feat:pfc Lite(hearfFC）在群聊初步可用

interest_monitor_gui.py

@@ -93,6 +93,10 @@ class InterestMonitorApp:
         # --- 初始化和启动刷新 ---
         self.update_display() # 首次加载并开始刷新循环
 
+    def on_stream_selected(self, event=None):
+        """当 Combobox 选择改变时调用，更新单个流的图表"""
+        self.update_single_stream_plot()
+
     def get_random_color(self):
         """生成随机颜色用于区分线条"""
         return "#{:06x}".format(random.randint(0, 0xFFFFFF))
@@ -305,11 +309,82 @@ class InterestMonitorApp:
         self.ax_single_probability.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
 
         selected_name = self.selected_stream_id.get()
+        selected_sid = None
+
+        # --- 新增：根据选中的名称找到 stream_id ---
+        if selected_name:
+            for sid, name in self.stream_display_names.items():
+                if name == selected_name:
+                    selected_sid = sid
+                    break
+
+        all_times = [] # 用于确定 X 轴范围
+
+        # --- 新增：绘制兴趣度图 ---
+        if selected_sid and selected_sid in self.stream_history and self.stream_history[selected_sid]:
+            history = self.stream_history[selected_sid]
+            timestamps, interests = zip(*history)
+            try:
+                mpl_dates = [datetime.fromtimestamp(ts) for ts in timestamps]
+                all_times.extend(mpl_dates)
+                self.ax_single_interest.plot(
+                    mpl_dates,
+                    interests,
+                    color=self.stream_colors.get(selected_sid, 'blue'),
+                    marker='.',
+                    markersize=3,
+                    linestyle='-',
+                    linewidth=1
+                )
+            except ValueError as e:
+                 print(f"Skipping interest plot for {selected_sid} due to invalid timestamp: {e}")
+
+        # --- 新增：绘制概率图 ---
+        if selected_sid and selected_sid in self.probability_history and self.probability_history[selected_sid]:
+            prob_history = self.probability_history[selected_sid]
+            prob_timestamps, probabilities = zip(*prob_history)
+            try:
+                prob_mpl_dates = [datetime.fromtimestamp(ts) for ts in prob_timestamps]
+                # 注意：概率图的时间点可能与兴趣度不同，也需要加入 all_times
+                all_times.extend(prob_mpl_dates) 
+                self.ax_single_probability.plot(
+                    prob_mpl_dates,
+                    probabilities,
+                    color=self.stream_colors.get(selected_sid, 'green'), # 可以用不同颜色
+                    marker='.',
+                    markersize=3,
+                    linestyle='-',
+                    linewidth=1
+                )
+            except ValueError as e:
+                 print(f"Skipping probability plot for {selected_sid} due to invalid timestamp: {e}")
+
+        # --- 新增：调整 X 轴范围和格式 ---
+        if all_times:
+             min_time = min(all_times)
+             max_time = max(all_times)
+             # 设置共享的 X 轴范围
+             self.ax_single_interest.set_xlim(min_time, max_time) 
+             # self.ax_single_probability.set_xlim(min_time, max_time) # sharex 会自动同步
+             # 自动格式化X轴标签 (应用到共享轴的最后一个子图上通常即可)
+             self.fig_single.autofmt_xdate()
+        else:
+            # 如果没有数据，设置一个默认的时间范围
+            now = datetime.now()
+            one_hour_ago = now - timedelta(hours=1)
+            self.ax_single_interest.set_xlim(one_hour_ago, now)
+            # self.ax_single_probability.set_xlim(one_hour_ago, now) # sharex 会自动同步
+
+        # --- 新增：重新绘制画布 ---
+        self.canvas_single.draw()
+
     def update_display(self):
         """主更新循环"""
         try:
             self.load_and_update_history() # 从文件加载数据并更新内部状态
-            self.update_plot()             # 根据内部状态更新图表
+            # *** 修改：分别调用两个图表的更新方法 ***
+            self.update_all_streams_plot() # 更新所有流的图表
+            self.update_single_stream_plot() # 更新单个流的图表
         except Exception as e:
             # 提供更详细的错误信息
             import traceback

src/do_tool/tool_use.py

@@ -165,7 +165,7 @@ class ToolUser:
                 tool_calls_str = ""
                 for tool_call in tool_calls:
                     tool_calls_str += f"{tool_call['function']['name']}\n"
-                logger.info(f"根据:\n{prompt}\n模型请求调用{len(tool_calls)}个工具: {tool_calls_str}")
+                logger.info(f"根据:\n{prompt[0:100]}...\n模型请求调用{len(tool_calls)}个工具: {tool_calls_str}")
                 tool_results = []
                 structured_info = {}  # 动态生成键
 

src/heart_flow/heartflow.py

@@ -244,6 +244,10 @@ class Heartflow:
         """获取指定ID的SubHeartflow实例"""
         return self._subheartflows.get(observe_chat_id)
 
+    def get_all_subheartflows_streams_ids(self) -> list[Any]:
+        """获取当前所有活跃的子心流的 ID 列表"""
+        return list(self._subheartflows.keys())
+
 
 init_prompt()
 # 创建一个全局的管理器实例

src/heart_flow/sub_heartflow.py

@@ -37,13 +37,13 @@ def init_prompt():
     # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
     prompt += "{extra_info}\n"
     # prompt += "{prompt_schedule}\n"
-    prompt += "{relation_prompt_all}\n"
+    # prompt += "{relation_prompt_all}\n"
     prompt += "{prompt_personality}\n"
     prompt += "刚刚你的想法是{current_thinking_info}。可以适当转换话题\n"
     prompt += "-----------------------------------\n"
     prompt += "现在是{time_now}，你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：\n{chat_observe_info}\n"
     prompt += "你现在{mood_info}\n"
-    prompt += "你注意到{sender_name}刚刚说：{message_txt}\n"
+    # prompt += "你注意到{sender_name}刚刚说：{message_txt}\n"
     prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白"
     prompt += "思考时可以想想如何对群聊内容进行回复。回复的要求是：平淡一些，简短一些，说中文，尽量不要说你说过的话。如果你要回复，最好只回复一个人的一个话题\n"
     prompt += "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)，不要带有括号和动作描写"
@@ -199,7 +199,7 @@ class SubHeartflow:
             logger.error(f"[{self.subheartflow_id}] do_observe called but no valid observation found.")
 
     async def do_thinking_before_reply(
-        self, message_txt: str, sender_info: UserInfo, chat_stream: ChatStream, extra_info: str, obs_id: list[str] = None # 修改 obs_id 类型为 list[str]
+        self,  chat_stream: ChatStream, extra_info: str, obs_id: list[str] = None # 修改 obs_id 类型为 list[str]
     ):
         async with self._thinking_lock: # 获取思考锁
             # --- 在思考前确保观察已执行 --- #
@@ -246,45 +246,45 @@ class SubHeartflow:
             identity_detail = individuality.identity.identity_detail
             if identity_detail: random.shuffle(identity_detail); prompt_personality += f",{identity_detail[0]}"
 
-            who_chat_in_group = [
-                (chat_stream.platform, sender_info.user_id, sender_info.user_nickname) # 先添加当前发送者
-            ]
-            # 获取最近发言者，排除当前发送者，避免重复
-            recent_speakers = get_recent_group_speaker(
-                chat_stream.stream_id,
-                (chat_stream.platform, sender_info.user_id),
-                limit=global_config.MAX_CONTEXT_SIZE -1 # 减去当前发送者
-            )
-            who_chat_in_group.extend(recent_speakers)
+            # who_chat_in_group = [
+            #     (chat_stream.platform, sender_info.user_id, sender_info.user_nickname) # 先添加当前发送者
+            # ]
+            # # 获取最近发言者，排除当前发送者，避免重复
+            # recent_speakers = get_recent_group_speaker(
+            #     chat_stream.stream_id,
+            #     (chat_stream.platform, sender_info.user_id),
+            #     limit=global_config.MAX_CONTEXT_SIZE -1 # 减去当前发送者
+            # )
+            # who_chat_in_group.extend(recent_speakers)
 
