action_names:
                 logger.warning(
-                    f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (可用: {available_action_names})，将强制使用 'no_reply'"
+                    f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (内部动作: {internal_action_names}, 可用插件动作: {available_action_names})，将强制使用 'complete_talk'"
                 )
                 reasoning = (
                     f"LLM 返回了当前不可用的动作 '{action}' (可用: {available_action_names})。原始理由: {reasoning}"
                 )
-                action = "no_reply"
+                action = "complete_talk"
+                logger.warning(f"{self.log_prefix}动作已转换为 complete_talk")
 
             # 创建ActionPlannerInfo对象
             # 将列表转换为字典格式
@@ -201,7 +241,7 @@ class BrainPlanner:
             available_actions_dict = dict(current_available_actions)
             action_planner_infos.append(
                 ActionPlannerInfo(
-                    action_type="no_reply",
+                    action_type="complete_talk",
                     reasoning=f"解析单个action时出错: {e}",
                     action_data={},
                     action_message=None,
@@ -218,7 +258,7 @@ class BrainPlanner:
     ) -> List[ActionPlannerInfo]:
         # sourcery skip: use-named-expression
         """
-        规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
+        规划器 (Planner): 使用LLM根据上下文决定做出什么动作（ReAct模式）。
         """
 
         # 获取聊天上下文
@@ -226,7 +266,7 @@ class BrainPlanner:
             chat_id=self.chat_id,
             timestamp=time.time(),
             limit=int(global_config.chat.max_context_size * 0.6),
-            filter_no_read_command=True,
+            filter_intercept_message_level=1,
         )
         message_id_list: list[Tuple[str, "DatabaseMessages"]] = []
         chat_content_block, message_id_list = build_readable_messages_with_id(
@@ -257,18 +297,20 @@ class BrainPlanner:
 
         logger.debug(f"{self.log_prefix}过滤后有{len(filtered_actions)}个可用动作")
 
-        # 构建包含所有动作的提示词
+        # 构建包含所有动作的提示词：使用统一的 ReAct Prompt
+        prompt_key = "brain_planner_prompt_react"
+        # 这里不记录日志，避免重复打印，由调用方按需控制 log_prompt
         prompt, message_id_list = await self.build_planner_prompt(
-            is_group_chat=is_group_chat,
             chat_target_info=chat_target_info,
             current_available_actions=filtered_actions,
             chat_content_block=chat_content_block,
             message_id_list=message_id_list,
             interest=global_config.personality.interest,
+            prompt_key=prompt_key,
         )
 
         # 调用LLM获取决策
-        actions = await self._execute_main_planner(
+        reasoning, actions = await self._execute_main_planner(
             prompt=prompt,
             message_id_list=message_id_list,
             filtered_actions=filtered_actions,
@@ -276,16 +318,22 @@ class BrainPlanner:
             loop_start_time=loop_start_time,
         )
 
+        # 记录和展示计划日志
+        logger.info(
+            f"{self.log_prefix}Planner: {reasoning}。选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
+        )
+        self.add_plan_log(reasoning, actions)
+
         return actions
 
     async def build_planner_prompt(
         self,
-        is_group_chat: bool,
         chat_target_info: Optional["TargetPersonInfo"],
         current_available_actions: Dict[str, ActionInfo],
         message_id_list: List[Tuple[str, "DatabaseMessages"]],
         chat_content_block: str = "",
         interest: str = "",
+        prompt_key: str = "brain_planner_prompt_react",
     ) -> tuple[str, List[Tuple[str, "DatabaseMessages"]]]:
         """构建 Planner LLM 的提示词 (获取模板并填充数据)"""
         try:
@@ -321,7 +369,7 @@ class BrainPlanner:
             name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
 
             # 获取主规划器模板并填充
-            planner_prompt_template = await global_prompt_manager.get_prompt_async("brain_planner_prompt")
+            planner_prompt_template = await global_prompt_manager.get_prompt_async(prompt_key)
             prompt = planner_prompt_template.format(
                 time_block=time_block,
                 chat_context_description=chat_context_description,
@@ -431,17 +479,18 @@ class BrainPlanner:
         filtered_actions: Dict[str, ActionInfo],
         available_actions: Dict[str, ActionInfo],
         loop_start_time: float,
-    ) -> List[ActionPlannerInfo]:
+    ) -> Tuple[str, List[ActionPlannerInfo]]:
         """执行主规划器"""
         llm_content = None
         actions: List[ActionPlannerInfo] = []
+        extracted_reasoning = ""
 
         try:
             # 调用LLM
             llm_content, (reasoning_content, _, _) = await self.planner_llm.generate_response_async(prompt=prompt)
 
-            # logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
-            # logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
+            logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
+            logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
 
             if global_config.debug.show_planner_prompt:
                 logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
@@ -456,10 +505,11 @@ class BrainPlanner:
 
         except Exception as req_e:
             logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
-            return [
+            extracted_reasoning = f"LLM 请求失败，模型出现问题: {req_e}"
+            return extracted_reasoning, [
                 ActionPlannerInfo(
-                    action_type="no_reply",
-                    reasoning=f"LLM 请求失败，模型出现问题: {req_e}",
+                    action_type="complete_talk",
+                    reasoning=extracted_reasoning,
                     action_data={},
                     action_message=None,
                     available_actions=available_actions,
@@ -469,24 +519,32 @@ class BrainPlanner:
         # 解析LLM响应
         if llm_content:
             try:
-                if json_objects := self._extract_json_from_markdown(llm_content):
-                    logger.debug(f"{self.log_prefix}从响应中提取到{len(json_objects)}个JSON对象")
+                json_objects, extracted_reasoning = self._extract_json_from_markdown(llm_content)
+                if json_objects:
+                    logger.info(f"{self.log_prefix}从响应中提取到{len(json_objects)}个JSON对象")
+                    for i, json_obj in enumerate(json_objects):
+                        logger.info(f"{self.log_prefix}解析第{i+1}个JSON对象: {json_obj}")
                     filtered_actions_list = list(filtered_actions.items())
                     for json_obj in json_objects:
-                        actions.extend(self._parse_single_action(json_obj, message_id_list, filtered_actions_list))
+                        parsed_actions = self._parse_single_action(json_obj, message_id_list, filtered_actions_list)
+                        logger.info(f"{self.log_prefix}解析后的动作: {[a.action_type for a in parsed_actions]}")
+                        actions.extend(parsed_actions)
                 else:
                     # 尝试解析为直接的JSON
                     logger.warning(f"{self.log_prefix}LLM没有返回可用动作: {llm_content}")
-                    actions = self._create_no_reply("LLM没有返回可用动作", available_actions)
+                    extracted_reasoning = extracted_reasoning or "LLM没有返回可用动作"
+                    actions = self._create_complete_talk(extracted_reasoning, available_actions)
 
             except Exception as json_e:
                 logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
-                actions = self._create_no_reply(f"解析LLM响应JSON失败: {json_e}", available_actions)
+                extracted_reasoning = f"解析LLM响应JSON失败: {json_e}"
+                actions = self._create_complete_talk(extracted_reasoning, available_actions)
                 traceback.print_exc()
         else:
-            actions = self._create_no_reply("规划器没有获得LLM响应", available_actions)
+            extracted_reasoning = "规划器没有获得LLM响应"
+            actions = self._create_complete_talk(extracted_reasoning, available_actions)
 
-        # 添加循环开始时间到所有非no_reply动作
+        # 添加循环开始时间到所有动作
         for action in actions:
             action.action_data = action.action_data or {}
             action.action_data["loop_start_time"] = loop_start_time
@@ -495,47 +553,136 @@ class BrainPlanner:
             f"{self.log_prefix}规划器决定执行{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
         )
 
-        return actions
+        return extracted_reasoning, actions
 
-    def _create_no_reply(self, reasoning: str, available_actions: Dict[str, ActionInfo]) -> List[ActionPlannerInfo]:
-        """创建no_reply"""
+    def _create_complete_talk(self, reasoning: str, available_actions: Dict[str, ActionInfo]) -> List[ActionPlannerInfo]:
+        """创建complete_talk"""
         return [
             ActionPlannerInfo(
-                action_type="no_reply",
+                action_type="complete_talk",
                 reasoning=reasoning,
                 action_data={},
                 action_message=None,
                 available_actions=available_actions,
             )
         ]
+    
+    def add_plan_log(self, reasoning: str, actions: List[ActionPlannerInfo]):
+        """添加计划日志"""
+        self.plan_log.append((reasoning, time.time(), actions))
+        if len(self.plan_log) > 20:
+            self.plan_log.pop(0)
 
-    def _extract_json_from_markdown(self, content: str) -> List[dict]:
+    def _extract_json_from_markdown(self, content: str) -> Tuple[List[dict], str]:
         # sourcery skip: for-append-to-extend
-        """从Markdown格式的内容中提取JSON对象"""
+        """从Markdown格式的内容中提取JSON对象和推理内容"""
         json_objects = []
+        reasoning_content = ""
 
         # 使用正则表达式查找```json包裹的JSON内容
         json_pattern = r"```json\s*(.*?)\s*```"
-        matches = re.findall(json_pattern, content, re.DOTALL)
+        markdown_matches = re.findall(json_pattern, content, re.DOTALL)
 
-        for match in matches:
+        # 提取JSON之前的内容作为推理文本
+        first_json_pos = len(content)
+        if markdown_matches:
+            # 找到第一个```json的位置
+            first_json_pos = content.find("```json")
+            if first_json_pos > 0:
+                reasoning_content = content[:first_json_pos].strip()
+                # 清理推理内容中的注释标记
+                reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
+                reasoning_content = reasoning_content.strip()
+
+        # 处理```json包裹的JSON
+        for match in markdown_matches:
             try:
                 # 清理可能的注释和格式问题
                 json_str = re.sub(r"//.*?\n", "\n", match)  # 移除单行注释
                 json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL)  # 移除多行注释
                 if json_str := json_str.strip():
-                    json_obj = json.loads(repair_json(json_str))
-                    if isinstance(json_obj, dict):
-                        json_objects.append(json_obj)
-                    elif isinstance(json_obj, list):
-                        for item in json_obj:
-                            if isinstance(item, dict):
-                                json_objects.append(item)
+                    # 先尝试将整个块作为一个JSON对象或数组（适用于多行JSON）
+                    try:
+                        json_obj = json.loads(repair_json(json_str))
+                        if isinstance(json_obj, dict):
+                            json_objects.append(json_obj)
+                        elif isinstance(json_obj, list):
+                            for item in json_obj:
+                                if isinstance(item, dict):
+                                    json_objects.append(item)
+                    except json.JSONDecodeError:
+                        # 如果整个块解析失败，尝试按行分割（适用于多个单行JSON对象）
+                        lines = [line.strip() for line in json_str.split("\n") if line.strip()]
+                        for line in lines:
+                            try:
+                                # 尝试解析每一行作为独立的JSON对象
+                                json_obj = json.loads(repair_json(line))
+                                if isinstance(json_obj, dict):
+                                    json_objects.append(json_obj)
+                                elif isinstance(json_obj, list):
+                                    for item in json_obj:
+                                        if isinstance(item, dict):
+                                            json_objects.append(item)
+                            except json.JSONDecodeError:
+                                # 单行解析失败，继续下一行
+                                continue
             except Exception as e:
-                logger.warning(f"解析JSON块失败: {e}, 块内容: {match[:100]}...")
+                logger.warning(f"{self.log_prefix}解析JSON块失败: {e}, 块内容: {match[:100]}...")
                 continue
 
-        return json_objects
+        # 如果没有找到完整的```json```块，尝试查找不完整的代码块（缺少结尾```）
+        if not json_objects:
+            json_start_pos = content.find("```json")
+            if json_start_pos != -1:
+                # 找到```json之后的内容
+                json_content_start = json_start_pos + 7  # ```json的长度
+                # 提取从```json之后到内容结尾的所有内容
+                incomplete_json_str = content[json_content_start:].strip()
+
+                # 提取JSON之前的内容作为推理文本
+                if json_start_pos > 0:
+                    reasoning_content = content[:json_start_pos].strip()
+                    reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
+                    reasoning_content = reasoning_content.strip()
+
+                if incomplete_json_str:
+                    try:
+                        # 清理可能的注释和格式问题
+                        json_str = re.sub(r"//.*?\n", "\n", incomplete_json_str)
+                        json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL)
+                        json_str = json_str.strip()
+
+                        if json_str:
+                            # 尝试按行分割，每行可能是一个JSON对象
+                            lines = [line.strip() for line in json_str.split("\n") if line.strip()]
+                            for line in lines:
+                                try:
+                                    json_obj = json.loads(repair_json(line))
+                                    if isinstance(json_obj, dict):
+                                        json_objects.append(json_obj)
+                                    elif isinstance(json_obj, list):
+                                        for item in json_obj:
+                                            if isinstance(item, dict):
+                                                json_objects.append(item)
+                                except json.JSONDecodeError:
+                                    pass
+
+                            # 如果按行解析没有成功，尝试将整个块作为一个JSON对象或数组
+                            if not json_objects:
+                                try:
+                                    json_obj = json.loads(repair_json(json_str))
+                                    if isinstance(json_obj, dict):
+                                        json_objects.append(json_obj)
+                                    elif isinstance(json_obj, list):
+                                        for item in json_obj:
+                                            if isinstance(item, dict):
+                                                json_objects.append(item)
+                                except Exception as e:
+                                    logger.debug(f"尝试解析不完整的JSON代码块失败: {e}")
+                    except Exception as e:
+                        logger.debug(f"处理不完整的JSON代码块时出错: {e}")
+
+        return json_objects, reasoning_content
 
 
 init_prompt()

src/chat/heart_flow/heartFC_chat.py

@@ -190,7 +190,7 @@ class HeartFChatting:
             limit_mode="latest",
             filter_mai=True,
             filter_command=False,
-            filter_no_read_command=True,
+            filter_intercept_message_level=0,
         )
 
         # 根据连续 no_reply 次数动态调整阈值
@@ -485,7 +485,7 @@ class HeartFChatting:
                     chat_id=self.stream_id,
                     timestamp=time.time(),
                     limit=int(global_config.chat.max_context_size * 0.6),
-                    filter_no_read_command=True,
+                    filter_intercept_message_level=1,
                 )
                 chat_content_block, message_id_list = build_readable_messages_with_id(
                     messages=message_list_before_now,

src/chat/message_receive/bot.py

@@ -83,7 +83,7 @@ class ChatBot:
 
             self._started = True
 
-    async def _process_commands_with_new_system(self, message: MessageRecv):
+    async def _process_commands(self, message: MessageRecv):
         # sourcery skip: use-named-expression
         """使用新插件系统处理命令"""
         try:
@@ -115,17 +115,17 @@ class ChatBot:
 
                 try:
                     # 执行命令
-                    success, response, intercept_message = await command_instance.execute()
-                    message.is_no_read_command = bool(intercept_message)
+                    success, response, intercept_message_level = await command_instance.execute()
+                    message.intercept_message_level = intercept_message_level
 
                     # 记录命令执行结果
                     if success:
-                        logger.info(f"命令执行成功: {command_class.__name__} (拦截: {intercept_message})")
+                        logger.info(f"命令执行成功: {command_class.__name__} (拦截等级: {intercept_message_level})")
                     else:
                         logger.warning(f"命令执行失败: {command_class.__name__} - {response}")
 
                     # 根据命令的拦截设置决定是否继续处理消息
-                    return True, response, not intercept_message  # 找到命令，根据intercept_message决定是否继续
+                    return True, response, not bool(intercept_message_level)  # 找到命令，根据intercept_message决定是否继续
 
                 except Exception as e:
                     logger.error(f"执行命令时出错: {command_class.__name__} - {e}")
@@ -295,7 +295,7 @@ class ChatBot:
             #     return
 
             # 命令处理 - 使用新插件系统检查并处理命令
-            is_command, cmd_result, continue_process = await self._process_commands_with_new_system(message)
+            is_command, cmd_result, continue_process = await self._process_commands(message)
 
             # 如果是命令且不需要继续处理，则直接返回
             if is_command and not continue_process:

src/chat/message_receive/message.py

@@ -122,7 +122,7 @@ class MessageRecv(Message):
         self.is_notify = False
 
         self.is_command = False
-        self.is_no_read_command = False
+        self.intercept_message_level = 0
 
         self.priority_mode = "interest"
         self.priority_info = None

src/chat/message_receive/storage.py

@@ -72,7 +72,7 @@ class MessageStorage:
                 key_words = ""
                 key_words_lite = ""
                 selected_expressions = message.selected_expressions
-                is_no_read_command = False
+                intercept_message_level = 0
             else:
                 filtered_display_message = ""
                 interest_value = message.interest_value
@@ -86,7 +86,7 @@ class MessageStorage:
                 is_picid = message.is_picid
                 is_notify = message.is_notify
                 is_command = message.is_command
-                is_no_read_command = getattr(message, "is_no_read_command", False)
+                intercept_message_level = getattr(message, "intercept_message_level", 0)
                 # 序列化关键词列表为JSON字符串
                 key_words = MessageStorage._serialize_keywords(message.key_words)
                 key_words_lite = MessageStorage._serialize_keywords(message.key_words_lite)
@@ -138,7 +138,7 @@ class MessageStorage:
                 is_picid=is_picid,
                 is_notify=is_notify,
                 is_command=is_command,
-                is_no_read_command=is_no_read_command,
+                intercept_message_level=intercept_message_level,
                 key_words=key_words,
                 key_words_lite=key_words_lite,
                 selected_expressions=selected_expressions,

src/chat/planner_actions/action_modifier.py

@@ -69,7 +69,7 @@ class ActionModifier:
             chat_id=self.chat_stream.stream_id,
             timestamp=time.time(),
             limit=min(int(global_config.chat.max_context_size * 0.33), 10),
-            filter_no_read_command=True,
+            filter_intercept_message_level=1,
         )
 
         chat_content = build_readable_messages(

src/chat/planner_actions/planner.py

@@ -316,7 +316,7 @@ class ActionPlanner:
             chat_id=self.chat_id,
             timestamp=time.time(),
             limit=int(global_config.chat.max_context_size * 0.6),
-            filter_no_read_command=True,
+            filter_intercept_message_level=1,
         )
         message_id_list: list[Tuple[str, "DatabaseMessages"]] = []
         chat_content_block, message_id_list = build_readable_messages_with_id(

src/chat/replyer/group_generator.py

@@ -256,7 +256,7 @@ class DefaultReplyer:
             logger.debug(f"使用处理器选中的{len(selected_expressions)}个表达方式")
             for expr in selected_expressions:
                 if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-                    style_habits.append(f"当{expr['situation']}时，使用 {expr['style']}")
+                    style_habits.append(f"当{expr['situation']}时：{expr['style']}")
         else:
             logger.debug("没有从处理器获得表达方式，将使用空的表达方式")
             # 不再在replyer中进行随机选择，全部交给处理器处理
@@ -751,14 +751,14 @@ class DefaultReplyer:
             chat_id=chat_id,
             timestamp=reply_time_point,
             limit=global_config.chat.max_context_size * 1,
-            filter_no_read_command=True,
+            filter_intercept_message_level=1,
         )
 
         message_list_before_short = get_raw_msg_before_timestamp_with_chat(
             chat_id=chat_id,
             timestamp=reply_time_point,
             limit=int(global_config.chat.max_context_size * 0.33),
-            filter_no_read_command=True,
+            filter_intercept_message_level=1,
         )
 
         person_list_short: List[Person] = []
@@ -941,7 +941,7 @@ class DefaultReplyer:
             chat_id=chat_id,
             timestamp=time.time(),
             limit=min(int(global_config.chat.max_context_size * 0.33), 15),
-            filter_no_read_command=True,
+            filter_intercept_message_level=1,
         )
         chat_talking_prompt_half = build_readable_messages(
             message_list_before_now_half,

src/chat/replyer/private_generator.py

@@ -271,7 +271,7 @@ class PrivateReplyer:
             logger.debug(f"使用处理器选中的{len(selected_expressions)}个表达方式")
             for expr in selected_expressions:
                 if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-                    style_habits.append(f"当{expr['situation']}时，使用 {expr['style']}")
+                    style_habits.append(f"当{expr['situation']}时：{expr['style']}")
         else:
             logger.debug("没有从处理器获得表达方式，将使用空的表达方式")
             # 不再在replyer中进行随机选择，全部交给处理器处理
@@ -663,7 +663,7 @@ class PrivateReplyer:
             chat_id=chat_id,
             timestamp=time.time(),
             limit=global_config.chat.max_context_size,
-            filter_no_read_command=True,
+            filter_intercept_message_level=1,
         )
 
         dialogue_prompt = build_readable_messages(
@@ -678,7 +678,7 @@ class PrivateReplyer:
             chat_id=chat_id,
             timestamp=time.time(),
             limit=int(global_config.chat.max_context_size * 0.33),
-            filter_no_read_command=True,
+            filter_intercept_message_level=1,
         )
 
         person_list_short: List[Person] = []
@@ -878,7 +878,7 @@ class PrivateReplyer:
             chat_id=chat_id,
             timestamp=time.time(),
             limit=min(int(global_config.chat.max_context_size * 0.33), 15),
-            filter_no_read_command=True,
+            filter_intercept_message_level=1,
         )
         chat_talking_prompt_half = build_readable_messages(
             message_list_before_now_half,

src/chat/replyer/prompt/replyer_prompt.py

@@ -19,7 +19,7 @@ def init_replyer_prompt():
 {planner_reasoning}
 {identity}
 {chat_prompt}你正在群里聊天,现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，{mood_state}
-尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。
+尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理。
 {reply_style}
 请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出一句回复内容就好。
 不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。
@@ -39,7 +39,7 @@ def init_replyer_prompt():
 {planner_reasoning}
 {identity}
 {chat_prompt}你正在和{sender_name}聊天,现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，{mood_state}
-尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。
+尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理。
 {reply_style}
 请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
 {moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。""",

src/chat/utils/chat_message_builder.py

@@ -120,7 +120,7 @@ def get_raw_msg_by_timestamp_with_chat(
     limit_mode: str = "latest",
     filter_bot=False,
     filter_command=False,
-    filter_no_read_command=False,
+    filter_intercept_message_level: Optional[int] = None,
 ) -> List[DatabaseMessages]:
     """获取在特定聊天从指定时间戳到指定时间戳的消息，按时间升序排序，返回消息列表
     limit: 限制返回的消息数量，0为不限制
@@ -138,7 +138,7 @@ def get_raw_msg_by_timestamp_with_chat(
         limit_mode=limit_mode,
         filter_bot=filter_bot,
         filter_command=filter_command,
-        filter_no_read_command=filter_no_read_command,
+        filter_intercept_message_level=filter_intercept_message_level,
     )
 
 
@@ -150,7 +150,7 @@ def get_raw_msg_by_timestamp_with_chat_inclusive(
     limit_mode: str = "latest",
     filter_bot=False,
     filter_command=False,
-    filter_no_read_command=False,
+    filter_intercept_message_level: Optional[int] = None,
 ) -> List[DatabaseMessages]:
     """获取在特定聊天从指定时间戳到指定时间戳的消息（包含边界），按时间升序排序，返回消息列表
     limit: 限制返回的消息数量，0为不限制
@@ -167,7 +167,7 @@ def get_raw_msg_by_timestamp_with_chat_inclusive(
         limit_mode=limit_mode,
         filter_bot=filter_bot,
         filter_command=filter_command,
-        filter_no_read_command=filter_no_read_command,
+        filter_intercept_message_level=filter_intercept_message_level,
     )
 
 
@@ -303,7 +303,7 @@ def get_raw_msg_before_timestamp(timestamp: float, limit: int = 0) -> List[Datab
 
 
 def get_raw_msg_before_timestamp_with_chat(
-    chat_id: str, timestamp: float, limit: int = 0, filter_no_read_command: bool = False
+    chat_id: str, timestamp: float, limit: int = 0, filter_intercept_message_level: Optional[int] = None
 ) -> List[DatabaseMessages]:
     """获取指定时间戳之前的消息，按时间升序排序，返回消息列表
     limit: 限制返回的消息数量，0为不限制
@@ -311,7 +311,7 @@ def get_raw_msg_before_timestamp_with_chat(
     filter_query = {"chat_id": chat_id, "time": {"$lt": timestamp}}
     sort_order = [("time", 1)]
     return find_messages(
-        message_filter=filter_query, sort=sort_order, limit=limit, filter_no_read_command=filter_no_read_command
+        message_filter=filter_query, sort=sort_order, limit=limit, filter_intercept_message_level=filter_intercept_message_level
     )
 
 

src/chat/utils/statistic.py

@@ -8,7 +8,7 @@ from typing import Any, Dict, Tuple, List
 
 from src.common.logger import get_logger
 from src.common.database.database import db
-from src.common.database.database_model import OnlineTime, LLMUsage, Messages
+from src.common.database.database_model import OnlineTime, LLMUsage, Messages, ActionRecords
 from src.manager.async_task_manager import AsyncTask
 from src.manager.local_store_manager import local_storage
 from src.config.config import global_config
@@ -505,13 +505,6 @@ class StatisticOutputTask(AsyncTask):
             for period_key, _ in collect_period
         }
 
-        # 获取bot的QQ账号
-        bot_qq_account = (
-            str(global_config.bot.qq_account)
-            if hasattr(global_config, "bot") and hasattr(global_config.bot, "qq_account")
-            else ""
-        )
-
         query_start_timestamp = collect_period[-1][1].timestamp()  # Messages.time is a DoubleField (timestamp)
         for message in Messages.select().where(Messages.time >= query_start_timestamp):  # type: ignore
             message_time_ts = message.time  # This is a float timestamp
@@ -537,7 +530,7 @@ class StatisticOutputTask(AsyncTask):
             if not chat_id:  # Should not happen if above logic is correct
                 continue
 
-            # Update name_mapping
+            # Update name_mapping（仅用于展示聊天名称）
             try:
                 if chat_id in self.name_mapping:
                     if chat_name != self.name_mapping[chat_id][0] and message_time_ts > self.name_mapping[chat_id][1]:
@@ -549,19 +542,30 @@ class StatisticOutputTask(AsyncTask):
                 # 重置为正确的格式
                 self.name_mapping[chat_id] = (chat_name, message_time_ts)
 
-            # 检查是否是bot发送的消息（回复）
-            is_bot_reply = False
-            if bot_qq_account and message.user_id == bot_qq_account:
-                is_bot_reply = True
-
             for idx, (_, period_start_dt) in enumerate(collect_period):
                 if message_time_ts >= period_start_dt.timestamp():
                     for period_key, _ in collect_period[idx:]:
                         stats[period_key][TOTAL_MSG_CNT] += 1
                         stats[period_key][MSG_CNT_BY_CHAT][chat_id] += 1
-                        if is_bot_reply:
-                            stats[period_key][TOTAL_REPLY_CNT] += 1
                     break
+
+        # 使用 ActionRecords 中的 reply 动作次数作为回复数基准
+        try:
+            action_query_start_timestamp = collect_period[-1][1].timestamp()
+            for action in ActionRecords.select().where(ActionRecords.time >= action_query_start_timestamp):  # type: ignore
+                # 仅统计已完成的 reply 动作
+                if action.action_name != "reply" or not action.action_done:
+                    continue
+
+                action_time_ts = action.time
+                for idx, (_, period_start_dt) in enumerate(collect_period):
+                    if action_time_ts >= period_start_dt.timestamp():
+                        for period_key, _ in collect_period[idx:]:
+                            stats[period_key][TOTAL_REPLY_CNT] += 1
+                        break
+        except Exception as e:
+            logger.warning(f"统计 reply 动作次数失败，将回复数视为 0，错误信息：{e}")
+
         return stats
 
     def _collect_all_statistics(self, now: datetime) -> Dict[str, Dict[str, Any]]:

src/common/data_models/database_data_model.py

@@ -77,7 +77,7 @@ class DatabaseMessages(BaseDataModel):
         is_emoji: bool = False,
         is_picid: bool = False,
         is_command: bool = False,
-        is_no_read_command: bool = False,
+        intercept_message_level: int = 0,
         is_notify: bool = False,
         selected_expressions: Optional[str] = None,
         user_id: str = "",
@@ -120,7 +120,7 @@ class DatabaseMessages(BaseDataModel):
         self.is_emoji = is_emoji
         self.is_picid = is_picid
         self.is_command = is_command
-        self.is_no_read_command = is_no_read_command
+        self.intercept_message_level = intercept_message_level
         self.is_notify = is_notify
 
         self.selected_expressions = selected_expressions
@@ -188,7 +188,7 @@ class DatabaseMessages(BaseDataModel):
             "is_emoji": self.is_emoji,
             "is_picid": self.is_picid,
             "is_command": self.is_command,
-            "is_no_read_command": self.is_no_read_command,
+            "intercept_message_level": self.intercept_message_level,
             "is_notify": self.is_notify,
             "selected_expressions": self.selected_expressions,
             "user_id": self.user_info.user_id,

src/common/data_models/message_data_model.py

@@ -22,7 +22,7 @@ class MessageAndActionModel(BaseDataModel):
     is_action_record: bool = field(default=False)
     action_name: Optional[str] = None
     is_command: bool = field(default=False)
-    is_no_read_command: bool = field(default=False)
+    intercept_message_level: int = field(default=0)
 
     @classmethod
     def from_DatabaseMessages(cls, message: "DatabaseMessages"):
@@ -37,7 +37,7 @@ class MessageAndActionModel(BaseDataModel):
             display_message=message.display_message,
             chat_info_platform=message.chat_info.platform,
             is_command=message.is_command,
-            is_no_read_command=getattr(message, "is_no_read_command", False),
+            intercept_message_level=getattr(message, "intercept_message_level", 0),
         )
 
 

src/common/database/database_model.py

@@ -170,7 +170,7 @@ class Messages(BaseModel):
     is_emoji = BooleanField(default=False)
     is_picid = BooleanField(default=False)
     is_command = BooleanField(default=False)
-    is_no_read_command = BooleanField(default=False)
+    intercept_message_level = IntegerField(default=0)
     is_notify = BooleanField(default=False)
 
     selected_expressions = TextField(null=True)
@@ -324,7 +324,6 @@ class Expression(BaseModel):
 
     # new mode fields
     context = TextField(null=True)
-    up_content = TextField(null=True)
 
     content_list = TextField(null=True)
     count = IntegerField(default=1)

src/common/message_repository.py

@@ -25,7 +25,7 @@ def find_messages(
     limit_mode: str = "latest",
     filter_bot=False,
     filter_command=False,
-    filter_no_read_command=False,
+    filter_intercept_message_level: Optional[int] = None,
 ) -> List[DatabaseMessages]:
     """
     根据提供的过滤器、排序和限制条件查找消息。
@@ -85,8 +85,9 @@ def find_messages(
             # 使用按位取反构造 Peewee 的 NOT 条件，避免直接与 False 比较
             query = query.where(~Messages.is_command)
 
-        if filter_no_read_command:
-            query = query.where(~Messages.is_no_read_command)
+        if filter_intercept_message_level is not None:
+            # 过滤掉所有 intercept_message_level > filter_intercept_message_level 的消息
+            query = query.where(Messages.intercept_message_level <= filter_intercept_message_level)
 
         if limit > 0:
             if limit_mode == "earliest":

src/common/toml_utils.py

@@ -4,6 +4,7 @@ TOML 工具函数
 提供 TOML 文件的格式化保存功能，确保数组等元素以美观的多行格式输出。
 """
 
+import re
 from typing import Any
 import tomlkit
 from tomlkit.items import AoT, Table, Array
@@ -54,14 +55,71 @@ def _format_toml_value(obj: Any, threshold: int, depth: int = 0) -> Any:
     return obj
 
 
-def save_toml_with_format(data: Any, file_path: str, multiline_threshold: int = 1) -> None:
-    """格式化 TOML 数据并保存到文件"""
+def _update_toml_doc(target: Any, source: Any) -> None:
+    """
+    递归合并字典，将 source 的值更新到 target 中，保留 target 的注释和格式。
+    - 已存在的键：更新值（递归处理嵌套字典）
+    - 新增的键：添加到 target
+    - 跳过 version 字段
+    """
+    if isinstance(source, list) or not isinstance(source, dict) or not isinstance(target, dict):
+        return
+
+    for key, value in source.items():
+        if key == "version":
+            continue
+        if key in target:
+            # 已存在的键：递归更新或直接赋值
+            target_value = target[key]
+            if isinstance(value, dict) and isinstance(target_value, dict):
+                _update_toml_doc(target_value, value)
+            else:
+                try:
+                    target[key] = tomlkit.item(value)
+                except (TypeError, ValueError):
+                    target[key] = value
+        else:
+            # 新增的键：添加到 target
+            try:
+                target[key] = tomlkit.item(value)
+            except (TypeError, ValueError):
+                target[key] = value
+
+
+def save_toml_with_format(
+    data: Any, file_path: str, multiline_threshold: int = 1, preserve_comments: bool = True
+) -> None:
+    """
+    格式化 TOML 数据并保存到文件。
+
+    Args:
+        data: 要保存的数据（dict 或 tomlkit 文档）
+        file_path: 保存路径
+        multiline_threshold: 数组多行格式化阈值，-1 表示不格式化
+        preserve_comments: 是否保留原文件的注释和格式（默认 True）
+            若为 True 且文件已存在且 data 不是 tomlkit 文档，会先读取原文件，再将 data 合并进去
+    """
+    import os
+    from tomlkit import TOMLDocument
+
+    # 如果需要保留注释、文件存在、且 data 不是已有的 tomlkit 文档，先读取原文件再合并
+    if preserve_comments and os.path.exists(file_path) and not isinstance(data, TOMLDocument):
+        with open(file_path, "r", encoding="utf-8") as f:
+            doc = tomlkit.load(f)
+        _update_toml_doc(doc, data)
+        data = doc
+
     formatted = _format_toml_value(data, multiline_threshold) if multiline_threshold >= 0 else data
+    output = tomlkit.dumps(formatted)
+    # 规范化：将 3+ 连续空行压缩为 1 个空行，防止空行累积
+    output = re.sub(r'\n{3,}', '\n\n', output)
     with open(file_path, "w", encoding="utf-8") as f:
-        tomlkit.dump(formatted, f)
+        f.write(output)
 
 
 def format_toml_string(data: Any, multiline_threshold: int = 1) -> str:
     """格式化 TOML 数据并返回字符串"""
     formatted = _format_toml_value(data, multiline_threshold) if multiline_threshold >= 0 else data
-    return tomlkit.dumps(formatted)
+    output = tomlkit.dumps(formatted)
+    # 规范化：将 3+ 连续空行压缩为 1 个空行，防止空行累积
+    return re.sub(r'\n{3,}', '\n\n', output)

src/config/api_ada_configs.py

@@ -60,6 +60,12 @@ class ModelInfo(ConfigBase):
     price_out: float = field(default=0.0)
     """每M token输出价格"""
 
+    temperature: float | None = field(default=None)
+    """模型级别温度（可选），会覆盖任务配置中的温度"""
+
+    max_tokens: int | None = field(default=None)
+    """模型级别最大token数（可选），会覆盖任务配置中的max_tokens"""
+
     force_stream_mode: bool = field(default=False)
     """是否强制使用流式输出模式"""
 

src/config/config.py

@@ -35,6 +35,7 @@ from src.config.official_configs import (
     MemoryConfig,
     DebugConfig,
     JargonConfig,
+    DreamConfig,
 )
 
 from .api_ada_configs import (
@@ -57,7 +58,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template")
 
 # 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
 # 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/
-MMC_VERSION = "0.11.6"
+MMC_VERSION = "0.11.7-snapshot.1"
 
 
 def get_key_comment(toml_table, key):
@@ -357,6 +358,7 @@ class Config(ConfigBase):
     mood: MoodConfig
     voice: VoiceConfig
     jargon: JargonConfig
+    dream: DreamConfig
 
 
 @dataclass

src/config/official_configs.py

@@ -173,7 +173,11 @@ class ChatConfig(ConfigBase):
     def get_talk_value(self, chat_id: Optional[str]) -> float:
         """根据规则返回当前 chat 的动态 talk_value，未匹配则回退到基础值。"""
         if not self.enable_talk_value_rules or not self.talk_value_rules:
-            return self.talk_value
+            result = self.talk_value
+            # 防止返回0值，自动转换为0.0001
+            if result == 0:
+                return 0.0000001
+            return result
 
         now_min = self._now_minutes()
 
@@ -199,7 +203,11 @@ class ChatConfig(ConfigBase):
                 start_min, end_min = parsed
                 if self._in_range(now_min, start_min, end_min):
                     try:
-                        return float(value)
+                        result = float(value)
+                        # 防止返回0值，自动转换为0.0001
+                        if result == 0:
+                            return 0.0000001
+                        return result
                     except Exception:
                         continue
 
@@ -218,12 +226,20 @@ class ChatConfig(ConfigBase):
             start_min, end_min = parsed
             if self._in_range(now_min, start_min, end_min):
                 try:
-                    return float(value)
+                    result = float(value)
+                    # 防止返回0值，自动转换为0.0001
+                    if result == 0:
+                        return 0.0000001
+                    return result
                 except Exception:
                     continue
 