-            relation_prompt = ""
-            unique_speakers = set() # 确保人物信息不重复
-            for person_tuple in who_chat_in_group:
-                person_key = (person_tuple[0], person_tuple[1]) # 使用 platform+id 作为唯一标识
-                if person_key not in unique_speakers:
-                    relation_prompt += await relationship_manager.build_relationship_info(person_tuple)
-                    unique_speakers.add(person_key)
+            # relation_prompt = ""
+            # unique_speakers = set() # 确保人物信息不重复
+            # for person_tuple in who_chat_in_group:
+            #     person_key = (person_tuple[0], person_tuple[1]) # 使用 platform+id 作为唯一标识
+            #     if person_key not in unique_speakers:
+            #         relation_prompt += await relationship_manager.build_relationship_info(person_tuple)
+            #         unique_speakers.add(person_key)
 
-            relation_prompt_all = (await global_prompt_manager.get_prompt_async("relationship_prompt")).format(
-                relation_prompt, sender_info.user_nickname
-            )
+            # relation_prompt_all = (await global_prompt_manager.get_prompt_async("relationship_prompt")).format(
+            #     relation_prompt, sender_info.user_nickname
+            # )
 
-            sender_name_sign = (
-                f"<{chat_stream.platform}:{sender_info.user_id}:{sender_info.user_nickname}:{sender_info.user_cardname or 'NoCard'}>"
-            )
+            # sender_name_sign = (
+            #     f"<{chat_stream.platform}:{sender_info.user_id}:{sender_info.user_nickname}:{sender_info.user_cardname or 'NoCard'}>"
+            # )
 
             time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
 
             prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_before")).format(
                 extra_info=extra_info_prompt,
-                relation_prompt_all=relation_prompt_all,
+                # relation_prompt_all=relation_prompt_all,
                 prompt_personality=prompt_personality,
                 current_thinking_info=current_thinking_info,
                 time_now=time_now,
                 chat_observe_info=chat_observe_info,
                 mood_info=mood_info,
-                sender_name=sender_name_sign,
-                message_txt=message_txt,
+                # sender_name=sender_name_sign,
+                # message_txt=message_txt,
                 bot_name=self.bot_name,
             )
 

src/plugins/chat_module/heartFC_chat/heartFC__generator.py

@@ -38,7 +38,7 @@ class ResponseGenerator:
         self.current_model_type = "r1"  # 默认使用 R1
         self.current_model_name = "unknown model"
 
-    async def generate_response(self, message: MessageRecv, thinking_id: str) -> Optional[List[str]]:
+    async def generate_response(self, message: MessageRecv, thinking_id: str,) -> Optional[List[str]]:
         """根据当前模型类型选择对应的生成函数"""
 
         logger.info(

src/plugins/chat_module/heartFC_chat/heartFC_chat.py

@@ -1,8 +1,9 @@
 import time
 from random import random
 import traceback
-from typing import List, Optional
+from typing import List, Optional, Dict
 import asyncio
+from asyncio import Lock
 from ...moods.moods import MoodManager
 from ....config.config import global_config
 from ...chat.emoji_manager import emoji_manager
@@ -19,7 +20,8 @@ from ...utils.timer_calculater import Timer
 from src.do_tool.tool_use import ToolUser
 from .interest import InterestManager, InterestChatting
 from src.plugins.chat.chat_stream import chat_manager
-from src.plugins.chat.message import MessageInfo
+from src.plugins.chat.message import BaseMessageInfo
+from .pf_chatting import PFChatting
 
 # 定义日志配置
 chat_config = LogConfig(
@@ -33,13 +35,32 @@ logger = get_module_logger("heartFC_chat", config=chat_config)
 INTEREST_MONITOR_INTERVAL_SECONDS = 1
 
 class HeartFC_Chat:
+    _instance = None # For potential singleton access if needed by MessageManager
+
     def __init__(self):
+        # --- Updated Init ---
+        if HeartFC_Chat._instance is not None:
+            # Prevent re-initialization if used as a singleton
+            return
+        self.logger = logger # Make logger accessible via self
         self.gpt = ResponseGenerator()
         self.mood_manager = MoodManager.get_instance()
         self.mood_manager.start_mood_update()
         self.tool_user = ToolUser()
         self.interest_manager = InterestManager()
         self._interest_monitor_task: Optional[asyncio.Task] = None
+        # --- New PFChatting Management ---
+        self.pf_chatting_instances: Dict[str, PFChatting] = {}
+        self._pf_chatting_lock = Lock()
+        # --- End New PFChatting Management ---
+        HeartFC_Chat._instance = self # Register instance
+        # --- End Updated Init ---
+
+    # --- Added Class Method for Singleton Access ---
+    @classmethod
+    def get_instance(cls):
+        return cls._instance
+    # --- End Added Class Method ---
 
     async def start(self):
         """启动异步任务,如兴趣监控器"""
@@ -61,14 +82,29 @@ class HeartFC_Chat:
         else:
             logger.warning("跳过兴趣监控任务创建：任务已存在或正在运行。")
 
+    # --- Added PFChatting Instance Manager ---
+    async def _get_or_create_pf_chatting(self, stream_id: str) -> Optional[PFChatting]:
+        """获取现有PFChatting实例或创建新实例。"""
+        async with self._pf_chatting_lock:
+            if stream_id not in self.pf_chatting_instances:
+                self.logger.info(f"为流 {stream_id} 创建新的PFChatting实例")
+                # 传递 self (HeartFC_Chat 实例) 进行依赖注入
+                instance = PFChatting(stream_id, self)
+                # 执行异步初始化
+                if not await instance._initialize():
+                    self.logger.error(f"为流 {stream_id} 初始化PFChatting失败")
+                    return None
+                self.pf_chatting_instances[stream_id] = instance
+            return self.pf_chatting_instances[stream_id]
+    # --- End Added PFChatting Instance Manager ---
+
     async def _interest_monitor_loop(self):
         """后台任务，定期检查兴趣度变化并触发回复"""
         logger.info("兴趣监控循环开始...")
         while True:
             await asyncio.sleep(INTEREST_MONITOR_INTERVAL_SECONDS)
             try:
-                # --- 修改：遍历 SubHeartflow 并检查触发器 ---
-                active_stream_ids = list(heartflow.get_all_subheartflows_streams_ids()) # 需要 heartflow 提供此方法
+                active_stream_ids = list(heartflow.get_all_subheartflows_streams_ids())
                 logger.trace(f"检查 {len(active_stream_ids)} 个活跃流是否足以开启心流对话...")
 
                 for stream_id in active_stream_ids:
@@ -77,26 +113,28 @@ class HeartFC_Chat:
                         logger.warning(f"监控循环: 无法获取活跃流 {stream_id} 的 sub_hf")
                         continue
 
-                    # --- 获取 Observation 和消息列表 --- #
-                    observation = sub_hf._get_primary_observation()
-                    if not observation:
-                        logger.warning(f"[{stream_id}] SubHeartflow 没有在观察，无法检查触发器。")
-                        continue
-                    observed_messages = observation.talking_message # 获取消息字典列表
-                    # --- 结束获取 --- #
-
                     should_trigger = False
                     try:
-                        # check_reply_trigger 可以选择性地接收 observed_messages 作为参数
-                        should_trigger = await sub_hf.check_reply_trigger() # 目前 check_reply_trigger 还不处理这个
+                        interest_chatting = self.interest_manager.get_interest_chatting(stream_id)
+                        if interest_chatting:
+                            should_trigger = interest_chatting.should_evaluate_reply()
+                            if should_trigger:
+                                logger.info(f"[{stream_id}] 基于兴趣概率决定启动交流模式 (概率: {interest_chatting.current_reply_probability:.4f})。")
+                        else:
+                            logger.trace(f"[{stream_id}] 没有找到对应的 InterestChatting 实例，跳过基于兴趣的触发检查。")
                     except Exception as e:
-                        logger.error(f"错误调用 check_reply_trigger 流 {stream_id}: {e}")
+                        logger.error(f"检查兴趣触发器时出错 流 {stream_id}: {e}")
                         logger.error(traceback.format_exc())
 
                     if should_trigger:
-                        logger.info(f"[{stream_id}] SubHeartflow 决定开启心流对话。")
-                        # 调用修改后的处理函数，传递 stream_id 和 observed_messages
-                        asyncio.create_task(self._process_triggered_reply(stream_id, observed_messages))
+                        logger.info(f"[{stream_id}] 触发条件满足, 委托给PFChatting.")
+                        # --- 修改: 获取 PFChatting 实例并调用 add_time (无参数，时间由内部衰减逻辑决定) ---
+                        pf_instance = await self._get_or_create_pf_chatting(stream_id)
+                        if pf_instance:
+                            # 调用 add_time 启动或延长循环，时间由 PFChatting 内部决定
+                            asyncio.create_task(pf_instance.add_time())
+                        else:
+                            logger.error(f"[{stream_id}] 无法获取或创建PFChatting实例。跳过触发。")
 
 
             except asyncio.CancelledError:
@@ -107,32 +145,6 @@ class HeartFC_Chat:
                 logger.error(traceback.format_exc())
                 await asyncio.sleep(5) # 发生错误时等待
 