         # 3) 未命中规则返回基础值
-        return self.talk_value
+        result = self.talk_value
+        # 防止返回0值，自动转换为0.0001
+        if result == 0:
+            return 0.0000001
+        return result
 
 
 @dataclass
@@ -247,6 +263,9 @@ class MemoryConfig(ConfigBase):
     enable_jargon_detection: bool = True
     """记忆检索过程中是否启用黑话识别"""
 
+    global_memory: bool = False
+    """是否允许记忆检索在聊天记录中进行全局查询（忽略当前chat_id，仅对 search_chat_history 等工具生效）"""
+
     def __post_init__(self):
         """验证配置值"""
         if self.max_agent_iterations < 1:
@@ -342,22 +361,30 @@ class ExpressionConfig(ConfigBase):
             tuple: (是否使用表达, 是否学习表达, 学习间隔)
         """
         if not self.learning_list:
-            # 如果没有配置，使用默认值：启用表达，启用学习，300秒间隔
-            return True, True, 300
+            # 如果没有配置，使用默认值：启用表达，启用学习，学习强度1.0（对应300秒间隔）
+            return True, True, 1.0
 
         # 优先检查聊天流特定的配置
         if chat_stream_id:
             specific_expression_config = self._get_stream_specific_config(chat_stream_id)
             if specific_expression_config is not None:
-                return specific_expression_config
+                use_expression, enable_learning, learning_intensity = specific_expression_config
+                # 防止学习强度为0，自动转换为0.0001
+                if learning_intensity == 0:
+                    learning_intensity = 0.0000001
+                return use_expression, enable_learning, learning_intensity
 
         # 检查全局配置（第一个元素为空字符串的配置）
         global_expression_config = self._get_global_config()
         if global_expression_config is not None:
-            return global_expression_config
+            use_expression, enable_learning, learning_intensity = global_expression_config
+            # 防止学习强度为0，自动转换为0.0001
+            if learning_intensity == 0:
+                learning_intensity = 0.0000001
+            return use_expression, enable_learning, learning_intensity
 
-        # 如果都没有匹配，返回默认值
-        return True, True, 300
+        # 如果都没有匹配，返回默认值：启用表达，启用学习，学习强度1.0（对应300秒间隔）
+        return True, True, 1.0
 
     def _get_stream_specific_config(self, chat_stream_id: str) -> Optional[tuple[bool, bool, int]]:
         """
@@ -393,6 +420,9 @@ class ExpressionConfig(ConfigBase):
                 use_expression: bool = config_item[1].lower() == "enable"
                 enable_learning: bool = config_item[2].lower() == "enable"
                 learning_intensity: float = float(config_item[3])
+                # 防止学习强度为0，自动转换为0.0001
+                if learning_intensity == 0:
+                    learning_intensity = 0.0000001
                 return use_expression, enable_learning, learning_intensity  # type: ignore
             except (ValueError, IndexError):
                 continue
@@ -416,6 +446,9 @@ class ExpressionConfig(ConfigBase):
                     use_expression: bool = config_item[1].lower() == "enable"
                     enable_learning: bool = config_item[2].lower() == "enable"
                     learning_intensity = float(config_item[3])
+                    # 防止学习强度为0，自动转换为0.0001
+                    if learning_intensity == 0:
+                        learning_intensity = 0.0000001
                     return use_expression, enable_learning, learning_intensity  # type: ignore
                 except (ValueError, IndexError):
                     continue
@@ -714,3 +747,89 @@ class JargonConfig(ConfigBase):
 
     all_global: bool = False
     """是否将所有新增的jargon项目默认为全局（is_global=True），chat_id记录第一次存储时的id"""
+
+
+@dataclass
+class DreamConfig(ConfigBase):
+    """Dream配置类"""
+
+    interval_minutes: int = 30
+    """做梦时间间隔（分钟），默认30分钟"""
+
+    max_iterations: int = 20
+    """做梦最大轮次，默认20轮"""
+
+    first_delay_seconds: int = 60
+    """程序启动后首次做梦前的延迟时间（秒），默认60秒"""
+
+    dream_time_ranges: list[str] = field(default_factory=lambda: [])
+    """
+    做梦时间段配置列表，格式：["HH:MM-HH:MM", ...]
+    如果列表为空，则表示全天允许做梦。
+    如果配置了时间段，则只有在这些时间段内才会实际执行做梦流程。
+    时间段外，调度器仍会按间隔检查，但不会进入做梦流程。
+    
+    示例:
+    [
+        "09:00-22:00",      # 白天允许做梦
+        "23:00-02:00",      # 跨夜时间段（23:00到次日02:00）
+    ]
+    
+    支持跨夜区间，例如 "23:00-02:00" 表示从23:00到次日02:00。
+    """
+
+    def _now_minutes(self) -> int:
+        """返回本地时间的分钟数(0-1439)。"""
+        lt = time.localtime()
+        return lt.tm_hour * 60 + lt.tm_min
+
+    def _parse_range(self, range_str: str) -> Optional[tuple[int, int]]:
+        """解析 "HH:MM-HH:MM" 到 (start_min, end_min)。"""
+        try:
+            start_str, end_str = [s.strip() for s in range_str.split("-")]
+            sh, sm = [int(x) for x in start_str.split(":")]
+            eh, em = [int(x) for x in end_str.split(":")]
+            return sh * 60 + sm, eh * 60 + em
+        except Exception:
+            return None
+
+    def _in_range(self, now_min: int, start_min: int, end_min: int) -> bool:
+        """
+        判断 now_min 是否在 [start_min, end_min] 区间内。
+        支持跨夜：如果 start > end，则表示跨越午夜。
+        """
+        if start_min <= end_min:
+            return start_min <= now_min <= end_min
+        # 跨夜：例如 23:00-02:00
+        return now_min >= start_min or now_min <= end_min
+
+    def is_in_dream_time(self) -> bool:
+        """
+        检查当前时间是否在允许做梦的时间段内。
+        如果 dream_time_ranges 为空，则返回 True（全天允许）。
+        """
+        if not self.dream_time_ranges:
+            return True
+        
+        now_min = self._now_minutes()
+        
+        for time_range in self.dream_time_ranges:
+            if not isinstance(time_range, str):
+                continue
+            parsed = self._parse_range(time_range)
+            if not parsed:
+                continue
+            start_min, end_min = parsed
+            if self._in_range(now_min, start_min, end_min):
+                return True
+        
+        return False
+
+    def __post_init__(self):
+        """验证配置值"""
+        if self.interval_minutes < 1:
+            raise ValueError(f"interval_minutes 必须至少为1，当前值: {self.interval_minutes}")
+        if self.max_iterations < 1:
+            raise ValueError(f"max_iterations 必须至少为1，当前值: {self.max_iterations}")
+        if self.first_delay_seconds < 0:
+            raise ValueError(f"first_delay_seconds 不能为负数，当前值: {self.first_delay_seconds}")