-    async def _process_triggered_reply(self, stream_id: str, observed_messages: List[dict]):
-        """Helper coroutine to handle the processing of a triggered reply based on SubHeartflow trigger."""
-        try:
-            logger.info(f"[{stream_id}] SubHeartflow 触发回复...")
-            # 调用修改后的 trigger_reply_generation
-            await self.trigger_reply_generation(stream_id, observed_messages)
-
-            # --- 调整兴趣降低逻辑 ---
-            # 这里的兴趣降低可能不再适用，或者需要基于不同的逻辑
-            # 例如，回复后可以将 SubHeartflow 的某种"回复意愿"状态重置
-            # 暂时注释掉，或根据需要调整
-            # chatting_instance = self.interest_manager.get_interest_chatting(stream_id)
-            # if chatting_instance:
-            #    decrease_value = chatting_instance.trigger_threshold / 2 # 使用实例的阈值
-            #    self.interest_manager.decrease_interest(stream_id, value=decrease_value)
-            #    post_trigger_interest = self.interest_manager.get_interest(stream_id) # 获取更新后的兴趣
-            #    logger.info(f"[{stream_id}] Interest decreased by {decrease_value:.2f} (InstanceThreshold/2) after processing triggered reply. Current interest: {post_trigger_interest:.2f}")
-            # else:
-            #     logger.warning(f"[{stream_id}] Could not find InterestChatting instance after reply processing to decrease interest.")
-            logger.debug(f"[{stream_id}] Reply processing finished. (Interest decrease logic needs review).")
-
-        except Exception as e:
-            logger.error(f"Error processing SubHeartflow-triggered reply for stream_id {stream_id}: {e}") # 更新日志信息
-            logger.error(traceback.format_exc())
-    # --- 结束修改 ---
-
     async def _create_thinking_message(self, anchor_message: Optional[MessageRecv]):
         """创建思考消息 (尝试锚定到 anchor_message)"""
         if not anchor_message or not anchor_message.chat_stream:
@@ -270,7 +282,7 @@ class HeartFC_Chat:
         sub_hf = None
         anchor_message: Optional[MessageRecv] = None # <--- 重命名，用于锚定回复的消息对象
         userinfo: Optional[UserInfo] = None
-        messageinfo: Optional[MessageInfo] = None
+        messageinfo: Optional[BaseMessageInfo] = None
 
         timing_results = {}
         current_mind = None
@@ -295,33 +307,58 @@ class HeartFC_Chat:
                  logger.error(traceback.format_exc())
                  return
 
-            # --- 2. 尝试从 observed_messages 重建最后一条消息作为锚点 --- #
+            # --- 2. 尝试从 observed_messages 重建最后一条消息作为锚点, 失败则创建占位符 --- #
             try:
-                with Timer("获取最后消息锚点", timing_results):
+                with Timer("获取或创建锚点消息", timing_results):
+                    reconstruction_failed = False
                     if observed_messages:
-                        last_msg_dict = observed_messages[-1] # 直接从传入列表获取最后一条
-                        # 尝试从字典重建 MessageRecv 对象（可能需要调整 MessageRecv 的构造方式或创建一个辅助函数）
-                        # 这是一个简化示例，假设 MessageRecv 可以从字典初始化
-                        # 你可能需要根据 MessageRecv 的实际 __init__ 来调整
                         try:
-                            anchor_message = MessageRecv(last_msg_dict) # 假设 MessageRecv 支持从字典创建
+                            last_msg_dict = observed_messages[-1]
+                            logger.debug(f"[{stream_id}] Attempting to reconstruct MessageRecv from last observed message.")
+                            anchor_message = MessageRecv(last_msg_dict, chat_stream=chat)
+                            if not (anchor_message and anchor_message.message_info and anchor_message.message_info.message_id and anchor_message.message_info.user_info):
+                                raise ValueError("Reconstructed MessageRecv missing essential info.")
                             userinfo = anchor_message.message_info.user_info
                             messageinfo = anchor_message.message_info
-                            logger.debug(f"[{stream_id}] 获取到最后消息作为锚点: ID={messageinfo.message_id}, Sender={userinfo.user_nickname}")
-                        except Exception as e_msg:
-                            logger.error(f"[{stream_id}] 从字典重建最后消息 MessageRecv 失败: {e_msg}. 字典: {last_msg_dict}")
-                            anchor_message = None # 重置以表示失败
+                            logger.debug(f"[{stream_id}] Successfully reconstructed anchor message: ID={messageinfo.message_id}, Sender={userinfo.user_nickname}")
+                        except Exception as e_reconstruct:
+                            logger.warning(f"[{stream_id}] Reconstructing MessageRecv from observed message failed: {e_reconstruct}. Will create placeholder.")
+                            reconstruction_failed = True
                     else:
-                        logger.warning(f"[{stream_id}] 无法从 Observation 获取最后消息锚点。")
-            except Exception as e:
-                logger.error(f"[{stream_id}] 获取最后消息锚点时出错: {e}")
-                logger.error(traceback.format_exc())
-                # 即使没有锚点，也可能继续尝试生成非回复性消息，取决于后续逻辑
+                        logger.warning(f"[{stream_id}] observed_messages is empty. Will create placeholder anchor message.")
+                        reconstruction_failed = True # Treat empty observed_messages as a failure to reconstruct
 
-            # --- 3. 检查是否能继续 (需要思考消息锚点) ---
-            if not anchor_message:
-                logger.warning(f"[{stream_id}] 没有有效的消息锚点，无法创建思考消息和发送回复。取消回复生成。")
-                return
+                    # 如果重建失败或 observed_messages 为空，创建占位符
+                    if reconstruction_failed:
+                        placeholder_id = f"mid_{int(time.time() * 1000)}" # 使用毫秒时间戳增加唯一性
+                        placeholder_user = UserInfo(user_id="system_trigger", user_nickname="系统触发")
+                        placeholder_msg_info = BaseMessageInfo(
+                            message_id=placeholder_id,
+                            platform=chat.platform,
+                            group_info=chat.group_info,
+                            user_info=placeholder_user,
+                            time=time.time()
+                            # 其他 BaseMessageInfo 可能需要的字段设为默认值或 None
+                        )
+                        # 创建 MessageRecv 实例，注意它需要消息字典结构，我们创建一个最小化的
+                        placeholder_msg_dict = {
+                            "message_info": placeholder_msg_info.to_dict(),
+                            "processed_plain_text": "", # 提供空文本
+                            "raw_message": "",
+                            "time": placeholder_msg_info.time,
+                        }
+                        # 先只用字典创建实例
+                        anchor_message = MessageRecv(placeholder_msg_dict)
+                        # 然后调用方法更新 chat_stream
+                        anchor_message.update_chat_stream(chat)
+                        userinfo = anchor_message.message_info.user_info
+                        messageinfo = anchor_message.message_info
+                        logger.info(f"[{stream_id}] Created placeholder anchor message: ID={messageinfo.message_id}, Sender={userinfo.user_nickname}")
+
+            except Exception as e:
+                logger.error(f"[{stream_id}] 获取或创建锚点消息时出错: {e}")
+                logger.error(traceback.format_exc())
+                anchor_message = None # 确保出错时 anchor_message 为 None
 