src/dream/dream_agent.py

@@ -0,0 +1,558 @@
+import asyncio
+import random
+import time
+import json
+from typing import Any, Dict, List, Optional, Tuple
+
+from peewee import fn
+
+from src.common.logger import get_logger
+from src.config.config import global_config, model_config
+from src.common.database.database_model import ChatHistory, Jargon
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+from src.llm_models.payload_content.message import MessageBuilder, RoleType, Message
+from src.plugin_system.apis import llm_api
+from src.dream.dream_generator import generate_dream_summary
+
+# dream 工具工厂函数
+from src.dream.tools.search_chat_history_tool import make_search_chat_history
+from src.dream.tools.get_chat_history_detail_tool import make_get_chat_history_detail
+from src.dream.tools.delete_chat_history_tool import make_delete_chat_history
+from src.dream.tools.create_chat_history_tool import make_create_chat_history
+from src.dream.tools.update_chat_history_tool import make_update_chat_history
+from src.dream.tools.finish_maintenance_tool import make_finish_maintenance
+from src.dream.tools.search_jargon_tool import make_search_jargon
+from src.dream.tools.delete_jargon_tool import make_delete_jargon
+from src.dream.tools.update_jargon_tool import make_update_jargon
+
+logger = get_logger("dream_agent")
+
+
+def init_dream_prompts() -> None:
+    """初始化 dream agent 的提示词"""
+    Prompt(
+        """
+你的名字是{bot_name}，你现在处于"梦境维护模式（dream agent）"。
+你可以自由地在 ChatHistory 库中探索、整理、创建和删改记录，以帮助自己在未来更好地回忆和理解对话历史。
+
+本轮要维护的聊天ID：{chat_id}
+本轮随机选中的起始记忆 ID：{start_memory_id}
+请优先以这条起始记忆为切入点，先理解它的内容与上下文，再决定如何在其附近进行创建新概括、重写或删除等整理操作；如果起始记忆为空，则由你自行选择合适的切入点。
+
+你可以使用的工具包括：
+**ChatHistory 维护工具：**
+- search_chat_history：根据关键词或参与人搜索该 chat_id 下的历史记忆概括列表
+- get_chat_history_detail：查看某条概括的详细内容
+- create_chat_history：根据整理后的理解创建一条新的 ChatHistory 概括记录（主题、概括、关键词、关键信息等）
+- update_chat_history：在不改变事实的前提下重写或精炼主题、概括、关键词、关键信息
+- delete_chat_history：删除明显冗余、噪声、错误或无意义的记录，或者非常有时效性的信息，或者无太多有用信息的日常互动。
+你也可以先用 create_chat_history 创建一条新的综合概括，再对旧的冗余记录执行多次 delete_chat_history 来完成“合并”效果。
+
+**Jargon（黑话）维护工具（只读，禁止修改）：**
+- search_jargon：根据一个或多个关键词搜索Jargon 记录，通常是含义不明确的词条或者特殊的缩写
+
+**通用工具：**
+- finish_maintenance：当你认为当前维护工作已经完成，没有更多需要整理的内容时，调用此工具来结束本次运行
+
+**工作目标**：
+- 发现冗余、重复或高度相似的记录，并进行合并或删除；
+- 发现主题/概括过于含糊、啰嗦或缺少关键信息的记录，进行重写和精简；
+- summary要尽可能保持有用的信息；
+- 尽量保持信息的真实与可用性，不要凭空捏造事实。
+
+**合并准则**
+- 你可以新建一个记录，然后删除旧记录来实现合并。
+- 如果两个或多个记录的主题相似，内容是对主题不同方面的信息或讨论，且信息量较少，则可以合并为一条记录。
+- 如果两个记录冲突，可以根据逻辑保留一个或者进行整合，也可以采取更新的记录，删除旧的记录
+
+**轮次信息**：
+- 本次维护最多执行 {max_iterations} 轮
+- 每轮开始时，系统会告知你当前是第几轮，还剩多少轮
+- 如果提前完成维护工作，可以调用 finish_maintenance 工具主动结束
+
+**每一轮的执行方式（必须遵守）：**
+- 第一步：先用一小段中文自然语言，写出你的「思考」和本轮计划（例如要查什么、准备怎么合并/修改）；
+- 第二步：在这段思考之后，再通过工具调用来执行你的计划（可以调用 0~N 个工具）；
+- 第三步：收到工具结果后，在下一轮继续先写出新的思考，再视情况继续调用工具。
+
+请不要在没有先写出思考的情况下直接调用工具。
+只输出你的思考内容或工具调用结果，由系统负责真正执行工具调用。
+""",
+        name="dream_react_head_prompt",
+    )
+
+
+
+class DreamTool:
+    """dream 模块内部使用的简易工具封装"""
+
+    def __init__(self, name: str, description: str, parameters: List[Tuple], execute_func):
+        self.name = name
+        self.description = description
+        self.parameters = parameters
+        self.execute_func = execute_func
+
+    def get_tool_definition(self) -> Dict[str, Any]:
+        return {
+            "name": self.name,
+            "description": self.description,
+            "parameters": self.parameters,
+        }
+
+    async def execute(self, **kwargs) -> str:
+        return await self.execute_func(**kwargs)
+
+
+class DreamToolRegistry:
+    def __init__(self) -> None:
+        self.tools: Dict[str, DreamTool] = {}
+
+    def register_tool(self, tool: DreamTool) -> None:
+        """
+        注册或更新 dream 工具。
+        注意：dream agent 每个 chat_id 会重新初始化工具，这里允许覆盖已有同名工具。
+        """
+        self.tools[tool.name] = tool
+        logger.info(f"注册/更新 dream 工具: {tool.name}")
+
+    def get_tool(self, name: str) -> Optional[DreamTool]:
+        return self.tools.get(name)
+
+    def get_tool_definitions(self) -> List[Dict[str, Any]]:
+        return [tool.get_tool_definition() for tool in self.tools.values()]
+
+
+_dream_tool_registry = DreamToolRegistry()
+
+
+def get_dream_tool_registry() -> DreamToolRegistry:
+    return _dream_tool_registry
+
+
+def init_dream_tools(chat_id: str) -> None:
+    """注册 dream agent 可用的 ChatHistory / Jargon 相关工具（限定在当前 chat_id 作用域内）"""
+    from src.llm_models.payload_content.tool_option import ToolParamType
+
+    # 通过工厂函数生成绑定当前 chat_id 的工具实现
+    search_chat_history = make_search_chat_history(chat_id)
+    get_chat_history_detail = make_get_chat_history_detail(chat_id)
+    delete_chat_history = make_delete_chat_history(chat_id)
+    create_chat_history = make_create_chat_history(chat_id)
+    update_chat_history = make_update_chat_history(chat_id)
+    finish_maintenance = make_finish_maintenance(chat_id)
+
+    search_jargon = make_search_jargon(chat_id)
+    delete_jargon = make_delete_jargon(chat_id)
+    update_jargon = make_update_jargon(chat_id)
+
+    _dream_tool_registry.register_tool(
+        DreamTool(
+            "search_chat_history",
+            "根据关键词或参与人查询当前 chat_id 下的 ChatHistory 概览，便于快速定位相关记忆。",
+            [
+                ("keyword", ToolParamType.STRING, "关键词（可选，支持多个关键词，可用空格、逗号等分隔）。", False, None),
+                ("participant", ToolParamType.STRING, "参与人昵称（可选）。", False, None),
+            ],
+            search_chat_history,
+        )
+    )
+
+    _dream_tool_registry.register_tool(
+        DreamTool(
+            "get_chat_history_detail",
+            "根据 memory_id 获取单条 ChatHistory 的详细内容，包含主题、概括、关键词、关键信息等字段（不包含原文）。",
+            [
+                ("memory_id", ToolParamType.INTEGER, "ChatHistory 主键 ID。", True, None),
+            ],
+            get_chat_history_detail,
+        )
+    )
+
+    _dream_tool_registry.register_tool(
+        DreamTool(
+            "delete_chat_history",
+            "根据 memory_id 删除一条 ChatHistory 记录（请谨慎使用）。",
+            [
+                ("memory_id", ToolParamType.INTEGER, "需要删除的 ChatHistory 主键 ID。", True, None),
+            ],
+            delete_chat_history,
+        )
+    )
+
+    _dream_tool_registry.register_tool(
+        DreamTool(
+            "update_chat_history",
+            "按字段更新 ChatHistory 记录，可用于清理、重写或补充信息。",
+            [
+                ("memory_id", ToolParamType.INTEGER, "需要更新的 ChatHistory 主键 ID。", True, None),
+                ("theme", ToolParamType.STRING, "新的主题标题，如果不需要修改可不填。", False, None),
+                ("summary", ToolParamType.STRING, "新的概括内容，如果不需要修改可不填。", False, None),
+                ("keywords", ToolParamType.STRING, "新的关键词 JSON 字符串，如 ['关键词1','关键词2']。", False, None),
+                ("key_point", ToolParamType.STRING, "新的关键信息 JSON 字符串，如 ['要点1','要点2']。", False, None),
+            ],
+            update_chat_history,
+        )
+    )
+
+    _dream_tool_registry.register_tool(
+        DreamTool(
+            "create_chat_history",
+            "根据整理后的理解创建一条新的 ChatHistory 概括记录（主题、概括、关键词、关键信息等）。",
+            [
+                ("theme", ToolParamType.STRING, "新的主题标题（必填）。", True, None),
+                ("summary", ToolParamType.STRING, "新的概括内容（必填）。", True, None),
+                ("keywords", ToolParamType.STRING, "新的关键词 JSON 字符串，如 ['关键词1','关键词2']（必填）。", True, None),
+                ("key_point", ToolParamType.STRING, "新的关键信息 JSON 字符串，如 ['要点1','要点2']（必填）。", True, None),
+                ("start_time", ToolParamType.STRING, "起始时间戳（秒，Unix 时间，必填）。", True, None),
+                ("end_time", ToolParamType.STRING, "结束时间戳（秒，Unix 时间，必填）。", True, None),
+            ],
+            create_chat_history,
+        )
+    )
+
+    _dream_tool_registry.register_tool(
+        DreamTool(
+            "finish_maintenance",
+            "结束本次 dream 维护任务。当你认为当前 chat_id 下的维护工作已经完成，没有更多需要整理、合并或修改的内容时，调用此工具来主动结束本次运行。",
+            [
+                ("reason", ToolParamType.STRING, "结束维护的原因说明（可选），例如 '已完成所有记录的整理' 或 '当前记录质量良好，无需进一步维护'。", False, None),
+            ],
+            finish_maintenance,
+        )
+    )
+
+    # ==================== Jargon 维护工具 ====================
+    # 注册 Jargon 工具
+    _dream_tool_registry.register_tool(
+        DreamTool(
+            "search_jargon",
+            "根据一个或多个关键词搜索当前 chat_id 相关的 Jargon 记录概览（只包含 is_jargon=True，含全局 Jargon），便于快速理解黑话库。",
+            [
+                ("keyword", ToolParamType.STRING, "按一个或多个关键词搜索内容/含义/推断结果（必填）。", True, None),
+            ],
+            search_jargon,
+        )
+    )
+
+
+async def run_dream_agent_once(
+    chat_id: str,
+    max_iterations: Optional[int] = None,
+    start_memory_id: Optional[int] = None,
+) -> None:
+    """
+    运行一次 dream agent，对指定 chat_id 的 ChatHistory 进行最多 max_iterations 轮的整理。
+    如果 max_iterations 为 None，则使用配置文件中的默认值。
+    """
+    if max_iterations is None:
+        max_iterations = global_config.dream.max_iterations
+    
+    start_ts = time.time()
+    logger.info(f"[dream] 开始对 chat_id={chat_id} 进行 dream 维护，最多迭代 {max_iterations} 轮")
+
+    # 初始化工具（作用域限定在当前 chat_id）
+    init_dream_tools(chat_id)
+
+    tool_registry = get_dream_tool_registry()
+    tool_defs = tool_registry.get_tool_definitions()
+
+    bot_name = global_config.bot.nickname
+    time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+
+    head_prompt = await global_prompt_manager.format_prompt(
+        "dream_react_head_prompt",
+        bot_name=bot_name,
+        time_now=time_now,
+        chat_id=chat_id,
+        start_memory_id=start_memory_id if start_memory_id is not None else "无（本轮由你自由选择切入点）",
+        max_iterations=max_iterations,
+    )
+
+    conversation_messages: List[Message] = []
+
+    # 如果提供了起始记忆 ID，则在对话正式开始前，先把这条记忆的详细信息放入上下文，
+    # 避免 LLM 还需要额外调用一次 get_chat_history_detail 才能看到起始记忆内容。
+    if start_memory_id is not None:
+        try:
+            record = ChatHistory.get_or_none(ChatHistory.id == start_memory_id)
+            if record:
+                start_time_str = (
+                    time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(record.start_time))
+                    if record.start_time
+                    else "未知"
+                )
+                end_time_str = (
+                    time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(record.end_time))
+                    if record.end_time
+                    else "未知"
+                )
+                detail_text = (
+                    f"ID={record.id}\n"
+                    f"chat_id={record.chat_id}\n"
+                    f"时间范围={start_time_str} 至 {end_time_str}\n"
+                    f"主题={record.theme or '无'}\n"
+                    f"关键词={record.keywords or '无'}\n"
+                    f"参与者={record.participants or '无'}\n"
+                    f"概括={record.summary or '无'}\n"
+                    f"关键信息={record.key_point or '无'}"
+                )
+
+                logger.debug(
+                    f"[dream] 预加载起始记忆详情 memory_id={start_memory_id}，"
+                    f"预览: {detail_text[:200].replace(chr(10), ' ')}"
+                )
+
+                start_detail_builder = MessageBuilder()
+                start_detail_builder.set_role(RoleType.User)
+                start_detail_builder.add_text_content(
+                    "【起始记忆详情】以下是本轮随机/指定的起始记忆的详细信息，供你在整理时优先参考：\n\n"
+                    + detail_text
+                )
+                conversation_messages.append(start_detail_builder.build())
+            else:
+                logger.warning(
+                    f"[dream] 提供的 start_memory_id={start_memory_id} 未找到对应 ChatHistory 记录，"
+                    "将不预加载起始记忆详情。"
+                )
+        except Exception as e:
+            logger.error(f"[dream] 预加载起始记忆详情失败 start_memory_id={start_memory_id}: {e}")
+
+    # 注意：message_factory 必须是同步函数，返回消息列表（不能是 async/coroutine）
+    def message_factory(
+        _client,
+        *,
+        _head_prompt: str = head_prompt,
+        _conversation_messages: List[Message] = conversation_messages,
+    ) -> List[Message]:
+        messages: List[Message] = []
+        system_builder = MessageBuilder()
+        system_builder.set_role(RoleType.System)
+        system_builder.add_text_content(_head_prompt)
+        messages.append(system_builder.build())
+        messages.extend(_conversation_messages)
+        return messages
+
+    for iteration in range(1, max_iterations + 1):
+        # 在每轮开始时，添加轮次信息到对话中
+        remaining_rounds = max_iterations - iteration + 1
+        round_info_builder = MessageBuilder()
+        round_info_builder.set_role(RoleType.User)
+        round_info_builder.add_text_content(
+            f"【轮次信息】当前是第 {iteration}/{max_iterations} 轮，还剩 {remaining_rounds} 轮。"
+        )
+        conversation_messages.append(round_info_builder.build())
+
+        # 调用 LLM 让其决定是否要使用工具
+        success, response, reasoning_content, model_name, tool_calls = (
+            await llm_api.generate_with_model_with_tools_by_message_factory(
+                message_factory,
+                model_config=model_config.model_task_config.tool_use,
+                tool_options=tool_defs,
+                request_type="dream.react",
+            )
+        )
+
+        if not success:
+            logger.error(f"[dream] 第 {iteration} 轮 LLM 调用失败: {response}")
+            break
+
+        # 先输出「思考」内容，再输出工具调用信息（思考文本较长，仅在 debug 下输出）
+        thought_log = reasoning_content or (response[:300] if response else "")
+        if thought_log:
+            logger.debug(f"[dream] 第 {iteration} 轮思考内容: {thought_log}")
+
+        logger.info(
+            f"[dream] 第 {iteration} 轮响应，模型={model_name}，工具调用数={len(tool_calls) if tool_calls else 0}"
+        )
+
+        assistant_msg: Optional[Message] = None
+        if tool_calls:
+            builder = MessageBuilder()
+            builder.set_role(RoleType.Assistant)
+            if response and response.strip():
+                builder.add_text_content(response)
+            builder.set_tool_calls(tool_calls)
+            assistant_msg = builder.build()
+        elif response and response.strip():
+            builder = MessageBuilder()
+            builder.set_role(RoleType.Assistant)
+            builder.add_text_content(response)
+            assistant_msg = builder.build()
+
+        if assistant_msg:
+            conversation_messages.append(assistant_msg)
+
+        # 如果本轮没有工具调用，仅作为思考记录，继续下一轮
+        if not tool_calls:
+            logger.debug(f"[dream] 第 {iteration} 轮未调用任何工具，仅记录思考。")
+            continue
+
+        # 执行所有工具调用
+        tasks = []
+        finish_maintenance_called = False
+        for tc in tool_calls:
+            tool = tool_registry.get_tool(tc.func_name)
+            if not tool:
+                logger.warning(f"[dream] 未知工具：{tc.func_name}")
+                continue
+
+            # 检测是否调用了 finish_maintenance 工具
+            if tc.func_name == "finish_maintenance":
+                finish_maintenance_called = True
+
+            params = tc.args or {}
+
+            async def _run_single(t: DreamTool, p: Dict[str, Any], call_id: str, it: int):
+                try:
+                    result = await t.execute(**p)
+                    logger.debug(f"[dream] 第 {it} 轮 工具 {t.name} 执行完成")
+                    return call_id, result
+                except Exception as e:
+                    logger.error(f"[dream] 工具 {t.name} 执行失败: {e}")
+                    return call_id, f"工具 {t.name} 执行失败: {e}"
+
+            tasks.append(_run_single(tool, params, tc.call_id, iteration))
+
+        if not tasks:
+            continue
+
+        tool_results = await asyncio.gather(*tasks, return_exceptions=False)
+
+        # 将工具结果作为 Tool 消息追加
+        for call_id, obs in tool_results:
+            tool_builder = MessageBuilder()
+            tool_builder.set_role(RoleType.Tool)
+            tool_builder.add_text_content(str(obs))
+            tool_builder.add_tool_call(call_id)
+            conversation_messages.append(tool_builder.build())
+
+        # 如果调用了 finish_maintenance 工具，提前结束本次运行
+        if finish_maintenance_called:
+            logger.info(f"[dream] 第 {iteration} 轮检测到 finish_maintenance 工具调用，提前结束本次维护。")
+            break
+
+    cost = time.time() - start_ts
+    logger.info(f"[dream] 对 chat_id={chat_id} 的 dream 维护结束，共迭代 {iteration} 轮，耗时 {cost:.1f} 秒")
+
+    # 生成梦境总结
+    await generate_dream_summary(chat_id, conversation_messages, iteration, cost)
+
+
+def _pick_random_chat_id() -> Optional[str]:
+    """从 ChatHistory 中随机选择一个 chat_id，用于 dream agent 本次维护
+
+    规则：
+    - 只在 chat_id 所属的 ChatHistory 记录数 >= 10 时才会参与随机选择；
+    - 记录数不足 10 的 chat_id 将被跳过，不会触发做梦 react。
+    """
+    try:
+        # 统计每个 chat_id 的记录数，只保留记录数 >= 10 的 chat_id
+        rows = (
+            ChatHistory.select(ChatHistory.chat_id, fn.COUNT(ChatHistory.id).alias("cnt"))
+            .group_by(ChatHistory.chat_id)
+            .having(fn.COUNT(ChatHistory.id) >= 10)
+            .order_by(ChatHistory.chat_id)
+            .limit(200)
+        )
+        eligible_ids = [r.chat_id for r in rows]
+        if not eligible_ids:
+            logger.warning("[dream] ChatHistory 中暂无满足条件（记录数 >= 10）的 chat_id，本轮 dream 任务跳过。")
+            return None
+        chosen = random.choice(eligible_ids)
+        logger.info(f"[dream] 从 {len(eligible_ids)} 个满足条件的 chat_id 中随机选择：{chosen}")
+        return chosen
+    except Exception as e:
+        logger.error(f"[dream] 随机选择 chat_id 失败: {e}")
+        return None
+
+
+def _pick_random_memory_for_chat(chat_id: str) -> Optional[int]:
+    """
+    在给定 chat_id 下随机选择一条 ChatHistory 记录，作为本轮整理的起始记忆。
+    """
+    try:
+        rows = (
+            ChatHistory.select(ChatHistory.id)
+            .where(ChatHistory.chat_id == chat_id)
+            .order_by(ChatHistory.start_time.asc())
+            .limit(200)
+        )
+        ids = [r.id for r in rows]
+        if not ids:
+            logger.warning(f"[dream] chat_id={chat_id} 下暂无 ChatHistory 记录，无法选择起始记忆。")
+            return None
+        return random.choice(ids)
+    except Exception as e:
+        logger.error(f"[dream] 在 chat_id={chat_id} 下随机选择起始记忆失败: {e}")
+        return None
+
+
+async def run_dream_cycle_once() -> None:
+    """
+    单次 dream 周期：
+    - 随机选择一个 chat_id
+    - 在该 chat_id 下随机选择一条 ChatHistory 作为起始记忆
+    - 以这条起始记忆为切入点，对该 chat_id 运行一次 dream agent（最多 15 轮）
+    """
+    chat_id = _pick_random_chat_id()
+    if not chat_id:
+        return
+
+    start_memory_id = _pick_random_memory_for_chat(chat_id)
+    await run_dream_agent_once(
+        chat_id=chat_id,
+        max_iterations=None,  # 使用配置文件中的默认值
+        start_memory_id=start_memory_id,
+    )
+
+
+async def start_dream_scheduler(
+    first_delay_seconds: Optional[int] = None,
+    interval_seconds: Optional[int] = None,
+    stop_event: Optional[asyncio.Event] = None,
+) -> None:
+    """
+    dream 调度器：
+    - 程序启动后先等待 first_delay_seconds（如果为 None，则使用配置文件中的值，默认 60s）
+    - 然后每隔 interval_seconds（如果为 None，则使用配置文件中的值，默认 30 分钟）运行一次 dream agent 周期
+    - 如果提供 stop_event，则在 stop_event 被 set() 后优雅退出循环
+    """
+    if first_delay_seconds is None:
+        first_delay_seconds = global_config.dream.first_delay_seconds
+
+    if interval_seconds is None:
+        interval_seconds = global_config.dream.interval_minutes * 60
+    
+    logger.info(
+        f"[dream] dream 调度器启动：首次延迟 {first_delay_seconds}s，之后每隔 {interval_seconds}s ({interval_seconds // 60} 分钟) 运行一次 dream agent"
+    )
+
+    try:
+        await asyncio.sleep(first_delay_seconds)
+        while True:
+            if stop_event is not None and stop_event.is_set():
+                logger.info("[dream] 收到停止事件，结束 dream 调度器循环。")
+                break
+
+            start_ts = time.time()
+            # 检查当前时间是否在允许做梦的时间段内
+            if not global_config.dream.is_in_dream_time():
+                logger.debug("[dream] 当前时间不在允许做梦的时间段内，跳过本次执行")
+            else:
+                try:
+                    await run_dream_cycle_once()
+                except Exception as e:
+                    logger.error(f"[dream] 单次 dream 周期执行异常: {e}")
+
+            elapsed = time.time() - start_ts
+            # 保证两次执行之间至少间隔 interval_seconds
+            to_sleep = max(0.0, interval_seconds - elapsed)
+            await asyncio.sleep(to_sleep)
+    except asyncio.CancelledError:
+        logger.info("[dream] dream 调度器任务被取消，准备退出。")
+        raise
+
+
+# 初始化提示词
+init_dream_prompts()
+

src/dream/dream_generator.py

@@ -0,0 +1,198 @@
+import random
+from typing import List, Optional
+
+from src.common.logger import get_logger
+from src.config.config import model_config
+from src.chat.utils.prompt_builder import Prompt
+from src.llm_models.payload_content.message import RoleType, Message
+from src.llm_models.utils_model import LLMRequest
+
+logger = get_logger("dream_generator")
+
+# 初始化 utils 模型用于生成梦境总结
+_dream_summary_model: Optional[LLMRequest] = None
+
+# 梦境风格列表（21种）
+DREAM_STYLES = [
+    "保持诗意和想象力，自由编写",
+    "诗意朦胧，如薄雾笼罩的清晨",
+    "奇幻冒险，充满未知与探索",
+    "温暖怀旧，带着时光的痕迹",
+    "神秘悬疑，暗藏深意",
+    "浪漫唯美，如诗如画",
+    "科幻未来，科技与想象交织",
+    "自然清新，如山林间的微风",
+    "深沉哲思，引人深思",
+    "轻松幽默，充满趣味",
+    "悲伤忧郁，带着淡淡哀愁",
+    "激昂热烈，充满活力",
+    "宁静平和，如湖面般平静",
+    "荒诞离奇，打破常规",
+    "细腻温柔，如春风拂面",
+    "壮阔宏大，气势磅礴",
+    "简约纯粹，返璞归真",
+    "复杂多变，层次丰富",
+    "梦幻迷离，虚实难辨",
+    "现实写意，贴近生活",
+    "抽象概念，超越具象",
+]
+
+
+def get_random_dream_styles(count: int = 2) -> List[str]:
+    """从梦境风格列表中随机选择指定数量的风格"""
+    return random.sample(DREAM_STYLES, min(count, len(DREAM_STYLES)))
+
+
+def get_dream_summary_model() -> LLMRequest:
+    """获取用于生成梦境总结的 utils 模型实例"""
+    global _dream_summary_model
+    if _dream_summary_model is None:
+        _dream_summary_model = LLMRequest(
+            model_set=model_config.model_task_config.utils,
+            request_type="dream.summary",
+        )
+    return _dream_summary_model
+
+
+def init_dream_summary_prompt() -> None:
+    """初始化梦境总结的提示词"""
+    Prompt(
+        """
+你刚刚完成了一次对聊天记录的记忆整理工作。以下是整理过程的摘要：
+整理过程：
+{conversation_text}
+
+请将这次整理涉及的相关信息改写为一个富有诗意和想象力的"梦境"，请你仅使用具体的记忆的内容，而不是整理过程编写。
+要求：
+1. 使用第一人称视角
+2. 叙述直白，不要复杂修辞，口语化
+3. 长度控制在200-800字
+4. 用中文输出
+梦境风格：
+{dream_styles}
+请直接输出梦境内容，不要添加其他说明：
+""",
+        name="dream_summary_prompt",
+    )
+
+
+async def generate_dream_summary(
+    chat_id: str,
+    conversation_messages: List[Message],
+    total_iterations: int,
+    time_cost: float,
+) -> None:
+    """生成梦境总结并输出到日志"""
+    try:
+        import json
+        from src.chat.utils.prompt_builder import global_prompt_manager
+        
+        # 第一步：建立工具调用结果映射 (call_id -> result)
+        tool_results_map: dict[str, str] = {}
+        for msg in conversation_messages:
+            if msg.role == RoleType.Tool and msg.tool_call_id:
+                content = ""
+                if msg.content:
+                    if isinstance(msg.content, list) and msg.content:
+                        content = msg.content[0].text if hasattr(msg.content[0], "text") else str(msg.content[0])
+                    else:
+                        content = str(msg.content)
+                tool_results_map[msg.tool_call_id] = content
+        
+        # 第二步：详细记录所有工具调用操作和结果到日志
+        tool_call_count = 0
+        logger.info(f"[dream][工具调用详情] 开始记录 chat_id={chat_id} 的所有工具调用操作：")
+        
+        for msg in conversation_messages:
+            if msg.role == RoleType.Assistant and msg.tool_calls:
+                tool_call_count += 1
+                # 提取思考内容
+                thought_content = ""
+                if msg.content:
+                    if isinstance(msg.content, list) and msg.content:
+                        thought_content = msg.content[0].text if hasattr(msg.content[0], "text") else str(msg.content[0])
+                    else:
+                        thought_content = str(msg.content)
+                
+                logger.info(f"[dream][工具调用详情] === 第 {tool_call_count} 组工具调用 ===")
+                if thought_content:
+                    logger.info(f"[dream][工具调用详情] 思考内容：{thought_content[:500]}{'...' if len(thought_content) > 500 else ''}")
+                
+                # 记录每个工具调用的详细信息
+                for idx, tool_call in enumerate(msg.tool_calls, 1):
+                    tool_name = tool_call.func_name
+                    tool_args = tool_call.args or {}
+                    tool_call_id = tool_call.call_id
+                    tool_result = tool_results_map.get(tool_call_id, "未找到执行结果")
+                    
+                    # 格式化参数
+                    try:
+                        args_str = json.dumps(tool_args, ensure_ascii=False, indent=2) if tool_args else "无参数"
+                    except Exception:
+                        args_str = str(tool_args)
+                    
+                    logger.info(f"[dream][工具调用详情] --- 工具 {idx}: {tool_name} ---")
+                    logger.info(f"[dream][工具调用详情] 调用参数：\n{args_str}")
+                    logger.info(f"[dream][工具调用详情] 执行结果：\n{tool_result}")
+                    logger.info(f"[dream][工具调用详情] {'-' * 60}")
+        
+        logger.info(f"[dream][工具调用详情] 共记录了 {tool_call_count} 组工具调用操作")
+        
+        # 第三步：构建对话历史摘要（用于生成梦境）
+        conversation_summary = []
+        for msg in conversation_messages:
+            role = msg.role.value if hasattr(msg.role, "value") else str(msg.role)
+            content = ""
+            if msg.content:
+                content = msg.content[0].text if isinstance(msg.content, list) and msg.content else str(msg.content)
+            
+            if role == "user" and "轮次信息" in content:
+                # 跳过轮次信息消息
+                continue
+            
+            if role == "assistant":
+                # 只保留思考内容，简化工具调用信息
+                if content:
+                    # 截取前500字符，避免过长
+                    content_preview = content[:500] + ("..." if len(content) > 500 else "")
+                    conversation_summary.append(f"[{role}] {content_preview}")
+            elif role == "tool":
+                # 工具结果，只保留关键信息
+                if content:
+                    # 截取前300字符
+                    content_preview = content[:300] + ("..." if len(content) > 300 else "")
+                    conversation_summary.append(f"[工具执行] {content_preview}")
+        
+        conversation_text = "\n".join(conversation_summary[-20:])  # 只保留最后20条消息
+        
+        # 随机选择2个梦境风格
+        selected_styles = get_random_dream_styles(2)
+        dream_styles_text = "\n".join([f"{i+1}. {style}" for i, style in enumerate(selected_styles)])
+        
+        # 使用 Prompt 管理器格式化梦境生成 prompt
+        dream_prompt = await global_prompt_manager.format_prompt(
+            "dream_summary_prompt",
+            chat_id=chat_id,
+            total_iterations=total_iterations,
+            time_cost=time_cost,
+            conversation_text=conversation_text,
+            dream_styles=dream_styles_text,
+        )
+
+        # 调用 utils 模型生成梦境
+        summary_model = get_dream_summary_model()
+        dream_content, (reasoning, model_name, _) = await summary_model.generate_response_async(
+            dream_prompt,
+            max_tokens=512,
+            temperature=0.8,
+        )
+        
+        if dream_content:
+            logger.info(f"[dream][梦境总结] 对 chat_id={chat_id} 的整理过程梦境：\n{dream_content}")
+        else:
+            logger.warning("[dream][梦境总结] 未能生成梦境总结")
+            
+    except Exception as e:
+        logger.error(f"[dream][梦境总结] 生成梦境总结失败: {e}", exc_info=True)
+
+init_dream_summary_prompt()

src/dream/tools/__init__.py

@@ -0,0 +1,10 @@
+"""
+dream agent 工具实现模块。
+
+每个工具的具体实现放在独立文件中，通过 make_xxx(chat_id) 工厂函数
+生成绑定到特定 chat_id 的协程函数，由 dream_agent.init_dream_tools 统一注册。
+"""
+
+
+
+

src/dream/tools/create_chat_history_tool.py

@@ -0,0 +1,66 @@
+import time
+
+from src.common.logger import get_logger
+from src.common.database.database_model import ChatHistory
+
+logger = get_logger("dream_agent")
+
+
+def make_create_chat_history(chat_id: str):
+    async def create_chat_history(
+        theme: str,
+        summary: str,
+        keywords: str,
+        key_point: str,
+        start_time: float,
+        end_time: float,
+    ) -> str:
+        """创建一条新的 ChatHistory 概括记录（用于整理/合并后的新记忆）"""
+        try:
+            logger.info(
+                f"[dream][tool] 调用 create_chat_history("
+                f"theme={bool(theme)}, summary={bool(summary)}, "
+                f"keywords={bool(keywords)}, key_point={bool(key_point)}, "
+                f"start_time={start_time}, end_time={end_time}) (chat_id={chat_id})"
+            )
+
+            now_ts = time.time()
+
+            # 将传入的 start_time/end_time（如果有）解析为时间戳；否则回退为当前时间
+            def _parse_ts(value, default):
+                if value is None:
+                    return default
+                try:
+                    return float(value)
+                except (TypeError, ValueError):
+                    return default
+
+            start_ts = _parse_ts(start_time, now_ts)
+            end_ts = _parse_ts(end_time, now_ts)
+
+            record = ChatHistory.create(
+                chat_id=chat_id,
+                theme=theme,
+                summary=summary,
+                keywords=keywords,
+                key_point=key_point,
+                # 对于由 dream 整理产生的新概括，时间范围优先使用工具提供的时间，否则使用当前时间占位
+                start_time=start_ts,
+                end_time=end_ts,
+            )
+
+            msg = (
+                f"已创建新的 ChatHistory 记录，ID={record.id}，"
+                f"theme={record.theme or '无'}，summary={'有' if record.summary else '无'}。"
+            )
+            logger.info(f"[dream][tool] create_chat_history 完成: {msg}")
+            return msg
+        except Exception as e:
+            logger.error(f"create_chat_history 失败: {e}")
+            return f"create_chat_history 执行失败: {e}"
+
+    return create_chat_history
+
+
+
+

src/dream/tools/delete_chat_history_tool.py

@@ -0,0 +1,29 @@
+from src.common.logger import get_logger
+from src.common.database.database_model import ChatHistory
+
+logger = get_logger("dream_agent")
+
+
+def make_delete_chat_history(chat_id: str):  # chat_id 目前未直接使用，预留以备扩展
+    async def delete_chat_history(memory_id: int) -> str:
+        """删除一条 chat_history 记录"""
+        try:
+            logger.info(f"[dream][tool] 调用 delete_chat_history(memory_id={memory_id})")
+            record = ChatHistory.get_or_none(ChatHistory.id == memory_id)
+            if not record:
+                msg = f"未找到 ID={memory_id} 的 ChatHistory 记录，无法删除。"
+                logger.info(f"[dream][tool] delete_chat_history 未找到记录: {msg}")
+                return msg
+            rows = ChatHistory.delete().where(ChatHistory.id == memory_id).execute()
+            msg = f"已删除 ID={memory_id} 的 ChatHistory 记录，受影响行数={rows}。"
+            logger.info(f"[dream][tool] delete_chat_history 完成: {msg}")
+            return msg
+        except Exception as e:
+            logger.error(f"delete_chat_history 失败: {e}")
+            return f"delete_chat_history 执行失败: {e}"
+
+    return delete_chat_history
+
+
+
+

src/dream/tools/delete_jargon_tool.py

@@ -0,0 +1,29 @@
+from src.common.logger import get_logger
+from src.common.database.database_model import Jargon
+
+logger = get_logger("dream_agent")
+
+
+def make_delete_jargon(chat_id: str):  # chat_id 目前未直接使用，预留以备扩展
+    async def delete_jargon(jargon_id: int) -> str:
+        """删除一条 Jargon 记录"""
+        try:
+            logger.info(f"[dream][tool] 调用 delete_jargon(jargon_id={jargon_id})")
+            record = Jargon.get_or_none(Jargon.id == jargon_id)
+            if not record:
+                msg = f"未找到 ID={jargon_id} 的 Jargon 记录，无法删除。"
+                logger.info(f"[dream][tool] delete_jargon 未找到记录: {msg}")
+                return msg
+            rows = Jargon.delete().where(Jargon.id == jargon_id).execute()
+            msg = f"已删除 ID={jargon_id} 的 Jargon 记录（内容：{record.content}），受影响行数={rows}。"
+            logger.info(f"[dream][tool] delete_jargon 完成: {msg}")
+            return msg
+        except Exception as e:
+            logger.error(f"delete_jargon 失败: {e}")
+            return f"delete_jargon 执行失败: {e}"
+
+    return delete_jargon
+
+
+
+

src/dream/tools/finish_maintenance_tool.py

@@ -0,0 +1,20 @@
+from typing import Optional
+
+from src.common.logger import get_logger
+
+logger = get_logger("dream_agent")
+
+
+def make_finish_maintenance(chat_id: str):  # chat_id 目前未直接使用，预留以备扩展
+    async def finish_maintenance(reason: Optional[str] = None) -> str:
+        """结束本次 dream 维护任务。当你认为当前 chat_id 下的维护工作已经完成，没有更多需要整理的内容时，调用此工具来结束本次运行。"""
+        reason_text = f"，原因：{reason}" if reason else ""
+        msg = f"DREAM_MAINTENANCE_COMPLETE{reason_text}"
+        logger.info(f"[dream][tool] 调用 finish_maintenance，结束本次维护{reason_text}")
+        return msg
+
+    return finish_maintenance
+
+
+
+

src/dream/tools/get_chat_history_detail_tool.py

@@ -0,0 +1,55 @@
+import time
+from typing import Optional
+
+from src.common.logger import get_logger
+from src.common.database.database_model import ChatHistory
+
+logger = get_logger("dream_agent")
+
+
+def make_get_chat_history_detail(chat_id: str):  # chat_id 目前未直接使用，预留以备扩展
+    async def get_chat_history_detail(memory_id: int) -> str:
+        """获取单条 chat_history 的完整内容"""
+        try:
+            logger.info(f"[dream][tool] 调用 get_chat_history_detail(memory_id={memory_id})")
+            record = ChatHistory.get_or_none(ChatHistory.id == memory_id)
+            if not record:
+                msg = f"未找到 ID={memory_id} 的 ChatHistory 记录。"
+                logger.info(f"[dream][tool] get_chat_history_detail 未找到记录: {msg}")
+                return msg
+
+            # 将时间戳转换为可读时间格式
+            start_time_str = (
+                time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(record.start_time))
+                if record.start_time
+                else "未知"
+            )
+            end_time_str = (
+                time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(record.end_time))
+                if record.end_time
+                else "未知"
+            )
+
+            result = (
+                f"ID={record.id}\n"
+                # f"chat_id={record.chat_id}\n"
+                f"时间范围={start_time_str} 至 {end_time_str}\n"
+                f"主题={record.theme or '无'}\n"
+                f"关键词={record.keywords or '无'}\n"
+                f"参与者={record.participants or '无'}\n"
+                f"概括={record.summary or '无'}\n"
+                f"关键信息={record.key_point or '无'}"
+            )
+            logger.debug(
+                f"[dream][tool] get_chat_history_detail 成功，预览: {result[:200].replace(chr(10), ' ')}"
+            )
+            return result
+        except Exception as e:
+            logger.error(f"get_chat_history_detail 失败: {e}")
+            return f"get_chat_history_detail 执行失败: {e}"
+
+    return get_chat_history_detail
+
+
+
+

src/dream/tools/search_chat_history_tool.py

@@ -0,0 +1,226 @@
+import json
+from typing import List, Optional
+
+from src.common.logger import get_logger
+from src.common.database.database_model import ChatHistory
+from src.chat.utils.utils import parse_keywords_string
+
+logger = get_logger("dream_agent")
+
+
+def make_search_chat_history(chat_id: str):
+    async def search_chat_history(
+        keyword: Optional[str] = None,
+        participant: Optional[str] = None,
+    ) -> str:
+        """根据关键词或参与人查询记忆，返回匹配的记忆id、记忆标题theme和关键词keywords（dream 维护专用版本）"""
+        try:
+            # 检查参数
+            if not keyword and not participant:
+                return "未指定查询参数（需要提供keyword或participant之一）"
+
+            logger.info(
+                f"[dream][tool] 调用 search_chat_history(keyword={keyword}, participant={participant}) "
+                f"(作用域 chat_id={chat_id})"
+            )
+
+            # 构建查询条件
+            query = ChatHistory.select().where(ChatHistory.chat_id == chat_id)
+
+            # 执行查询（按时间倒序，最近的在前）
+            records = list(query.order_by(ChatHistory.start_time.desc()).limit(50))
+
+            filtered_records: List[ChatHistory] = []
+
+            for record in records:
+                participant_matched = True  # 如果没有participant条件，默认为True
+                keyword_matched = True  # 如果没有keyword条件，默认为True
+
+                # 检查参与人匹配
+                if participant:
+                    participant_matched = False
+                    participants_list: List[str] = []
+                    if record.participants:
+                        try:
+                            participants_data = (
+                                json.loads(record.participants)
+                                if isinstance(record.participants, str)
+                                else record.participants
+                            )
+                            if isinstance(participants_data, list):
+                                participants_list = [str(p).lower() for p in participants_data]
+                        except (json.JSONDecodeError, TypeError, ValueError):
+                            pass
+
+                    participant_lower = participant.lower().strip()
+                    if participant_lower and any(participant_lower in p for p in participants_list):
+                        participant_matched = True
+
+                # 检查关键词匹配
+                if keyword:
+                    keyword_matched = False
+                    # 解析多个关键词（支持空格、逗号等分隔符）
+                    keywords_list = parse_keywords_string(keyword)
+                    if not keywords_list:
+                        keywords_list = [keyword.strip()] if keyword.strip() else []
+
+                    # 转换为小写以便匹配
+                    keywords_lower = [kw.lower() for kw in keywords_list if kw.strip()]
+
+                    if keywords_lower:
+                        # 在theme、keywords、summary、original_text中搜索
+                        theme = (record.theme or "").lower()
+                        summary = (record.summary or "").lower()
+                        original_text = (record.original_text or "").lower()
+
+                        # 解析record中的keywords JSON
+                        record_keywords_list: List[str] = []
+                        if record.keywords:
+                            try:
+                                keywords_data = (
+                                    json.loads(record.keywords)
+                                    if isinstance(record.keywords, str)
+                                    else record.keywords
+                                )
+                                if isinstance(keywords_data, list):
+                                    record_keywords_list = [str(k).lower() for k in keywords_data]
+                            except (json.JSONDecodeError, TypeError, ValueError):
+                                pass
+
+                        # 有容错的全匹配：如果关键词数量>2，允许n-1个关键词匹配；否则必须全部匹配
+                        matched_count = 0
+                        for kw in keywords_lower:
+                            kw_matched = (
+                                kw in theme
+                                or kw in summary
+                                or kw in original_text
+                                or any(kw in k for k in record_keywords_list)
+                            )
+                            if kw_matched:
+                                matched_count += 1
+
+                        # 计算需要匹配的关键词数量
+                        total_keywords = len(keywords_lower)
+                        if total_keywords > 2:
+                            # 关键词数量>2，允许n-1个关键词匹配
+                            required_matches = total_keywords - 1
+                        else:
+                            # 关键词数量<=2，必须全部匹配
+                            required_matches = total_keywords
+
+                        keyword_matched = matched_count >= required_matches
+
+                # 两者都匹配（如果同时有participant和keyword，需要两者都匹配；如果只有一个条件，只需要该条件匹配）
+                matched = participant_matched and keyword_matched
+
+                if matched:
+                    filtered_records.append(record)
+
+            if not filtered_records:
+                if keyword and participant:
+                    keywords_str = "、".join(parse_keywords_string(keyword) if keyword else [])
+                    return f"未找到包含关键词'{keywords_str}'且参与人包含'{participant}'的聊天记录"
+                elif keyword:
+                    keywords_list = parse_keywords_string(keyword)
+                    keywords_str = "、".join(keywords_list)
+                    if len(keywords_list) > 2:
+                        required_count = len(keywords_list) - 1
+                        return (
+                            f"未找到包含至少{required_count}个关键词（共{len(keywords_list)}个）'{keywords_str}'的聊天记录"
+                        )
+                    else:
+                        return f"未找到包含所有关键词'{keywords_str}'的聊天记录"
+                elif participant:
+                    return f"未找到参与人包含'{participant}'的聊天记录"
+                else:
+                    return "未找到相关聊天记录"
+
+            # 如果匹配结果超过20条，不返回具体记录，只返回提示和所有相关关键词
+            if len(filtered_records) > 20:
+                all_keywords_set = set()
+                for record in filtered_records:
+                    if record.keywords:
+                        try:
+                            keywords_data = (
+                                json.loads(record.keywords)
+                                if isinstance(record.keywords, str)
+                                else record.keywords
+                            )
+                            if isinstance(keywords_data, list):
+                                for k in keywords_data:
+                                    k_str = str(k).strip()
+                                    if k_str:
+                                        all_keywords_set.add(k_str)
+                        except (json.JSONDecodeError, TypeError, ValueError):
+                            continue
+
+                search_label = keyword or participant or "当前条件"
+
+                if all_keywords_set:
+                    keywords_str = "、".join(sorted(all_keywords_set))
+                    response_text = (
+                        f"包含“{search_label}”的结果过多，请尝试更多关键词精确查找\n\n"
+                        f"有关\"{search_label}\"的关键词：\n"
+                        f"{keywords_str}"
+                    )
+                else:
+                    response_text = (
+                        f"包含“{search_label}”的结果过多，请尝试更多关键词精确查找\n\n"
+                        f"有关\"{search_label}\"的关键词信息为空"
+                    )
+
+                logger.info(
+                    f"[dream][tool] search_chat_history 匹配结果超过20条，返回关键词汇总提示，总数={len(filtered_records)}"
+                )
+                return response_text
+
+            # 构建结果文本，返回id、theme和keywords（最多20条）
+            results: List[str] = []
+            for record in filtered_records[:20]:
+                result_parts: List[str] = []
+
+                # 记忆ID
+                result_parts.append(f"记忆ID：{record.id}")
+
+                # 主题
+                if record.theme:
+                    result_parts.append(f"主题：{record.theme}")
+                else:
+                    result_parts.append("主题：（无）")
+
+                # 关键词
+                if record.keywords:
+                    try:
+                        keywords_data = (
+                            json.loads(record.keywords)
+                            if isinstance(record.keywords, str)
+                            else record.keywords
+                        )
+                        if isinstance(keywords_data, list) and keywords_data:
+                            keywords_str = "、".join([str(k) for k in keywords_data])
+                            result_parts.append(f"关键词：{keywords_str}")
+                        else:
+                            result_parts.append("关键词：（无）")
+                    except (json.JSONDecodeError, TypeError, ValueError):
+                        result_parts.append("关键词：（无）")
+                else:
+                    result_parts.append("关键词：（无）")
+
+                results.append("\n".join(result_parts))
+
+            if not results:
+                return "未找到相关聊天记录"
+
+            response_text = "\n\n---\n\n".join(results)
+
+            logger.info(f"[dream][tool] search_chat_history 返回 {len(filtered_records)} 条匹配记录")
+            return response_text
+        except Exception as e:
+            logger.error(f"search_chat_history 失败: {e}")
+            return f"search_chat_history 执行失败: {e}"
+
+    return search_chat_history
+
+
+
+