             # --- 4. 检查并发思考限制 (使用 anchor_message 简化获取) ---
             try:
@@ -399,6 +436,7 @@ class HeartFC_Chat:
                 with Timer("生成内心想法(SubHF)", timing_results):
                     # 不再传递 message_txt 和 sender_info, SubHeartflow 应基于其内部观察
                     current_mind, past_mind = await sub_hf.do_thinking_before_reply(
+                        # sender_info=userinfo,
                         chat_stream=chat,
                         extra_info=tool_result_info,
                         obs_id=get_mid_memory_id,
@@ -415,7 +453,8 @@ class HeartFC_Chat:
             # --- 9. 调用 ResponseGenerator 生成回复 (使用 anchor_message 和 current_mind) ---
             try:
                 with Timer("生成最终回复(GPT)", timing_results):
-                    response_set = await self.gpt.generate_response(anchor_message, thinking_id, current_mind=current_mind)
+                    # response_set = await self.gpt.generate_response(anchor_message, thinking_id, current_mind=current_mind)
+                    response_set = await self.gpt.generate_response(anchor_message, thinking_id)
             except Exception as e:
                  logger.error(f"[{stream_id}] GPT 生成回复失败: {e}")
                  logger.error(traceback.format_exc())

src/plugins/chat_module/heartFC_chat/interest.py

@@ -20,11 +20,11 @@ logger = get_module_logger("InterestManager", config=interest_log_config)
 
 
 # 定义常量
-DEFAULT_DECAY_RATE_PER_SECOND = 0.95  # 每秒衰减率 (兴趣保留 99%)
-MAX_INTEREST = 10.0                # 最大兴趣值
-MIN_INTEREST_THRESHOLD = 0.1      # 低于此值可能被清理 (可选)
+DEFAULT_DECAY_RATE_PER_SECOND = 0.98  # 每秒衰减率 (兴趣保留 99%)
+MAX_INTEREST = 15.0                # 最大兴趣值
+# MIN_INTEREST_THRESHOLD = 0.1      # 低于此值可能被清理 (可选)
 CLEANUP_INTERVAL_SECONDS = 3600     # 清理任务运行间隔 (例如：1小时)
-INACTIVE_THRESHOLD_SECONDS = 3600 * 24 # 不活跃时间阈值 (例如：1天)
+INACTIVE_THRESHOLD_SECONDS = 3600  # 不活跃时间阈值 (例如：1小时)
 LOG_INTERVAL_SECONDS = 3  # 日志记录间隔 (例如：30秒)
 LOG_DIRECTORY = "logs/interest" # 日志目录
 LOG_FILENAME = "interest_log.json" # 快照日志文件名 (保留，以防其他地方用到)
@@ -33,11 +33,11 @@ HISTORY_LOG_FILENAME = "interest_history.log" # 新的历史日志文件名
 # INTEREST_INCREASE_THRESHOLD = 0.5
 
 # --- 新增：概率回复相关常量 ---
-REPLY_TRIGGER_THRESHOLD = 5.0         # 触发概率回复的兴趣阈值 (示例值)
+REPLY_TRIGGER_THRESHOLD = 3.0         # 触发概率回复的兴趣阈值 (示例值)
 BASE_REPLY_PROBABILITY = 0.05         # 首次超过阈值时的基础回复概率 (示例值)
 PROBABILITY_INCREASE_RATE_PER_SECOND = 0.02 # 高于阈值时，每秒概率增加量 (线性增长, 示例值)
 PROBABILITY_DECAY_FACTOR_PER_SECOND = 0.3  # 低于阈值时，每秒概率衰减因子 (指数衰减, 示例值)
-MAX_REPLY_PROBABILITY = 0.95          # 回复概率上限 (示例值)
+MAX_REPLY_PROBABILITY = 1          # 回复概率上限 (示例值)
 # --- 结束：概率回复相关常量 ---
 
 class InterestChatting:
@@ -117,15 +117,15 @@ class InterestChatting:
                 # 持续高于阈值，线性增加概率
                 increase_amount = self.probability_increase_rate * time_delta
                 self.current_reply_probability += increase_amount
-                logger.debug(f"兴趣高于阈值 ({self.trigger_threshold}) 持续 {time_delta:.2f}秒. 概率增加 {increase_amount:.4f} 到 {self.current_reply_probability:.4f}")
+                # logger.debug(f"兴趣高于阈值 ({self.trigger_threshold}) 持续 {time_delta:.2f}秒. 概率增加 {increase_amount:.4f} 到 {self.current_reply_probability:.4f}")
 
             # 限制概率不超过最大值
             self.current_reply_probability = min(self.current_reply_probability, self.max_reply_probability)
 
         else: # 低于阈值
-            if self.is_above_threshold:
-                 # 刚低于阈值，开始衰减
-                 logger.debug(f"兴趣低于阈值 ({self.trigger_threshold}). 概率衰减开始于 {self.current_reply_probability:.4f}")
+            # if self.is_above_threshold:
+            #      # 刚低于阈值，开始衰减
+            #      logger.debug(f"兴趣低于阈值 ({self.trigger_threshold}). 概率衰减开始于 {self.current_reply_probability:.4f}")
             # else: # 持续低于阈值，继续衰减
             #     pass # 不需要特殊处理
 
@@ -133,12 +133,12 @@ class InterestChatting:
             # 检查 decay_factor 是否有效
             if 0 < self.probability_decay_factor < 1:
                 decay_multiplier = math.pow(self.probability_decay_factor, time_delta)
-                old_prob = self.current_reply_probability
+                # old_prob = self.current_reply_probability
                 self.current_reply_probability *= decay_multiplier
                 # 避免因浮点数精度问题导致概率略微大于0，直接设为0
                 if self.current_reply_probability < 1e-6:
                     self.current_reply_probability = 0.0
-                logger.debug(f"兴趣低于阈值 ({self.trigger_threshold}) 持续 {time_delta:.2f}秒. 概率从 {old_prob:.4f} 衰减到 {self.current_reply_probability:.4f} (因子: {self.probability_decay_factor})")
+                # logger.debug(f"兴趣低于阈值 ({self.trigger_threshold}) 持续 {time_delta:.2f}秒. 概率从 {old_prob:.4f} 衰减到 {self.current_reply_probability:.4f} (因子: {self.probability_decay_factor})")
             elif self.probability_decay_factor <= 0:
                 # 如果衰减因子无效或为0，直接清零
                 if self.current_reply_probability > 0:
@@ -212,19 +212,19 @@ class InterestChatting:
         # 确保概率是基于最新兴趣值计算的
         self._update_reply_probability(current_time)
         # 更新兴趣衰减（如果需要，取决于逻辑，这里保持和 get_interest 一致）
-        self._calculate_decay(current_time)
-        self.last_update_time = current_time # 更新时间戳
+        # self._calculate_decay(current_time)
+        # self.last_update_time = current_time # 更新时间戳
 
-        if self.is_above_threshold and self.current_reply_probability > 0:
+        if self.current_reply_probability > 0:
             # 只有在阈值之上且概率大于0时才有可能触发
             trigger = random.random() < self.current_reply_probability
             if trigger:
-                logger.info(f"Reply evaluation triggered! Probability: {self.current_reply_probability:.4f}, Threshold: {self.trigger_threshold}, Interest: {self.interest_level:.2f}")
+                logger.info(f"回复概率评估触发! 概率: {self.current_reply_probability:.4f}, 阈值: {self.trigger_threshold}, 兴趣: {self.interest_level:.2f}")
                 # 可选：触发后是否重置/降低概率？根据需要决定
                 # self.current_reply_probability = self.base_reply_probability # 例如，触发后降回基础概率
                 # self.current_reply_probability *= 0.5 # 例如，触发后概率减半
             else:
-                 logger.debug(f"Reply evaluation NOT triggered. Probability: {self.current_reply_probability:.4f}, Random value: {trigger + 1e-9:.4f}") # 打印随机值用于调试
+                 logger.debug(f"回复概率评估未触发。概率: {self.current_reply_probability:.4f}") 
             return trigger
         else:
             # logger.debug(f"Reply evaluation check: Below threshold or zero probability. Probability: {self.current_reply_probability:.4f}")
@@ -271,12 +271,12 @@ class InterestManager:
         except OSError as e:
             logger.error(f"Error creating log directory '{LOG_DIRECTORY}': {e}")
 