src/dream/tools/search_jargon_tool.py

@@ -0,0 +1,106 @@
+from typing import List
+
+from src.common.logger import get_logger
+from src.common.database.database_model import Jargon
+from src.config.config import global_config
+from src.chat.utils.utils import parse_keywords_string
+from src.jargon.jargon_utils import parse_chat_id_list, chat_id_list_contains
+
+logger = get_logger("dream_agent")
+
+
+def make_search_jargon(chat_id: str):
+    async def search_jargon(keyword: str) -> str:
+        """根据一个或多个关键词搜索当前 chat_id 相关的 Jargon 记录概览（只包含 is_jargon=True，是否跨 chat_id 由 all_global 决定）"""
+        try:
+            if not keyword or not keyword.strip():
+                return "未指定查询关键词（参数 keyword 为必填，且不能为空）"
+
+            logger.info(
+                f"[dream][tool] 调用 search_jargon(keyword={keyword}) (作用域 chat_id={chat_id})"
+            )
+
+            # 基础条件：只查 is_jargon=True 的记录
+            query = Jargon.select().where(Jargon.is_jargon)
+
+            # 根据 all_global 配置决定 chat_id 作用域
+            if global_config.jargon.all_global:
+                # 开启全局黑话：只看 is_global=True 的记录，不区分 chat_id
+                query = query.where(Jargon.is_global)
+            else:
+                # 关闭全局黑话：后续在 Python 层按 chat_id 列表过滤（包含 is_global=True）
+                pass
+
+            # 先按使用次数排序取一批候选，做一个安全上限
+            query = query.order_by(Jargon.count.desc()).limit(200)
+            candidates = list(query)
+
+            if not candidates:
+                msg = "未找到符合条件的 Jargon 记录。"
+                logger.info(f"[dream][tool] search_jargon 无记录: {msg}")
+                return msg
+
+            # 关键词为必填，因此此处必然执行关键词过滤（支持多个关键词，大小写不敏感）
+            keywords_list = parse_keywords_string(keyword) or []
+            if not keywords_list and keyword.strip():
+                keywords_list = [keyword.strip()]
+            keywords_lower = [kw.lower() for kw in keywords_list if kw.strip()]
+
+            # 先按关键词过滤（仅对 content 字段进行匹配）
+            filtered_keyword: List[Jargon] = []
+            for r in candidates:
+                content = (r.content or "").lower()
+
+                # 只要命中任意一个关键词即可视为匹配（OR 逻辑）
+                any_matched = False
+                for kw in keywords_lower:
+                    if not kw:
+                        continue
+                    if kw in content:
+                        any_matched = True
+                        break
+
+                if any_matched:
+                    filtered_keyword.append(r)
+
+            if global_config.jargon.all_global:
+                # 全局黑话模式：不再做 chat_id 过滤，直接使用关键词过滤结果
+                records = filtered_keyword
+            else:
+                # 非全局模式：仅保留全局黑话或 chat_id 列表中包含当前 chat_id 的记录
+                records = []
+                for r in filtered_keyword:
+                    if r.is_global:
+                        records.append(r)
+                        continue
+                    chat_id_list = parse_chat_id_list(r.chat_id)
+                    if chat_id_list_contains(chat_id_list, chat_id):
+                        records.append(r)
+
+            if not records:
+                scope_note = (
+                    "（当前为全局黑话模式，仅统计 is_global=True 的条目）"
+                    if global_config.jargon.all_global
+                    else "（当前为按 chat_id 作用域模式，仅统计全局黑话或与当前 chat_id 相关的条目）"
+                )
+                return f"未找到包含关键词'{keyword}'的 Jargon 记录{scope_note}"
+
+            lines: List[str] = []
+            for r in records:
+                is_jargon_str = "是" if r.is_jargon else "否" if r.is_jargon is False else "未判定"
+                is_global_str = "全局" if r.is_global else "非全局"
+                lines.append(
+                    f"ID={r.id} | 内容={r.content} | 含义={r.meaning or '无'} | "
+                    f"chat_id={r.chat_id} | {is_global_str} | 是否黑话={is_jargon_str}"
+                )
+
+            result = "\n".join(lines)
+            logger.info(f"[dream][tool] search_jargon 返回 {len(records)} 条记录")
+            return result
+        except Exception as e:
+            logger.error(f"search_jargon 失败: {e}")
+            return f"search_jargon 执行失败: {e}"
+
+    return search_jargon
+
+

src/dream/tools/update_chat_history_tool.py

@@ -0,0 +1,55 @@
+from typing import Any, Dict, Optional
+
+from src.common.logger import get_logger
+from src.common.database.database_model import ChatHistory
+from src.plugin_system.apis import database_api
+
+logger = get_logger("dream_agent")
+
+
+def make_update_chat_history(chat_id: str):  # chat_id 目前未直接使用，预留以备扩展
+    async def update_chat_history(
+        memory_id: int,
+        theme: Optional[str] = None,
+        summary: Optional[str] = None,
+        keywords: Optional[str] = None,
+        key_point: Optional[str] = None,
+    ) -> str:
+        """按字段更新 chat_history（字符串字段要求 JSON 的字段须传入已序列化的字符串）"""
+        try:
+            logger.info(
+                f"[dream][tool] 调用 update_chat_history(memory_id={memory_id}, "
+                f"theme={bool(theme)}, summary={bool(summary)}, keywords={bool(keywords)}, key_point={bool(key_point)})"
+            )
+            record = ChatHistory.get_or_none(ChatHistory.id == memory_id)
+            if not record:
+                msg = f"未找到 ID={memory_id} 的 ChatHistory 记录，无法更新。"
+                logger.info(f"[dream][tool] update_chat_history 未找到记录: {msg}")
+                return msg
+
+            data: Dict[str, Any] = {}
+            if theme is not None:
+                data["theme"] = theme
+            if summary is not None:
+                data["summary"] = summary
+            if keywords is not None:
+                data["keywords"] = keywords
+            if key_point is not None:
+                data["key_point"] = key_point
+
+            if not data:
+                return "未提供任何需要更新的字段。"
+
+            await database_api.db_save(ChatHistory, data=data, key_field="id", key_value=memory_id)
+            msg = f"已更新 ChatHistory 记录 ID={memory_id}，更新字段={list(data.keys())}。"
+            logger.info(f"[dream][tool] update_chat_history 完成: {msg}")
+            return msg
+        except Exception as e:
+            logger.error(f"update_chat_history 失败: {e}")
+            return f"update_chat_history 执行失败: {e}"
+
+    return update_chat_history
+
+
+
+

src/dream/tools/update_jargon_tool.py

@@ -0,0 +1,55 @@
+from typing import Any, Dict, Optional
+
+from src.common.logger import get_logger
+from src.common.database.database_model import Jargon
+from src.plugin_system.apis import database_api
+
+logger = get_logger("dream_agent")
+
+
+def make_update_jargon(chat_id: str):  # chat_id 目前未直接使用，预留以备扩展
+    async def update_jargon(
+        jargon_id: int,
+        meaning: Optional[str] = None,
+        is_global: Optional[bool] = None,
+        is_jargon: Optional[bool] = None,
+        content: Optional[str] = None,
+    ) -> str:
+        """按字段更新 Jargon 记录，可用于修正含义、调整全局性、标记是否为黑话等"""
+        try:
+            logger.info(
+                f"[dream][tool] 调用 update_jargon(jargon_id={jargon_id}, "
+                f"meaning={bool(meaning)}, is_global={is_global}, is_jargon={is_jargon}, content={bool(content)})"
+            )
+            record = Jargon.get_or_none(Jargon.id == jargon_id)
+            if not record:
+                msg = f"未找到 ID={jargon_id} 的 Jargon 记录，无法更新。"
+                logger.info(f"[dream][tool] update_jargon 未找到记录: {msg}")
+                return msg
+
+            data: Dict[str, Any] = {}
+            if meaning is not None:
+                data["meaning"] = meaning
+            if is_global is not None:
+                data["is_global"] = is_global
+            if is_jargon is not None:
+                data["is_jargon"] = is_jargon
+            if content is not None:
+                data["content"] = content
+
+            if not data:
+                return "未提供任何需要更新的字段。"
+
+            await database_api.db_save(Jargon, data=data, key_field="id", key_value=jargon_id)
+            msg = f"已更新 Jargon 记录 ID={jargon_id}，更新字段={list(data.keys())}。"
+            logger.info(f"[dream][tool] update_jargon 完成: {msg}")
+            return msg
+        except Exception as e:
+            logger.error(f"update_jargon 失败: {e}")
+            return f"update_jargon 执行失败: {e}"
+
+    return update_jargon
+
+
+
+

src/express/expression_learner.py

@@ -12,11 +12,10 @@ from src.config.config import model_config, global_config
 from src.chat.utils.chat_message_builder import (
     get_raw_msg_by_timestamp_with_chat_inclusive,
     build_anonymous_messages,
-    build_bare_messages,
 )
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.message_receive.chat_stream import get_chat_manager
-from src.express.express_utils import filter_message_content, calculate_similarity
+from src.express.express_utils import filter_message_content
 from json_repair import repair_json
 
 
@@ -26,10 +25,10 @@ logger = get_logger("expressor")
 
 
 def init_prompt() -> None:
-    learn_style_prompt = """
-{chat_str}
+    learn_style_prompt = """{chat_str}
 
-请从上面这段群聊中概括除了人名为"SELF"之外的人的语言风格
+请从上面这段群聊中概括除了人名为"SELF"之外的人的语言风格。
+每一行消息前面的方括号中的数字（如 [1]、[2]）是该行消息的唯一编号，请在输出中引用这些编号来标注“表达方式的来源行”。
 1. 只考虑文字，不要考虑表情包和图片
 2. 不要涉及具体的人名，但是可以涉及具体名词
 3. 思考有没有特殊的梗，一并总结成语言风格
@@ -37,41 +36,29 @@ def init_prompt() -> None:
 注意：总结成如下格式的规律，总结的内容要详细，但具有概括性：
 例如：当"AAAAA"时，可以"BBBBB", AAAAA代表某个具体的场景，不超过20个字。BBBBB代表对应的语言风格，特定句式或表达方式，不超过20个字。
 
-例如：
-当"对某件事表示十分惊叹"时，使用"我嘞个xxxx"
-当"表示讽刺的赞同，不讲道理"时，使用"对对对"
-当"想说明某个具体的事实观点，但懒得明说，使用"懂的都懂"
-当"当涉及游戏相关时，夸赞，略带戏谑意味"时，使用"这么强！"
+请严格以 JSON 数组的形式输出结果，每个元素为一个对象，结构如下（注意字段名）：
+[
+  {{"situation": "AAAAA", "style": "BBBBB", "source_id": "3"}},
+  {{"situation": "CCCC", "style": "DDDD", "source_id": "7"}}
+  {{"situation": "对某件事表示十分惊叹", "style": "使用 我嘞个xxxx", "source_id": "[消息编号]"}},
+  {{"situation": "表示讽刺的赞同，不讲道理", "style": "对对对", "source_id": "[消息编号]"}},
+  {{"situation": "当涉及游戏相关时，夸赞，略带戏谑意味", "style": "使用 这么强！", "source_id": "[消息编号]"}},
+]
 
-请注意：不要总结你自己（SELF）的发言，尽量保证总结内容的逻辑性
-现在请你概括
+请注意：
+- 不要总结你自己（SELF）的发言，尽量保证总结内容的逻辑性
+- 请只针对最重要的若干条表达方式进行总结，避免输出太多重复或相似的条目
+
+其中：
+- situation：表示“在什么情境下”的简短概括（不超过20个字）
+- style：表示对应的语言风格或常用表达（不超过20个字）
+- source_id：该表达方式对应的“来源行编号”，即上方聊天记录中方括号里的数字（例如 [3]），请只输出数字本身，不要包含方括号
+
+现在请你输出 JSON：
 """
     Prompt(learn_style_prompt, "learn_style_prompt")
 
-    match_expression_context_prompt = """
-**聊天内容**
-{chat_str}
 
-**从聊天内容总结的表达方式pairs**
-{expression_pairs}
-
-请你为上面的每一条表达方式，找到该表达方式的原文句子，并输出匹配结果，expression_pair不能有重复，每个expression_pair仅输出一个最合适的context。
-如果找不到原句，就不输出该句的匹配结果。
-以json格式输出：
-格式如下：
-{{
-    "expression_pair": "表达方式pair的序号（数字）",
-    "context": "与表达方式对应的原文句子的原始内容，不要修改原文句子的内容",
-}}，
-{{
-    "expression_pair": "表达方式pair的序号（数字）",
-    "context": "与表达方式对应的原文句子的原始内容，不要修改原文句子的内容",
-}}，
-...
-
-现在请你输出匹配结果：
-"""
-    Prompt(match_expression_context_prompt, "match_expression_context_prompt")
 
 
 class ExpressionLearner:
@@ -99,6 +86,10 @@ class ExpressionLearner:
         _, self.enable_learning, self.learning_intensity = global_config.expression.get_expression_config_for_chat(
             self.chat_id
         )
+        # 防止除以零：如果学习强度为0或负数，使用最小值0.0001
+        if self.learning_intensity <= 0:
+            logger.warning(f"学习强度为 {self.learning_intensity}，已自动调整为 0.0001 以避免除以零错误")
+            self.learning_intensity = 0.0000001
         self.min_messages_for_learning = 15 / self.learning_intensity  # 触发学习所需的最少消息数
         self.min_learning_interval = 120 / self.learning_intensity
 
@@ -193,7 +184,6 @@ class ExpressionLearner:
             situation,
             style,
             _context,
-            _up_content,
         ) in learnt_expressions:
             learnt_expressions_str += f"{situation}->{style}\n"
         logger.info(f"在 {self.chat_name} 学习到表达风格:\n{learnt_expressions_str}")
@@ -205,193 +195,17 @@ class ExpressionLearner:
             situation,
             style,
             context,
-            up_content,
         ) in learnt_expressions:
             await self._upsert_expression_record(
                 situation=situation,
                 style=style,
                 context=context,
-                up_content=up_content,
                 current_time=current_time,
             )
 
         return learnt_expressions
 
-    async def match_expression_context(
-        self, expression_pairs: List[Tuple[str, str]], random_msg_match_str: str
-    ) -> List[Tuple[str, str, str]]:
-        # 为expression_pairs逐个条目赋予编号，并构建成字符串
-        numbered_pairs = []
-        for i, (situation, style) in enumerate(expression_pairs, 1):
-            numbered_pairs.append(f'{i}. 当"{situation}"时，使用"{style}"')
-
-        expression_pairs_str = "\n".join(numbered_pairs)
-
-        prompt = "match_expression_context_prompt"
-        prompt = await global_prompt_manager.format_prompt(
-            prompt,
-            expression_pairs=expression_pairs_str,
-            chat_str=random_msg_match_str,
-        )
-
-        response, _ = await self.express_learn_model.generate_response_async(prompt, temperature=0.3)
-
-        # print(f"match_expression_context_prompt: {prompt}")
-        # print(f"{response}")
-
-        # 解析JSON响应
-        match_responses = []
-        try:
-            response = response.strip()
-
-            # 尝试提取JSON代码块（如果存在）
-            json_pattern = r"```json\s*(.*?)\s*```"
-            matches = re.findall(json_pattern, response, re.DOTALL)
-            if matches:
-                response = matches[0].strip()
-
-            # 移除可能的markdown代码块标记（如果没有找到```json，但可能有```）
-            if not matches:
-                response = re.sub(r"^```\s*", "", response, flags=re.MULTILINE)
-                response = re.sub(r"```\s*$", "", response, flags=re.MULTILINE)
-                response = response.strip()
-
-            # 检查是否已经是标准JSON数组格式
-            if response.startswith("[") and response.endswith("]"):
-                match_responses = json.loads(response)
-            else:
-                # 尝试直接解析多个JSON对象
-                try:
-                    # 如果是多个JSON对象用逗号分隔，包装成数组
-                    if response.startswith("{") and not response.startswith("["):
-                        response = "[" + response + "]"
-                        match_responses = json.loads(response)
-                    else:
-                        # 使用repair_json处理响应
-                        repaired_content = repair_json(response)
-
-                        # 确保repaired_content是列表格式
-                        if isinstance(repaired_content, str):
-                            try:
-                                parsed_data = json.loads(repaired_content)
-                                if isinstance(parsed_data, dict):
-                                    # 如果是字典，包装成列表
-                                    match_responses = [parsed_data]
-                                elif isinstance(parsed_data, list):
-                                    match_responses = parsed_data
-                                else:
-                                    match_responses = []
-                            except json.JSONDecodeError:
-                                match_responses = []
-                        elif isinstance(repaired_content, dict):
-                            # 如果是字典，包装成列表
-                            match_responses = [repaired_content]
-                        elif isinstance(repaired_content, list):
-                            match_responses = repaired_content
-                        else:
-                            match_responses = []
-                except json.JSONDecodeError:
-                    # 如果还是失败，尝试repair_json
-                    repaired_content = repair_json(response)
-                    if isinstance(repaired_content, str):
-                        parsed_data = json.loads(repaired_content)
-                        match_responses = parsed_data if isinstance(parsed_data, list) else [parsed_data]
-                    else:
-                        match_responses = repaired_content if isinstance(repaired_content, list) else [repaired_content]
-
-        except (json.JSONDecodeError, Exception) as e:
-            logger.error(f"解析匹配响应JSON失败: {e}, 响应内容: \n{response}")
-            return []
-
-        # 确保 match_responses 是一个列表
-        if not isinstance(match_responses, list):
-            if isinstance(match_responses, dict):
-                match_responses = [match_responses]
-            else:
-                logger.error(f"match_responses 不是列表或字典类型: {type(match_responses)}, 内容: {match_responses}")
-                return []
-
-        # 清理和规范化 match_responses 中的元素
-        normalized_responses = []
-        for item in match_responses:
-            if isinstance(item, dict):
-                # 已经是字典，直接添加
-                normalized_responses.append(item)
-            elif isinstance(item, str):
-                # 如果是字符串，尝试解析为 JSON
-                try:
-                    parsed = json.loads(item)
-                    if isinstance(parsed, dict):
-                        normalized_responses.append(parsed)
-                    elif isinstance(parsed, list):
-                        # 如果是列表，递归处理
-                        for sub_item in parsed:
-                            if isinstance(sub_item, dict):
-                                normalized_responses.append(sub_item)
-                            else:
-                                logger.debug(f"跳过非字典类型的子元素: {type(sub_item)}, 内容: {sub_item}")
-                    else:
-                        logger.debug(f"跳过无法转换为字典的字符串元素: {item}")
-                except (json.JSONDecodeError, TypeError):
-                    logger.debug(f"跳过无法解析为JSON的字符串元素: {item}")
-            elif isinstance(item, list):
-                # 如果是列表，展开并处理其中的字典
-                for sub_item in item:
-                    if isinstance(sub_item, dict):
-                        normalized_responses.append(sub_item)
-                    elif isinstance(sub_item, str):
-                        # 尝试解析字符串
-                        try:
-                            parsed = json.loads(sub_item)
-                            if isinstance(parsed, dict):
-                                normalized_responses.append(parsed)
-                            else:
-                                logger.debug(f"跳过非字典类型的解析结果: {type(parsed)}, 内容: {parsed}")
-                        except (json.JSONDecodeError, TypeError):
-                            logger.debug(f"跳过无法解析为JSON的字符串子元素: {sub_item}")
-                    else:
-                        logger.debug(f"跳过非字典类型的列表元素: {type(sub_item)}, 内容: {sub_item}")
-            else:
-                logger.debug(f"跳过无法处理的元素类型: {type(item)}, 内容: {item}")
-
-        match_responses = normalized_responses
-
-        matched_expressions = []
-        used_pair_indices = set()  # 用于跟踪已经使用的expression_pair索引
-
-        logger.debug(f"规范化后的 match_responses 类型: {type(match_responses)}, 长度: {len(match_responses)}")
-        logger.debug(f"规范化后的 match_responses 内容: {match_responses}")
-
-        for match_response in match_responses:
-            try:
-                # 检查 match_response 的类型（此时应该都是字典）
-                if not isinstance(match_response, dict):
-                    logger.error(f"match_response 不是字典类型: {type(match_response)}, 内容: {match_response}")
-                    continue
-
-                # 获取表达方式序号
-                if "expression_pair" not in match_response:
-                    logger.error(f"match_response 缺少 'expression_pair' 字段: {match_response}")
-                    continue
-
-                pair_index = int(match_response["expression_pair"]) - 1  # 转换为0-based索引
-
-                # 检查索引是否有效且未被使用过
-                if 0 <= pair_index < len(expression_pairs) and pair_index not in used_pair_indices:
-                    situation, style = expression_pairs[pair_index]
-                    context = match_response.get("context", "")
-                    matched_expressions.append((situation, style, context))
-                    used_pair_indices.add(pair_index)  # 标记该索引已使用
-                    logger.debug(f"成功匹配表达方式 {pair_index + 1}: {situation} -> {style}")
-                elif pair_index in used_pair_indices:
-                    logger.debug(f"跳过重复的表达方式 {pair_index + 1}")
-            except (ValueError, KeyError, IndexError, TypeError) as e:
-                logger.error(f"解析匹配条目失败: {e}, 条目: {match_response}")
-                continue
-
-        return matched_expressions
-
-    async def learn_expression(self, num: int = 10, timestamp_start: Optional[float] = None) -> Optional[List[Tuple[str, str, str, str]]]:
+    async def learn_expression(self, num: int = 10, timestamp_start: Optional[float] = None) -> Optional[List[Tuple[str, str, str]]]:
         """从指定聊天流学习表达方式
 