-    async def _periodic_cleanup_task(self, interval_seconds: int, threshold: float, max_age_seconds: int):
+    async def _periodic_cleanup_task(self, interval_seconds: int, max_age_seconds: int):
         """后台清理任务的异步函数"""
         while True:
             await asyncio.sleep(interval_seconds)
             logger.info(f"运行定期清理 (间隔: {interval_seconds}秒)...")
-            self.cleanup_inactive_chats(threshold=threshold, max_age_seconds=max_age_seconds)
+            self.cleanup_inactive_chats(max_age_seconds=max_age_seconds)
 
     async def _periodic_log_task(self, interval_seconds: int):
         """后台日志记录任务的异步函数 (记录历史数据，包含 group_name)"""
@@ -318,7 +318,7 @@ class InterestManager:
                         # 将每个条目作为单独的 JSON 行写入
                         f.write(json.dumps(log_entry, ensure_ascii=False) + '\n')
                         count += 1
-                logger.debug(f"Successfully appended {count} interest history entries to {self._history_log_file_path}")
+                # logger.debug(f"Successfully appended {count} interest history entries to {self._history_log_file_path}")
                 
                 # 注意：不再写入快照文件 interest_log.json
                 # 如果需要快照文件，可以在这里单独写入 self._snapshot_log_file_path
@@ -358,7 +358,6 @@ class InterestManager:
             self._cleanup_task = asyncio.create_task(
                 self._periodic_cleanup_task(
                     interval_seconds=CLEANUP_INTERVAL_SECONDS,
-                    threshold=MIN_INTEREST_THRESHOLD,
                     max_age_seconds=INACTIVE_THRESHOLD_SECONDS
                 )
             )
@@ -449,10 +448,9 @@ class InterestManager:
         else:
             logger.warning(f"尝试降低不存在的聊天流 {stream_id} 的兴趣度")
 
-    def cleanup_inactive_chats(self, threshold=MIN_INTEREST_THRESHOLD, max_age_seconds=INACTIVE_THRESHOLD_SECONDS):
+    def cleanup_inactive_chats(self, max_age_seconds=INACTIVE_THRESHOLD_SECONDS):
         """
         清理长时间不活跃的聊天流记录
-        threshold: 低于此兴趣度的将被清理
         max_age_seconds: 超过此时间未更新的将被清理
         """
         current_time = time.time()