         Args:
@@ -414,10 +228,8 @@ class ExpressionLearner:
         if not random_msg or random_msg == []:
             return None
 
-        # 学习用
-        random_msg_str: str = await build_anonymous_messages(random_msg)
-        # 溯源用
-        random_msg_match_str: str = await build_bare_messages(random_msg)
+        # 学习用（开启行编号，便于溯源）
+        random_msg_str: str = await build_anonymous_messages(random_msg, show_ids=True)
 
         prompt: str = await global_prompt_manager.format_prompt(
             "learn_style_prompt",
@@ -432,83 +244,107 @@ class ExpressionLearner:
         except Exception as e:
             logger.error(f"学习表达方式失败,模型生成出错: {e}")
             return None
-        expressions: List[Tuple[str, str]] = self.parse_expression_response(response)
+
+        # 解析 LLM 返回的表达方式列表（包含来源行编号）
+        expressions: List[Tuple[str, str, str]] = self.parse_expression_response(response)
         expressions = self._filter_self_reference_styles(expressions)
         if not expressions:
             logger.info("过滤后没有可用的表达方式（style 与机器人名称重复）")
             return None
         # logger.debug(f"学习{type_str}的response: {response}")
 
-        # 对表达方式溯源
-        matched_expressions: List[Tuple[str, str, str]] = await self.match_expression_context(
-            expressions, random_msg_match_str
-        )
-        # 为每条消息构建精简文本列表，保留到原消息索引的映射
-        bare_lines: List[Tuple[int, str]] = self._build_bare_lines(random_msg)
-        # 将 matched_expressions 结合上一句 up_content（若不存在上一句则跳过）
-        filtered_with_up: List[Tuple[str, str, str, str]] = []  # (situation, style, context, up_content)
-        for situation, style, context in matched_expressions:
-            # 在 bare_lines 中找到第一处相似度达到85%的行
-            pos = None
-            for i, (_, c) in enumerate(bare_lines):
-                similarity = calculate_similarity(c, context)
-                if similarity >= 0.85:  # 85%相似度阈值
-                    pos = i
-                    break
+        # 直接根据 source_id 在 random_msg 中溯源，获取 context
+        filtered_expressions: List[Tuple[str, str, str]] = []  # (situation, style, context)
 
-            if pos is None or pos == 0:
-                # 没有匹配到目标句或没有上一句，跳过该表达
+        for situation, style, source_id in expressions:
+            source_id_str = (source_id or "").strip()
+            if not source_id_str.isdigit():
+                # 无效的来源行编号，跳过
                 continue
 
-            # 检查目标句是否为空
-            target_content = bare_lines[pos][1]
-            if not target_content:
-                # 目标句为空，跳过该表达
+            line_index = int(source_id_str) - 1  # build_anonymous_messages 的编号从 1 开始
+            if line_index < 0 or line_index >= len(random_msg):
+                # 超出范围，跳过
                 continue
 
-            prev_original_idx = bare_lines[pos - 1][0]
-            up_content = filter_message_content(random_msg[prev_original_idx].processed_plain_text or "")
-            if not up_content:
-                # 上一句为空，跳过该表达
+            # 当前行的原始内容
+            current_msg = random_msg[line_index]
+            context = filter_message_content(current_msg.processed_plain_text or "")
+            if not context:
                 continue
-            filtered_with_up.append((situation, style, context, up_content))
 
-        if not filtered_with_up:
+            filtered_expressions.append((situation, style, context))
+
+        if not filtered_expressions:
             return None
 
-        return filtered_with_up
+        return filtered_expressions
 
     def parse_expression_response(self, response: str) -> List[Tuple[str, str, str]]:
         """
-        解析LLM返回的表达风格总结，每一行提取"当"和"使用"之间的内容，存储为(situation, style)元组
+        解析 LLM 返回的表达风格总结 JSON，提取 (situation, style, source_id) 元组列表。
+
+        期望的 JSON 结构：
+        [
+          {"situation": "AAAAA", "style": "BBBBB", "source_id": "3"},
+          ...
+        ]
         """
+        if not response:
+            return []
+
+        raw = response.strip()
+
+        # 尝试提取 ```json 代码块
+        json_block_pattern = r"```json\s*(.*?)\s*```"
+        match = re.search(json_block_pattern, raw, re.DOTALL)
+        if match:
+            raw = match.group(1).strip()
+        else:
+            # 去掉可能存在的通用 ``` 包裹
+            raw = re.sub(r"^```\s*", "", raw, flags=re.MULTILINE)
+            raw = re.sub(r"```\s*$", "", raw, flags=re.MULTILINE)
+            raw = raw.strip()
+
+        parsed = None
         expressions: List[Tuple[str, str, str]] = []
-        for line in response.splitlines():
-            line = line.strip()
-            if not line:
+
+        try:
+            # 优先尝试直接解析
+            if raw.startswith("[") and raw.endswith("]"):
+                parsed = json.loads(raw)
+            else:
+                repaired = repair_json(raw)
+                if isinstance(repaired, str):
+                    parsed = json.loads(repaired)
+                else:
+                    parsed = repaired
+        except Exception:
+            logger.error(f"解析表达风格 JSON 失败，原始响应：{response}")
+            return []
+
+        if isinstance(parsed, dict):
+            parsed_list = [parsed]
+        elif isinstance(parsed, list):
+            parsed_list = parsed
+        else:
+            logger.error(f"表达风格解析结果类型异常: {type(parsed)}, 内容: {parsed}")
+            return []
+
+        for item in parsed_list:
+            if not isinstance(item, dict):
                 continue
-            # 查找"当"和下一个引号
-            idx_when = line.find('当"')
-            if idx_when == -1:
+            situation = str(item.get("situation", "")).strip()
+            style = str(item.get("style", "")).strip()
+            source_id = str(item.get("source_id", "")).strip()
+            if not situation or not style or not source_id:
+                # 三个字段必须同时存在
                 continue
-            idx_quote1 = idx_when + 1
-            idx_quote2 = line.find('"', idx_quote1 + 1)
-            if idx_quote2 == -1:
-                continue
-            situation = line[idx_quote1 + 1 : idx_quote2]
-            # 查找"使用"
-            idx_use = line.find('使用"', idx_quote2)
-            if idx_use == -1:
-                continue
-            idx_quote3 = idx_use + 2
-            idx_quote4 = line.find('"', idx_quote3 + 1)
-            if idx_quote4 == -1:
-                continue
-            style = line[idx_quote3 + 1 : idx_quote4]
-            expressions.append((situation, style))
+            expressions.append((situation, style, source_id))
+
         return expressions
 
-    def _filter_self_reference_styles(self, expressions: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+    def _filter_self_reference_styles(self, expressions: List[Tuple[str, str, str]]) -> List[Tuple[str, str, str]]:
         """
         过滤掉style与机器人名称/昵称重复的表达
         """
@@ -525,12 +361,12 @@ class ExpressionLearner:
 
         banned_casefold = {name.casefold() for name in banned_names if name}
 
-        filtered: List[Tuple[str, str]] = []
+        filtered: List[Tuple[str, str, str]] = []
         removed_count = 0
-        for situation, style in expressions:
+        for situation, style, source_id in expressions:
             normalized_style = (style or "").strip()
             if normalized_style and normalized_style.casefold() not in banned_casefold:
-                filtered.append((situation, style))
+                filtered.append((situation, style, source_id))
             else:
                 removed_count += 1
 
@@ -544,7 +380,6 @@ class ExpressionLearner:
         situation: str,
         style: str,
         context: str,
-        up_content: str,
         current_time: float,
     ) -> None:
         expr_obj = Expression.select().where((Expression.chat_id == self.chat_id) & (Expression.style == style)).first()
@@ -554,7 +389,6 @@ class ExpressionLearner:
                 expr_obj=expr_obj,
                 situation=situation,
                 context=context,
-                up_content=up_content,
                 current_time=current_time,
             )
             return
@@ -563,7 +397,6 @@ class ExpressionLearner:
             situation=situation,
             style=style,
             context=context,
-            up_content=up_content,
             current_time=current_time,
         )
 
@@ -572,7 +405,6 @@ class ExpressionLearner:
         situation: str,
         style: str,
         context: str,
-        up_content: str,
         current_time: float,
     ) -> None:
         content_list = [situation]
@@ -587,7 +419,6 @@ class ExpressionLearner:
             chat_id=self.chat_id,
             create_date=current_time,
             context=context,
-            up_content=up_content,
         )
 
     async def _update_existing_expression(
@@ -595,7 +426,6 @@ class ExpressionLearner:
         expr_obj: Expression,
         situation: str,
         context: str,
-        up_content: str,
         current_time: float,
     ) -> None:
         content_list = self._parse_content_list(expr_obj.content_list)
@@ -605,7 +435,6 @@ class ExpressionLearner:
         expr_obj.count = (expr_obj.count or 0) + 1
         expr_obj.last_active_time = current_time
         expr_obj.context = context
-        expr_obj.up_content = up_content
 
         new_situation = await self._compose_situation_text(
             content_list=content_list,
@@ -651,27 +480,6 @@ class ExpressionLearner:
             logger.error(f"概括表达情境失败: {e}")
         return None
 
-    def _build_bare_lines(self, messages: List) -> List[Tuple[int, str]]:
-        """
-        为每条消息构建精简文本列表，保留到原消息索引的映射
-
-        Args:
-            messages: 消息列表
-
-        Returns:
-            List[Tuple[int, str]]: (original_index, bare_content) 元组列表
-        """
-        bare_lines: List[Tuple[int, str]] = []
-
-        for idx, msg in enumerate(messages):
-            content = msg.processed_plain_text or ""
-            content = filter_message_content(content)
-            # 即使content为空也要记录，防止错位
-            bare_lines.append((idx, content))
-
-        return bare_lines
-
-
 init_prompt()
 
 

src/hippo_memorizer/chat_history_summarizer.py

@@ -429,15 +429,36 @@ class ChatHistorySummarizer:
         # 2. 构造编号后的消息字符串和参与者信息
         numbered_lines, index_to_msg_str, index_to_msg_text, index_to_participants = self._build_numbered_messages_for_llm(messages)
 
-        # 3. 调用 LLM 识别话题，并得到 topic -> indices
+        # 3. 调用 LLM 识别话题，并得到 topic -> indices（失败时最多重试 3 次）
         existing_topics = list(self.topic_cache.keys())
-        success, topic_to_indices = await self._analyze_topics_with_llm(
-            numbered_lines=numbered_lines,
-            existing_topics=existing_topics,
-        )
+        max_retries = 3
+        attempt = 0
+        success = False
+        topic_to_indices: Dict[str, List[int]] = {}
+
+        while attempt < max_retries:
+            attempt += 1
+            success, topic_to_indices = await self._analyze_topics_with_llm(
+                numbered_lines=numbered_lines,
+                existing_topics=existing_topics,
+            )
+
+            if success and topic_to_indices:
+                if attempt > 1:
+                    logger.info(
+                        f"{self.log_prefix} 话题识别在第 {attempt} 次重试后成功 | 话题数: {len(topic_to_indices)}"
+                    )
+                break
+
+            logger.warning(
+                f"{self.log_prefix} 话题识别失败或无有效话题，第 {attempt} 次尝试失败"
+                + ("" if attempt >= max_retries else "，准备重试")
+            )
 
         if not success or not topic_to_indices:
-            logger.warning(f"{self.log_prefix} 话题识别失败或无有效话题，本次检查忽略")
+            logger.error(
+                f"{self.log_prefix} 话题识别连续 {max_retries} 次失败或始终无有效话题，本次检查放弃"
+            )
             # 即使识别失败，也认为是一次“检查”，但不更新 no_update_checks（保持原状）
             return
 

src/hippo_memorizer/memory_forget_task.py

@@ -28,10 +28,10 @@ class MemoryForgetTask(AsyncTask):
             # logger.info("[记忆遗忘] 开始遗忘检查...")
 
             # 执行4个阶段的遗忘检查
-            await self._forget_stage_1(current_time)
-            await self._forget_stage_2(current_time)
-            await self._forget_stage_3(current_time)
-            await self._forget_stage_4(current_time)
+            # await self._forget_stage_1(current_time)
+            # await self._forget_stage_2(current_time)
+            # await self._forget_stage_3(current_time)
+            # await self._forget_stage_4(current_time)
 
             # logger.info("[记忆遗忘] 遗忘检查完成")
         except Exception as e:

src/llm_models/utils_model.py

@@ -315,12 +315,30 @@ class LLMRequest:
         while retry_remain > 0:
             try:
                 if request_type == RequestType.RESPONSE:
+                    # 温度优先级：参数传入 > 模型级别配置 > extra_params > 任务配置
+                    effective_temperature = temperature
+                    if effective_temperature is None:
+                        effective_temperature = model_info.temperature
+                    if effective_temperature is None:
+                        effective_temperature = (model_info.extra_params or {}).get("temperature")
+                    if effective_temperature is None:
+                        effective_temperature = self.model_for_task.temperature
+                    
+                    # max_tokens 优先级：参数传入 > 模型级别配置 > extra_params > 任务配置
+                    effective_max_tokens = max_tokens
+                    if effective_max_tokens is None:
+                        effective_max_tokens = model_info.max_tokens
+                    if effective_max_tokens is None:
+                        effective_max_tokens = (model_info.extra_params or {}).get("max_tokens")
+                    if effective_max_tokens is None:
+                        effective_max_tokens = self.model_for_task.max_tokens
+                    
                     return await client.get_response(
                         model_info=model_info,
                         message_list=(compressed_messages or message_list),
                         tool_options=tool_options,
-                        max_tokens=self.model_for_task.max_tokens if max_tokens is None else max_tokens,
-                        temperature=temperature if temperature is not None else (model_info.extra_params or {}).get("temperature", self.model_for_task.temperature),
+                        max_tokens=effective_max_tokens,
+                        temperature=effective_temperature,
                         response_format=response_format,
                         stream_response_handler=stream_response_handler,
                         async_response_parser=async_response_parser,

src/main.py

@@ -16,6 +16,7 @@ from src.common.server import get_global_server, Server
 from src.mood.mood_manager import mood_manager
 from src.chat.knowledge import lpmm_start_up
 from rich.traceback import install
+
 # from src.api.main import start_api_server
 
 # 导入新的插件管理器
@@ -23,6 +24,7 @@ from src.plugin_system.core.plugin_manager import plugin_manager
 
 # 导入消息API和traceback模块
 from src.common.message import get_global_api
+from src.dream.dream_agent import start_dream_scheduler
 
 # 插件系统现在使用统一的插件加载器
 
@@ -106,7 +108,7 @@ class MainSystem:
         await async_task_manager.add_task(TelemetryHeartBeatTask())
 
         # 添加记忆遗忘任务
-        from src.chat.utils.memory_forget_task import MemoryForgetTask
+        from src.hippo_memorizer.memory_forget_task import MemoryForgetTask
 
         await async_task_manager.add_task(MemoryForgetTask())
 
@@ -159,6 +161,7 @@ class MainSystem:
         try:
             tasks = [
                 get_emoji_manager().start_periodic_check_register(),
+                start_dream_scheduler(),
                 self.app.run(),
                 self.server.run(),
             ]

src/memory_system/retrieval_tools/query_chat_history.py

@@ -5,11 +5,13 @@
 
 import json
 from typing import Optional
+from datetime import datetime
+
 from src.common.logger import get_logger
 from src.common.database.database_model import ChatHistory
 from src.chat.utils.utils import parse_keywords_string
+from src.config.config import global_config
 from .tool_registry import register_memory_retrieval_tool
-from datetime import datetime
 
 logger = get_logger("memory_retrieval_tools")
 
@@ -33,7 +35,18 @@ async def search_chat_history(
             return "未指定查询参数（需要提供keyword或participant之一）"
 
         # 构建查询条件
-        query = ChatHistory.select().where(ChatHistory.chat_id == chat_id)
+        # 根据配置决定是否限制在当前 chat_id 内查询
+        use_global_search = global_config.memory.global_memory
+
+        if use_global_search:
+            # 全局查询所有聊天记录
+            query = ChatHistory.select()
+            logger.debug(
+                f"search_chat_history 启用全局查询模式，忽略 chat_id 过滤，keyword={keyword}, participant={participant}"
+            )
+        else:
+            # 仅在当前聊天流内查询
+            query = ChatHistory.select().where(ChatHistory.chat_id == chat_id)
 
         # 执行查询
         records = list(query.order_by(ChatHistory.start_time.desc()).limit(50))
@@ -139,9 +152,45 @@ async def search_chat_history(
             else:
                 return "未找到相关聊天记录"
 