src/plugins/chat_module/heartFC_chat/pf_chatting.py

@@ -0,0 +1,726 @@
+import asyncio
+import time
+import traceback
+from typing import List, Optional, Dict, Any, Deque, Union, TYPE_CHECKING
+from collections import deque
+import json
+
+from ....config.config import global_config
+from ...chat.message import MessageRecv, BaseMessageInfo, MessageThinking, MessageSending
+from ...chat.chat_stream import ChatStream
+from ...message import UserInfo
+from src.heart_flow.heartflow import heartflow, SubHeartflow
+from src.plugins.chat.chat_stream import chat_manager
+from .messagesender import MessageManager
+from src.common.logger import get_module_logger, LogConfig, DEFAULT_CONFIG # 引入 DEFAULT_CONFIG
+from src.plugins.models.utils_model import LLMRequest
+from src.individuality.individuality import Individuality
+
+# 定义日志配置 (使用 loguru 格式)
+interest_log_config = LogConfig(
+    console_format=DEFAULT_CONFIG["console_format"], # 使用默认控制台格式
+    file_format=DEFAULT_CONFIG["file_format"]     # 使用默认文件格式
+)
+logger = get_module_logger("PFChattingLoop", config=interest_log_config) # Logger Name Changed
+
+
+# Forward declaration for type hinting
+if TYPE_CHECKING:
+    from .heartFC_chat import HeartFC_Chat
+
+PLANNER_TOOL_DEFINITION = [
+    {
+        "type": "function",
+        "function": {
+            "name": "decide_reply_action",
+            "description": "根据当前聊天内容和上下文，决定机器人是否应该回复以及如何回复。",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "action": {
+                        "type": "string",
+                        "enum": ["no_reply", "text_reply", "emoji_reply"],
+                        "description": "决定采取的行动：'no_reply'(不回复), 'text_reply'(文本回复) 或 'emoji_reply'(表情回复)。"
+                    },
+                    "reasoning": {
+                         "type": "string",
+                         "description": "做出此决定的简要理由。"
+                     },
+                    "emoji_query": {
+                        "type": "string",
+                        "description": '如果行动是\'emoji_reply\',则指定表情的主题或概念（例如，"开心"、"困惑"）。仅在需要表情回复时提供。'
+                    }
+                },
+                "required": ["action", "reasoning"] # 强制要求提供行动和理由
+            }
+        }
+    }
+]
+
+class PFChatting:
+    """
+    Manages a continuous Plan-Filter-Check (now Plan-Replier-Sender) loop
+    for generating replies within a specific chat stream, controlled by a timer.
+    The loop runs as long as the timer > 0.
+    """
+    def __init__(self, chat_id: str, heartfc_chat_instance: 'HeartFC_Chat'):
+        """
+        初始化PFChatting实例。
+
+        Args:
+            chat_id: The identifier for the chat stream (e.g., stream_id).
+            heartfc_chat_instance: 访问共享资源和方法的主HeartFC_Chat实例。
+        """
+        self.heartfc_chat = heartfc_chat_instance # 访问logger, gpt, tool_user, _send_response_messages等。
+        self.stream_id: str = chat_id
+        self.chat_stream: Optional[ChatStream] = None
+        self.sub_hf: Optional[SubHeartflow] = None
+        self._initialized = False
+        self._init_lock = asyncio.Lock() # Ensure initialization happens only once
+        self._processing_lock = asyncio.Lock() # 确保只有一个 Plan-Replier-Sender 周期在运行
+        self._timer_lock = asyncio.Lock() # 用于安全更新计时器
+
+        self.planner_llm = LLMRequest(
+            model=global_config.llm_normal,
+            temperature=global_config.llm_normal["temp"],
+            max_tokens=1000,
+            request_type="action_planning"
+        )
+
+        # Internal state for loop control
+        self._loop_timer: float = 0.0 # Remaining time for the loop in seconds
+        self._loop_active: bool = False # Is the loop currently running?
+        self._loop_task: Optional[asyncio.Task] = None # Stores the main loop task
+        self._trigger_count_this_activation: int = 0 # Counts triggers within an active period
+
+        # Removed pending_replies as processing is now serial within the loop
+        # self.pending_replies: Dict[str, PendingReply] = {}
+
+
+    async def _initialize(self) -> bool:
+        """
+        Lazy initialization to resolve chat_stream and sub_hf using the provided identifier.
+        Ensures the instance is ready to handle triggers.
+        """
+        async with self._init_lock:
+            if self._initialized:
+                return True
+            try:
+                self.chat_stream = chat_manager.get_stream(self.stream_id)
+
+                if not self.chat_stream:
+                    logger.error(f"PFChatting-{self.stream_id} 获取ChatStream失败。")
+                    return False
+
+                # 子心流(SubHeartflow)可能初始不存在但后续会被创建
+                # 在需要它的方法中应优雅处理其可能缺失的情况
+                self.sub_hf = heartflow.get_subheartflow(self.stream_id)
+                if not self.sub_hf:
+                    logger.warning(f"PFChatting-{self.stream_id} 获取SubHeartflow失败。一些功能可能受限。")
+                    # 决定是否继续初始化。目前允许初始化。
+
+                self._initialized = True
+                logger.info(f"PFChatting-{self.stream_id} 初始化成功。")
+                return True
+            except Exception as e:
+                logger.error(f"PFChatting-{self.stream_id} 初始化失败: {e}")
+                logger.error(traceback.format_exc())
+                return False
+
+    async def add_time(self):
+        """
+        Adds time to the loop timer with decay and starts the loop if it's not active.
+        Called externally (e.g., by HeartFC_Chat) to trigger or extend activity.
+        Durations: 1st trigger = 10s, 2nd = 5s, 3rd+ = 2s.
+        """
+        if not self._initialized:
+             if not await self._initialize():
+                 logger.error(f"PFChatting-{self.stream_id} 无法添加时间: 未初始化。")
+                 return
+
+        async with self._timer_lock:
+            duration_to_add: float = 0.0
+
+            if not self._loop_active: # First trigger for this activation cycle
+                duration_to_add = 10.0
+                self._trigger_count_this_activation = 1 # Start counting for this activation
+                logger.info(f"[{self.stream_id}] First trigger in activation. Adding {duration_to_add:.1f}s.")
+            else: # Loop is already active, apply decay
+                self._trigger_count_this_activation += 1
+                if self._trigger_count_this_activation == 2:
+                    duration_to_add = 5.0
+                    logger.info(f"[{self.stream_id}] 2nd trigger in activation. Adding {duration_to_add:.1f}s.")
+                else: # 3rd trigger or more
+                    duration_to_add = 2.0
+                    logger.info(f"[{self.stream_id}] {self._trigger_count_this_activation}rd/+ trigger in activation. Adding {duration_to_add:.1f}s.")
+
+            new_timer_value = self._loop_timer + duration_to_add
+            self._loop_timer = max(0, new_timer_value) # Ensure timer doesn't go negative conceptually
+            logger.info(f"[{self.stream_id}] Timer is now {self._loop_timer:.1f}s.")
+
+            if not self._loop_active and self._loop_timer > 0:
+                logger.info(f"[{self.stream_id}] Timer > 0 and loop not active. Starting PF loop.")
+                self._loop_active = True
+                # Cancel previous task just in case (shouldn't happen if logic is correct)
+                if self._loop_task and not self._loop_task.done():
+                    logger.warning(f"[{self.stream_id}] Found existing loop task unexpectedly during start. Cancelling it.")
+                    self._loop_task.cancel()
+
+                self._loop_task = asyncio.create_task(self._run_pf_loop())
+                # Add callback to reset state if loop finishes or errors out
+                self._loop_task.add_done_callback(self._handle_loop_completion)
+            elif self._loop_active:
+                 logger.debug(f"[{self.stream_id}] Loop already active. Timer extended.")
+
+
+    def _handle_loop_completion(self, task: asyncio.Task):
+        """Callback executed when the _run_pf_loop task finishes."""
+        try:
+            # Check if the task raised an exception
+            exception = task.exception()
+            if exception:
+                logger.error(f"[{self.stream_id}] PF loop task completed with error: {exception}")
+                logger.error(traceback.format_exc())
+            else:
+                logger.info(f"[{self.stream_id}] PF loop task completed normally (timer likely expired or cancelled).")
+        except asyncio.CancelledError:
+            logger.info(f"[{self.stream_id}] PF loop task was cancelled.")
+        finally:
+            # Reset state regardless of how the task finished
+            self._loop_active = False
+            self._loop_task = None
+            # Ensure lock is released if the loop somehow exited while holding it
+            if self._processing_lock.locked():
+                logger.warning(f"[{self.stream_id}] Releasing processing lock after loop task completion.")
+                self._processing_lock.release()
+            logger.info(f"[{self.stream_id}] Loop state reset.")
+
+
+    async def _run_pf_loop(self):
+        """
+        主循环，当计时器>0时持续进行计划并可能回复消息
+        管理每个循环周期的处理锁
+        """
+        logger.info(f"[{self.stream_id}] 开始执行PF循环")
+        try:
+            while True:
+                # 使用计时器锁安全地检查当前计时器值
+                async with self._timer_lock:
+                    current_timer = self._loop_timer
+                    if current_timer <= 0:
+                        logger.info(f"[{self.stream_id}] 计时器为零或负数({current_timer:.1f}秒)，退出PF循环")
+                        break  # 退出条件：计时器到期
+
+                # 记录循环开始时间
+                loop_cycle_start_time = time.monotonic()
+                # 标记本周期是否执行了操作
+                action_taken_this_cycle = False  
+
+                # 获取处理锁，确保每个计划-回复-发送周期独占执行
+                acquired_lock = False
+                try:
+                    await self._processing_lock.acquire()
+                    acquired_lock = True
+                    logger.debug(f"[{self.stream_id}] 循环获取到处理锁")
+
+                    # --- Planner ---
+                    # Planner decides action, reasoning, emoji_query, etc.
+                    planner_result = await self._planner() # Modify planner to return decision dict
+                    action = planner_result.get("action", "error")
+                    reasoning = planner_result.get("reasoning", "Planner did not provide reasoning.")
+                    emoji_query = planner_result.get("emoji_query", "")
+                    current_mind = planner_result.get("current_mind", "[Mind unavailable]")
+                    send_emoji_from_tools = planner_result.get("send_emoji_from_tools", "")
+                    observed_messages = planner_result.get("observed_messages", []) # Planner needs to return this
+
+                    if action == "text_reply":
+                        logger.info(f"[{self.stream_id}] 计划循环决定: 回复文本.")
+                        action_taken_this_cycle = True