-        # 构建结果文本，返回id、theme和keywords
+        # 如果匹配结果超过20条，不返回具体记录，只返回提示和所有相关关键词
+        if len(filtered_records) > 20:
+            # 统计所有记录上的关键词并去重
+            all_keywords_set = set()
+            for record in filtered_records:
+                if record.keywords:
+                    try:
+                        keywords_data = (
+                            json.loads(record.keywords)
+                            if isinstance(record.keywords, str)
+                            else record.keywords
+                        )
+                        if isinstance(keywords_data, list):
+                            for k in keywords_data:
+                                k_str = str(k).strip()
+                                if k_str:
+                                    all_keywords_set.add(k_str)
+                    except (json.JSONDecodeError, TypeError, ValueError):
+                        continue
+
+            # xxx 使用用户原始查询词，优先 keyword，其次 participant，最后退化成“当前条件”
+            search_label = keyword or participant or "当前条件"
+
+            if all_keywords_set:
+                keywords_str = "、".join(sorted(all_keywords_set))
+                return (
+                    f"包含“{search_label}”的结果过多，请尝试更多关键词精确查找\n\n"
+                    f"有关\"{search_label}\"的关键词：\n"
+                    f"{keywords_str}"
+                )
+            else:
+                return (
+                    f"包含“{search_label}”的结果过多，请尝试更多关键词精确查找\n\n"
+                    f"有关\"{search_label}\"的关键词信息为空"
+                )
+
+        # 构建结果文本，返回id、theme和keywords（最多20条）
         results = []
-        for record in filtered_records[:20]:  # 最多返回20条记录
+        for record in filtered_records[:20]:
             result_parts = []
 
             # 添加记忆ID
@@ -173,9 +222,6 @@ async def search_chat_history(
             return "未找到相关聊天记录"
 
         response_text = "\n\n---\n\n".join(results)
-        if len(filtered_records) > 20:
-            omitted_count = len(filtered_records) - 20
-            response_text += f"\n\n(还有{omitted_count}条记录已省略，可使用记忆ID查询详细信息)"
         return response_text
 
     except Exception as e:

src/plugin_system/apis/message_api.py

@@ -72,7 +72,7 @@ def get_messages_by_time_in_chat(
     limit_mode: str = "latest",
     filter_mai: bool = False,
     filter_command: bool = False,
-    filter_no_read_command: bool = False,
+    filter_intercept_message_level: Optional[int] = None,
 ) -> List[DatabaseMessages]:
     """
     获取指定聊天中指定时间范围内的消息
@@ -111,7 +111,7 @@ def get_messages_by_time_in_chat(
         limit_mode=limit_mode,
         filter_bot=filter_mai,
         filter_command=filter_command,
-        filter_no_read_command=filter_no_read_command,
+        filter_intercept_message_level=filter_intercept_message_level,
     )
 
 
@@ -123,7 +123,7 @@ def get_messages_by_time_in_chat_inclusive(
     limit_mode: str = "latest",
     filter_mai: bool = False,
     filter_command: bool = False,
-    filter_no_read_command: bool = False,
+    filter_intercept_message_level: Optional[int] = None,
 ) -> List[DatabaseMessages]:
     """
     获取指定聊天中指定时间范围内的消息（包含边界）
@@ -158,7 +158,7 @@ def get_messages_by_time_in_chat_inclusive(
         limit_mode=limit_mode,
         filter_bot=filter_mai,
         filter_command=filter_command,
-        filter_no_read_command=filter_no_read_command,
+        filter_intercept_message_level=filter_intercept_message_level,
     )
     if filter_mai:
         return filter_mai_messages(messages)
@@ -284,7 +284,7 @@ def get_messages_before_time_in_chat(
     timestamp: float,
     limit: int = 0,
     filter_mai: bool = False,
-    filter_no_read_command: bool = False,
+    filter_intercept_message_level: Optional[int] = None,
 ) -> List[DatabaseMessages]:
     """
     获取指定聊天中指定时间戳之前的消息
@@ -313,7 +313,7 @@ def get_messages_before_time_in_chat(
         chat_id=chat_id,
         timestamp=timestamp,
         limit=limit,
-        filter_no_read_command=filter_no_read_command,
+        filter_intercept_message_level=filter_intercept_message_level,
     )
     if filter_mai:
         return filter_mai_messages(messages)

src/plugin_system/base/base_command.py

@@ -55,11 +55,11 @@ class BaseCommand(ABC):
         self.matched_groups = groups
 
     @abstractmethod
-    async def execute(self) -> Tuple[bool, Optional[str], bool]:
+    async def execute(self) -> Tuple[bool, Optional[str], int]:
         """执行Command的抽象方法，子类必须实现
 
         Returns:
-            Tuple[bool, Optional[str], bool]: (是否执行成功, 可选的回复消息, 是否拦截消息 不进行 后续处理)
+            Tuple[bool, Optional[str], int]: (是否执行成功, 可选的回复消息, 拦截消息力度，0代表不拦截，1代表仅不触发回复，replyer可见，2代表不触发回复，replyer不可见)
         """
         pass
 

src/webui/config_routes.py

@@ -8,7 +8,7 @@ from fastapi import APIRouter, HTTPException, Body
 from typing import Any, Annotated
 
 from src.common.logger import get_logger
-from src.common.toml_utils import save_toml_with_format
+from src.common.toml_utils import save_toml_with_format, _update_toml_doc
 from src.config.config import Config, APIAdapterConfig, CONFIG_DIR, PROJECT_ROOT
 from src.config.official_configs import (
     BotConfig,
@@ -51,40 +51,6 @@ PathBody = Annotated[dict[str, str], Body()]
 router = APIRouter(prefix="/config", tags=["config"])
 
 
-# ===== 辅助函数 =====
-
-
-def _update_dict_preserve_comments(target: Any, source: Any) -> None:
-    """
-    递归合并字典，保留 target 中的注释和格式
-    将 source 的值更新到 target 中（仅更新已存在的键）
-
-    Args:
-        target: 目标字典（tomlkit 对象，包含注释）
-        source: 源字典（普通 dict 或 list）
-    """
-    # 如果 source 是列表，直接替换（数组表没有注释保留的意义）
-    if isinstance(source, list):
-        return  # 调用者需要直接赋值
-
-    # 如果都是字典，递归合并
-    if isinstance(source, dict) and isinstance(target, dict):
-        for key, value in source.items():
-            if key == "version":
-                continue  # 跳过版本号
-            if key in target:
-                target_value = target[key]
-                # 递归处理嵌套字典
-                if isinstance(value, dict) and isinstance(target_value, dict):
-                    _update_dict_preserve_comments(target_value, value)
-                else:
-                    # 使用 tomlkit.item 保持类型
-                    try:
-                        target[key] = tomlkit.item(value)
-                    except (TypeError, ValueError):
-                        target[key] = value
-
-
 # ===== 架构获取接口 =====
 
 
@@ -238,7 +204,7 @@ async def update_bot_config(config_data: ConfigBody):
         except Exception as e:
             raise HTTPException(status_code=400, detail=f"配置数据验证失败: {str(e)}") from e
 
-        # 保存配置文件（格式化数组为多行）
+        # 保存配置文件（自动保留注释和格式）
         config_path = os.path.join(CONFIG_DIR, "bot_config.toml")
         save_toml_with_format(config_data, config_path)
 
@@ -261,7 +227,7 @@ async def update_model_config(config_data: ConfigBody):
         except Exception as e:
             raise HTTPException(status_code=400, detail=f"配置数据验证失败: {str(e)}") from e
 
-        # 保存配置文件（格式化数组为多行）
+        # 保存配置文件（自动保留注释和格式）
         config_path = os.path.join(CONFIG_DIR, "model_config.toml")
         save_toml_with_format(config_data, config_path)
 
@@ -300,7 +266,7 @@ async def update_bot_config_section(section_name: str, section_data: SectionBody
             config_data[section_name] = section_data
         elif isinstance(section_data, dict) and isinstance(config_data[section_name], dict):
             # 字典递归合并
-            _update_dict_preserve_comments(config_data[section_name], section_data)
+            _update_toml_doc(config_data[section_name], section_data)
         else:
             # 其他类型直接替换
             config_data[section_name] = section_data
@@ -398,7 +364,7 @@ async def update_model_config_section(section_name: str, section_data: SectionBo
             config_data[section_name] = section_data
         elif isinstance(section_data, dict) and isinstance(config_data[section_name], dict):
             # 字典递归合并
-            _update_dict_preserve_comments(config_data[section_name], section_data)
+            _update_toml_doc(config_data[section_name], section_data)
         else:
             # 其他类型直接替换
             config_data[section_name] = section_data

src/webui/expression_routes.py

@@ -1,7 +1,7 @@
 """表达方式管理 API 路由"""
 
 from fastapi import APIRouter, HTTPException, Header, Query, Cookie
-from pydantic import BaseModel
+from pydantic import BaseModel, NonNegativeFloat
 from typing import Optional, List, Dict
 from src.common.logger import get_logger
 from src.common.database.database_model import Expression, ChatStreams
@@ -21,7 +21,6 @@ class ExpressionResponse(BaseModel):
     situation: str
     style: str
     context: Optional[str]
-    up_content: Optional[str]
     last_active_time: float
     chat_id: str
     create_date: Optional[float]
@@ -49,8 +48,7 @@ class ExpressionCreateRequest(BaseModel):
 
     situation: str
     style: str
-    context: Optional[str] = None
-    up_content: Optional[str] = None
+    context: Optional[str] = NonNegativeFloat
     chat_id: str
 
 
@@ -60,7 +58,6 @@ class ExpressionUpdateRequest(BaseModel):
     situation: Optional[str] = None
     style: Optional[str] = None
     context: Optional[str] = None
-    up_content: Optional[str] = None
     chat_id: Optional[str] = None
 
 
@@ -102,7 +99,6 @@ def expression_to_response(expression: Expression) -> ExpressionResponse:
         situation=expression.situation,
         style=expression.style,
         context=expression.context,
-        up_content=expression.up_content,
         last_active_time=expression.last_active_time,
         chat_id=expression.chat_id,
         create_date=expression.create_date,
@@ -310,7 +306,6 @@ async def create_expression(request: ExpressionCreateRequest, maibot_session: Op
             situation=request.situation,
             style=request.style,
             context=request.context,
-            up_content=request.up_content,
             chat_id=request.chat_id,
             last_active_time=current_time,
             create_date=current_time,

src/webui/plugin_routes.py

@@ -1420,18 +1420,8 @@ async def update_plugin_config(
             shutil.copy(config_path, backup_path)
             logger.info(f"已备份配置文件: {backup_path}")
 
-        # 写入新配置（使用 tomlkit 保留注释）
-        import tomlkit
-
-        # 先读取原配置以保留注释和格式
-        existing_doc = tomlkit.document()
-        if config_path.exists():
-            with open(config_path, "r", encoding="utf-8") as f:
-                existing_doc = tomlkit.load(f)
-        # 更新值
-        for key, value in request.config.items():
-            existing_doc[key] = value
-        save_toml_with_format(existing_doc, str(config_path))
+        # 写入新配置（自动保留注释和格式）
+        save_toml_with_format(request.config, str(config_path))
 
         logger.info(f"已更新插件配置: {plugin_id}")
 

src/webui/routes.py

@@ -223,9 +223,9 @@ async def update_token(
         # 更新 token
         success, message = token_manager.update_token(request.new_token)
         
-        # 如果更新成功，更新 Cookie
+        # 如果更新成功，清除 Cookie，要求用户重新登录
         if success:
-            set_auth_cookie(response, request.new_token)
+            clear_auth_cookie(response)
 
         return TokenUpdateResponse(success=success, message=message)
     except HTTPException:
@@ -272,8 +272,8 @@ async def regenerate_token(
         # 重新生成 token
         new_token = token_manager.regenerate_token()
         
-        # 更新 Cookie
-        set_auth_cookie(response, new_token)
+        # 清除 Cookie，要求用户重新登录
+        clear_auth_cookie(response)
 
         return TokenRegenerateResponse(success=True, token=new_token, message="Token 已重新生成")
     except HTTPException:

src/webui/token_manager.py

@@ -160,13 +160,29 @@ class TokenManager:
 
     def regenerate_token(self) -> str:
         """
-        重新生成 token
+        重新生成 token（保留 first_setup_completed 状态）
 
         Returns:
             str: 新生成的 token
         """
         logger.info("正在重新生成 WebUI Token...")
-        return self._create_new_token()
+        
+        # 生成新的 64 位十六进制字符串
+        new_token = secrets.token_hex(32)
+        
+        # 加载现有配置，保留 first_setup_completed 状态
+        config = self._load_config()
+        old_token = config.get("access_token", "")[:8] if config.get("access_token") else "无"
+        first_setup_completed = config.get("first_setup_completed", True)  # 默认为 True，表示已完成配置
+        
+        config["access_token"] = new_token
+        config["updated_at"] = self._get_current_timestamp()
+        config["first_setup_completed"] = first_setup_completed  # 保留原来的状态
+        
+        self._save_config(config)
+        logger.info(f"WebUI Token 已重新生成: {old_token}... -> {new_token[:8]}...")
+        
+        return new_token
 
     def _validate_token_format(self, token: str) -> bool:
         """

template/bot_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "6.23.5"
+version = "7.0.2"
 
 #----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
 # 如果你想要修改配置文件，请递增version的值
@@ -69,6 +69,7 @@ learning_list = [ # 表达学习配置列表，支持按聊天流配置
     # 第三位: 是否学习表达 ("enable"/"disable") 
     # 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）
     # 学习强度越高，学习越频繁；学习强度越低，学习越少
+    # 如果学习强度设置为0会自动转换为0.0001以避免除以零错误
 ]
 
 expression_groups = [
@@ -86,7 +87,7 @@ allow_reflect = [] # 允许进行表达反思的聊天流ID列表，格式：["q
 
 
 [chat] # 麦麦的聊天设置
-talk_value = 1 # 聊天频率，越小越沉默，范围0-1
+talk_value = 1 # 聊天频率，越小越沉默，范围0-1，如果设置为0会自动转换为0.0001以避免除以零错误
 mentioned_bot_reply = true # 是否启用提及必回复
 max_context_size = 30 # 上下文长度
 planner_smooth = 2 # 规划器平滑，增大数值会减小planner负荷，略微降低反应速度，推荐1-5，0为关闭，必须大于等于0
@@ -97,7 +98,7 @@ enable_talk_value_rules = true # 是否启用动态发言频率规则
 # 推荐格式（对象数组）：{ target="platform:id:type" 或 "", time="HH:MM-HH:MM", value=0.5 }
 # 说明:
 # - target 为空字符串表示全局；type 为 group/private，例如："qq:1919810:group" 或 "qq:114514:private"；
-# - 支持跨夜区间，例如 "23:00-02:00"；数值范围建议 0-1。
+# - 支持跨夜区间，例如 "23:00-02:00"；数值范围建议 0-1，如果 value 设置为0会自动转换为0.0001以避免除以零错误。
 talk_value_rules = [
     { target = "", time = "00:00-08:59", value = 0.8 },
     { target = "", time = "09:00-22:59", value = 1.0 },
@@ -110,6 +111,24 @@ include_planner_reasoning = false # 是否将planner推理加入replyer，默认
 [memory]
 max_agent_iterations = 3 # 记忆思考深度（最低为1（不深入思考））
 enable_jargon_detection = true # 记忆检索过程中是否启用黑话识别
+global_memory = false # 是否允许记忆检索进行全局查询
+
+[dream]
+interval_minutes = 45 # 做梦时间间隔（分钟），默认30分钟
+max_iterations = 20 # 做梦最大轮次，默认20轮
+first_delay_seconds = 1200 # 程序启动后首次做梦前的延迟时间（秒），默认60秒
+
+# 做梦时间段配置，格式：["HH:MM-HH:MM", ...]
+# 如果列表为空，则表示全天允许做梦。
+# 如果配置了时间段，则只有在这些时间段内才会实际执行做梦流程。
+# 时间段外，调度器仍会按间隔检查，但不会进入做梦流程。
+# 支持跨夜区间，例如 "23:00-02:00" 表示从23:00到次日02:00。
+# 示例：
+dream_time_ranges = [
+    # "09:00-22:00",      # 白天允许做梦
+    "23:00-10:00",      # 跨夜时间段（23:00到次日10:00）
+]
+# dream_time_ranges = []
 
 [jargon]
 all_global = true # 是否开启全局黑话模式，注意，此功能关闭后，已经记录的全局黑话不会改变，需要手动删除

template/model_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "1.8.2"
+version = "1.9.0"
 
 # 配置文件版本号迭代规则同bot_config.toml
 
@@ -54,9 +54,11 @@ name = "siliconflow-deepseek-v3.2"
 api_provider = "SiliconFlow"
 price_in = 2.0
 price_out = 3.0
+# temperature = 0.5    # 可选：为该模型单独指定温度，会覆盖任务配置中的温度
+# max_tokens = 4096    # 可选：为该模型单独指定最大token数，会覆盖任务配置中的max_tokens
 [models.extra_params] # 可选的额外参数配置
 enable_thinking = false # 不启用思考
-# temperature = 0.5    # 可选：为该模型单独指定温度，会覆盖任务配置中的温度
+
 
 [[models]]
 model_identifier = "deepseek-ai/DeepSeek-V3.2-Exp"
@@ -64,9 +66,11 @@ name = "siliconflow-deepseek-v3.2-think"
 api_provider = "SiliconFlow"    
 price_in = 2.0
 price_out = 3.0
+# temperature = 0.7    # 可选：为该模型单独指定温度，会覆盖任务配置中的温度
+# max_tokens = 4096    # 可选：为该模型单独指定最大token数，会覆盖任务配置中的max_tokens
 [models.extra_params] # 可选的额外参数配置
 enable_thinking = true # 启用思考
-# temperature = 0.7    # 可选：为该模型单独指定温度，会覆盖任务配置中的温度
+
 
 [[models]]
 model_identifier = "Qwen/Qwen3-Next-80B-A3B-Instruct"

webui/dist/index.html

@@ -7,7 +7,7 @@
     <link rel="icon" type="image/x-icon" href="/maimai.ico" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>MaiBot Dashboard</title>
-    <script type="module" crossorigin src="/assets/index-DJb_iiTR.js"></script>
+    <script type="module" crossorigin src="/assets/index-siV9e-l5.js"></script>
     <link rel="modulepreload" crossorigin href="/assets/react-vendor-Dtc2IqVY.js">
     <link rel="modulepreload" crossorigin href="/assets/router-CWhjJi2n.js">
     <link rel="modulepreload" crossorigin href="/assets/utils-CCeOswSm.js">
@@ -21,7 +21,7 @@
     <link rel="modulepreload" crossorigin href="/assets/uppy-BHC3OXBx.js">
     <link rel="modulepreload" crossorigin href="/assets/markdown-A1ShuLvG.js">
     <link rel="modulepreload" crossorigin href="/assets/reactflow-B3n3_Vkw.js">
-    <link rel="stylesheet" crossorigin href="/assets/index-QJDQd8Xo.css">
+    <link rel="stylesheet" crossorigin href="/assets/index-DM1UfLap.css">
   </head>
   <body>
     <div id="root" class="notranslate"></div>