+                        # --- 回复器 ---
+                        anchor_message = await self._get_anchor_message(observed_messages)
+                        if not anchor_message:
+                             logger.error(f"[{self.stream_id}] 循环: 无法获取锚点消息用于回复. 跳过周期.")
+                        else:
+                            thinking_id = await self.heartfc_chat._create_thinking_message(anchor_message)
+                            if not thinking_id:
+                                logger.error(f"[{self.stream_id}] 循环: 无法创建思考ID. 跳过周期.")
+                            else:
+                                replier_result = None
+                                try:
+                                    # 直接 await 回复器工作
+                                    replier_result = await self._replier_work(
+                                        observed_messages=observed_messages,
+                                        anchor_message=anchor_message,
+                                        thinking_id=thinking_id,
+                                        current_mind=current_mind,
+                                        send_emoji=send_emoji_from_tools
+                                    )
+                                except Exception as e_replier:
+                                     logger.error(f"[{self.stream_id}] 循环: 回复器工作失败: {e_replier}")
+                                     self._cleanup_thinking_message(thinking_id) # 清理思考消息
+                                     # 继续循环, 视为非操作周期
+
+                                if replier_result:
+                                    # --- Sender ---
+                                    try:
+                                        await self._sender(thinking_id, anchor_message, replier_result)
+                                        logger.info(f"[{self.stream_id}] 循环: 发送器完成成功.")
+                                    except Exception as e_sender:
+                                         logger.error(f"[{self.stream_id}] 循环: 发送器失败: {e_sender}")
+                                         self._cleanup_thinking_message(thinking_id) # 确保发送失败时清理
+                                         # 继续循环, 视为非操作周期
+                                else:
+                                     # Replier failed to produce result
+                                     logger.warning(f"[{self.stream_id}] 循环: 回复器未产生结果. 跳过发送.")
+                                     self._cleanup_thinking_message(thinking_id) # 清理思考消息
+
+                    elif action == "emoji_reply":
+                        logger.info(f"[{self.stream_id}] 计划循环决定: 回复表情 ('{emoji_query}').")
+                        action_taken_this_cycle = True
+                        anchor = await self._get_anchor_message(observed_messages)
+                        if anchor:
+                            try:
+                                await self.heartfc_chat._handle_emoji(anchor, [], emoji_query)
+                            except Exception as e_emoji:
+                                logger.error(f"[{self.stream_id}] 循环: 发送表情失败: {e_emoji}")
+                        else:
+                             logger.warning(f"[{self.stream_id}] 循环: 无法发送表情, 无法获取锚点.")
+
+                    elif action == "no_reply":
+                        logger.info(f"[{self.stream_id}] 计划循环决定: 不回复. 原因: {reasoning}")
+                        # Do nothing else, action_taken_this_cycle remains False
+
+                    elif action == "error":
+                         logger.error(f"[{self.stream_id}] 计划循环返回错误或失败. 原因: {reasoning}")
+                         # 视为非操作周期
+
+                    else: # Unknown action
+                         logger.warning(f"[{self.stream_id}] 计划循环返回未知动作: {action}. 视为不回复.")
+                         # 视为非操作周期
+
+                except Exception as e_cycle:
+                    # Catch errors occurring within the locked section (e.g., planner crash)
+                    logger.error(f"[{self.stream_id}] 循环周期执行时发生错误: {e_cycle}")
+                    logger.error(traceback.format_exc())
+                    # Ensure lock is released if an error occurs before the finally block
+                    if acquired_lock and self._processing_lock.locked():
+                         self._processing_lock.release()
+                         acquired_lock = False # 防止在 finally 块中重复释放
+                         logger.warning(f"[{self.stream_id}] 由于循环周期中的错误释放了处理锁.")
+
+                finally:
+                    # Ensure the lock is always released after a cycle
+                    if acquired_lock:
+                        self._processing_lock.release()
+                        logger.debug(f"[{self.stream_id}] 循环释放了处理锁.")
+
+                # --- Timer Decrement ---
+                cycle_duration = time.monotonic() - loop_cycle_start_time
+                async with self._timer_lock:
+                    self._loop_timer -= cycle_duration
+                    logger.debug(f"[{self.stream_id}] 循环周期耗时 {cycle_duration:.2f}s. 计时器剩余: {self._loop_timer:.1f}s.")
+
+                # --- Delay ---
+                # Add a small delay, especially if no action was taken, to prevent busy-waiting
+                try:
+                    if not action_taken_this_cycle and cycle_duration < 1.5:
+                         # If nothing happened and cycle was fast, wait a bit longer
+                         await asyncio.sleep(1.5 - cycle_duration)
+                    elif cycle_duration < 0.2: # Minimum delay even if action was taken
+                         await asyncio.sleep(0.2)
+                except asyncio.CancelledError:
+                    logger.info(f"[{self.stream_id}] Sleep interrupted, likely loop cancellation.")
+                    break # Exit loop if cancelled during sleep
+
+        except asyncio.CancelledError:
+             logger.info(f"[{self.stream_id}] PF loop task received cancellation request.")
+        except Exception as e_loop_outer:
+             # Catch errors outside the main cycle lock (should be rare)
+             logger.error(f"[{self.stream_id}] PF loop encountered unexpected outer error: {e_loop_outer}")
+             logger.error(traceback.format_exc())
+        finally:
+            # Reset trigger count when loop finishes
+            async with self._timer_lock:
+                self._trigger_count_this_activation = 0
+                logger.debug(f"[{self.stream_id}] Trigger count reset to 0 as loop finishes.")
+            logger.info(f"[{self.stream_id}] PF loop finished execution run.")
+            # State reset (_loop_active, _loop_task) is handled by _handle_loop_completion callback
+
+    async def _planner(self) -> Dict[str, Any]:
+        """
+        规划器 (Planner): 使用LLM根据上下文决定是否和如何回复。
+        Returns a dictionary containing the decision and context.
+        {'action': str, 'reasoning': str, 'emoji_query': str, 'current_mind': str,
+         'send_emoji_from_tools': str, 'observed_messages': List[dict]}
+        """
+        observed_messages: List[dict] = []
+        tool_result_info = {}
+        get_mid_memory_id = []
+        send_emoji_from_tools = "" # Renamed for clarity
+        current_mind: Optional[str] = None
+
+        # --- 获取最新的观察信息 ---
+        try:
+            if self.sub_hf and self.sub_hf._get_primary_observation():
+                observation = self.sub_hf._get_primary_observation()
+                logger.debug(f"[{self.stream_id}][Planner] 调用 observation.observe()...")
+                await observation.observe() # 主动观察以获取最新消息
+                observed_messages = observation.talking_message # 获取更新后的消息列表
+                logger.debug(f"[{self.stream_id}][Planner] 获取到 {len(observed_messages)} 条观察消息。")
+            else:
+                logger.warning(f"[{self.stream_id}][Planner] 无法获取 SubHeartflow 或 Observation 来获取消息。")
+        except Exception as e:
+            logger.error(f"[{self.stream_id}][Planner] 获取观察信息时出错: {e}")
+            logger.error(traceback.format_exc())
+        # --- 结束获取观察信息 ---
+
+        # --- (Moved from _replier_work) 1. 思考前使用工具 ---
+        try:
+            observation_context_text = ""
+            if observed_messages:
+                context_texts = [msg.get('detailed_plain_text', '') for msg in observed_messages if msg.get('detailed_plain_text')]
+                observation_context_text = "\n".join(context_texts)
+                logger.debug(f"[{self.stream_id}][Planner] Context for tools: {observation_context_text[:100]}...")
+
+            if observation_context_text and self.sub_hf:
+                 # Ensure SubHeartflow exists for tool use context
+                tool_result = await self.heartfc_chat.tool_user.use_tool(
+                    message_txt=observation_context_text,
+                    chat_stream=self.chat_stream,
+                    sub_heartflow=self.sub_hf
+                )
+                if tool_result.get("used_tools", False):
+                    tool_result_info = tool_result.get("structured_info", {})
+                    logger.debug(f"[{self.stream_id}][Planner] Tool results: {tool_result_info}")
+                    if "mid_chat_mem" in tool_result_info:
+                        get_mid_memory_id = [mem["content"] for mem in tool_result_info["mid_chat_mem"] if "content" in mem]
+                    if "send_emoji" in tool_result_info and tool_result_info["send_emoji"]:
+                        send_emoji_from_tools = tool_result_info["send_emoji"][0].get("content", "") # Use renamed var
+            elif not self.sub_hf:
+                 logger.warning(f"[{self.stream_id}][Planner] Skipping tool use because SubHeartflow is not available.")
+
+        except Exception as e_tool:
+            logger.error(f"[PFChatting-{self.stream_id}][Planner] Tool use failed: {e_tool}")
+            # Continue even if tool use fails
+        # --- 结束工具使用 ---
+
+        # 心流思考，然后plan
+        try:
+            if self.sub_hf:
+                # Ensure arguments match the current do_thinking_before_reply signature
+                current_mind, past_mind = await self.sub_hf.do_thinking_before_reply(
+                    chat_stream=self.chat_stream,
+                    extra_info=tool_result_info,
+                    obs_id=get_mid_memory_id,
+                )
+                logger.info(f"[{self.stream_id}][Planner] SubHeartflow thought: {current_mind}")
+            else:
+                 logger.warning(f"[{self.stream_id}][Planner] Skipping SubHeartflow thinking because it is not available.")
+                 current_mind = "[心流思考不可用]" # Set a default/indicator value
+
+        except Exception as e_shf:
+             logger.error(f"[PFChatting-{self.stream_id}][Planner] SubHeartflow thinking failed: {e_shf}")
+             logger.error(traceback.format_exc())
+             current_mind = "[心流思考出错]" 
+
+
+        # --- 使用 LLM 进行决策 ---
+        action = "no_reply" # Default action
+        emoji_query = ""
+        reasoning = "默认决策或获取决策失败"
+        llm_error = False # Flag for LLM failure
+
+        try:
+            # 构建提示 (Now includes current_mind)
+            prompt = self._build_planner_prompt(observed_messages, current_mind)
+            logger.trace(f"[{self.stream_id}][Planner] Prompt: {prompt}")
+
+            # 准备 LLM 请求 Payload
+            payload = {
+                "model": self.planner_llm.model_name,
+                "messages": [{"role": "user", "content": prompt}],
+                "tools": PLANNER_TOOL_DEFINITION,
+                "tool_choice": {"type": "function", "function": {"name": "decide_reply_action"}}, # 强制调用此工具
+            }
+
+            logger.debug(f"[{self.stream_id}][Planner] 发送 Planner LLM 请求...")
+            # 调用 LLM
+            response = await self.planner_llm._execute_request(
+                endpoint="/chat/completions", payload=payload, prompt=prompt
+            )
+
+            # 解析 LLM 响应
+            if len(response) == 3: # 期望返回 content, reasoning_content, tool_calls
+                _, _, tool_calls = response
+                if tool_calls and isinstance(tool_calls, list) and len(tool_calls) > 0:
+                    # 通常强制调用后只会有一个 tool_call
+                    tool_call = tool_calls[0]
+                    if tool_call.get("type") == "function" and tool_call.get("function", {}).get("name") == "decide_reply_action":
+                        try:
+                            arguments = json.loads(tool_call["function"]["arguments"])
+                            action = arguments.get("action", "no_reply")
+                            reasoning = arguments.get("reasoning", "未提供理由")
+                            if action == "emoji_reply":
+                                # Planner's decision overrides tool's emoji if action is emoji_reply
+                                emoji_query = arguments.get("emoji_query", send_emoji_from_tools) # Use tool emoji as default if planner asks for emoji
+                            logger.info(f"[{self.stream_id}][Planner] LLM 决策: {action}, 理由: {reasoning}, EmojiQuery: '{emoji_query}'")
+                        except json.JSONDecodeError as json_e:
+                            logger.error(f"[{self.stream_id}][Planner] 解析工具参数失败: {json_e}. Arguments: {tool_call['function'].get('arguments')}")
+                            action = "error"; reasoning = "工具参数解析失败"; llm_error = True
+                        except Exception as parse_e:
+                             logger.error(f"[{self.stream_id}][Planner] 处理工具参数时出错: {parse_e}")
+                             action = "error"; reasoning = "处理工具参数时出错"; llm_error = True
+                    else:
+                        logger.warning(f"[{self.stream_id}][Planner] LLM 未按预期调用 'decide_reply_action' 工具。Tool calls: {tool_calls}")
+                        action = "error"; reasoning = "LLM未调用预期工具"; llm_error = True
+                else:
+                     logger.warning(f"[{self.stream_id}][Planner] LLM 响应中未包含有效的工具调用。Tool calls: {tool_calls}")
+                     action = "error"; reasoning = "LLM响应无工具调用"; llm_error = True
+            else:
+                logger.warning(f"[{self.stream_id}][Planner] LLM 未返回预期的工具调用响应。Response parts: {len(response)}")
+                action = "error"; reasoning = "LLM响应格式错误"; llm_error = True
+
+        except Exception as llm_e:
+            logger.error(f"[{self.stream_id}][Planner] Planner LLM 调用失败: {llm_e}")
+            logger.error(traceback.format_exc())
+            action = "error"; reasoning = f"LLM 调用失败: {llm_e}"; llm_error = True
+
+        # --- 返回决策结果 ---
+        # Note: Lock release is handled by the loop now
+        return {
+            "action": action,
+            "reasoning": reasoning,
+            "emoji_query": emoji_query, # Specific query if action is emoji_reply
+            "current_mind": current_mind,
+            "send_emoji_from_tools": send_emoji_from_tools, # Emoji suggested by pre-thinking tools
+            "observed_messages": observed_messages,
+            "llm_error": llm_error # Indicate if LLM decision process failed
+        }
+
+    async def _get_anchor_message(self, observed_messages: List[dict]) -> Optional[MessageRecv]:
+        """
+        重构观察到的最后一条消息作为回复的锚点，
+        如果重构失败或观察为空，则创建一个占位符。
+        """
+        if not self.chat_stream:
+            logger.error(f"[PFChatting-{self.stream_id}] 无法获取锚点消息: ChatStream 不可用.")
+            return None
+
+        try:
+            last_msg_dict = None
+            if observed_messages:
+                last_msg_dict = observed_messages[-1]
+
+            if last_msg_dict:
+                try:
+                    # Attempt reconstruction from the last observed message dictionary
+                    anchor_message = MessageRecv(last_msg_dict, chat_stream=self.chat_stream)
+                    # Basic validation
+                    if not (anchor_message and anchor_message.message_info and anchor_message.message_info.message_id and anchor_message.message_info.user_info):
+                        raise ValueError("重构的 MessageRecv 缺少必要信息.")
+                    logger.debug(f"[{self.stream_id}] 重构的锚点消息: ID={anchor_message.message_info.message_id}")
+                    return anchor_message
+                except Exception as e_reconstruct:
+                    logger.warning(f"[{self.stream_id}] 从观察到的消息重构 MessageRecv 失败: {e_reconstruct}. 创建占位符.")
+            else:
+                logger.warning(f"[{self.stream_id}] observed_messages 为空. 创建占位符锚点消息.")
+
+            # --- Create Placeholder ---
+            placeholder_id = f"mid_pf_{int(time.time() * 1000)}"
+            placeholder_user = UserInfo(user_id="system_trigger", user_nickname="System Trigger", platform=self.chat_stream.platform)
+            placeholder_msg_info = BaseMessageInfo(
+                message_id=placeholder_id,
+                platform=self.chat_stream.platform,
+                group_info=self.chat_stream.group_info,
+                user_info=placeholder_user,
+                time=time.time()
+            )
+            placeholder_msg_dict = {
+                "message_info": placeholder_msg_info.to_dict(),
+                "processed_plain_text": "[System Trigger Context]", # Placeholder text
+                "raw_message": "",
+                "time": placeholder_msg_info.time,
+            }
+            anchor_message = MessageRecv(placeholder_msg_dict)
+            anchor_message.update_chat_stream(self.chat_stream) # Associate with the stream
+            logger.info(f"[{self.stream_id}] Created placeholder anchor message: ID={anchor_message.message_info.message_id}")
+            return anchor_message
+
+        except Exception as e:
+            logger.error(f"[PFChatting-{self.stream_id}] Error getting/creating anchor message: {e}")
+            logger.error(traceback.format_exc())
+            return None
+
+    def _cleanup_thinking_message(self, thinking_id: str):
+        """Safely removes the thinking message."""
+        try:
+            container = MessageManager().get_container(self.stream_id)
+            container.remove_message(thinking_id, msg_type=MessageThinking)
+            logger.debug(f"[{self.stream_id}] Cleaned up thinking message {thinking_id}.")
+        except Exception as e:
+            logger.error(f"[{self.stream_id}] Error cleaning up thinking message {thinking_id}: {e}")
+
+
+    async def _sender(self, thinking_id: str, anchor_message: MessageRecv, replier_result: Dict[str, Any]):
+        """
+        发送器 (Sender): 使用HeartFC_Chat的方法发送生成的回复。
+        被 _run_pf_loop 直接调用和 await。
+        也处理相关的操作，如发送表情和更新关系。
+        Raises exception on failure to signal the loop.
+        """
+        # replier_result should contain 'response_set' and 'send_emoji'
+        response_set = replier_result.get("response_set")
+        send_emoji = replier_result.get("send_emoji", "") # Emoji determined by tools, passed via replier
+
+        if not response_set:
+             logger.error(f"[PFChatting-{self.stream_id}][Sender-{thinking_id}] Called with empty response_set.")
+             # Clean up thinking message before raising error
+             self._cleanup_thinking_message(thinking_id)
+             raise ValueError("Sender called with no response_set") # Signal failure to loop
+
+        first_bot_msg: Optional[MessageSending] = None
+        send_success = False
+        try:
+            # --- Send the main text response ---
+            logger.debug(f"[{self.stream_id}][Sender-{thinking_id}] Sending response messages...")
+            # This call implicitly handles replacing the MessageThinking with MessageSending/MessageSet
+            first_bot_msg = await self.heartfc_chat._send_response_messages(anchor_message, response_set, thinking_id)
+
+            if first_bot_msg:
+                send_success = True # Mark success
+                logger.info(f"[PFChatting-{self.stream_id}][Sender-{thinking_id}] Successfully sent reply.")
+
+                # --- Handle associated emoji (if determined by tools) ---
+                if send_emoji:
+                    logger.info(f"[PFChatting-{self.stream_id}][Sender-{thinking_id}] Sending associated emoji: {send_emoji}")
+                    try:
+                        # Use first_bot_msg as anchor if available, otherwise fallback to original anchor
+                        emoji_anchor = first_bot_msg if first_bot_msg else anchor_message
+                        await self.heartfc_chat._handle_emoji(emoji_anchor, response_set, send_emoji)
+                    except Exception as e_emoji:
+                        logger.error(f"[PFChatting-{self.stream_id}][Sender-{thinking_id}] Failed to send associated emoji: {e_emoji}")
+                        # Log error but don't fail the whole send process for emoji failure
+
+                # --- Update relationship ---
+                try:
+                    await self.heartfc_chat._update_relationship(anchor_message, response_set)
+                    logger.debug(f"[PFChatting-{self.stream_id}][Sender-{thinking_id}] Updated relationship.")
+                except Exception as e_rel:
+                    logger.error(f"[PFChatting-{self.stream_id}][Sender-{thinking_id}] Failed to update relationship: {e_rel}")
+                    # Log error but don't fail the whole send process for relationship update failure
+
+            else:
+                 # Sending failed (e.g., _send_response_messages found thinking message already gone)
+                 send_success = False
+                 logger.warning(f"[PFChatting-{self.stream_id}][Sender-{thinking_id}] Failed to send reply (maybe thinking message expired or was removed?).")
+                 # No need to clean up thinking message here, _send_response_messages implies it's gone or handled
+                 raise RuntimeError("Sending reply failed, _send_response_messages returned None.") # Signal failure
+
+
+        except Exception as e:
+            # Catch potential errors during sending or post-send actions
+            logger.error(f"[PFChatting-{self.stream_id}][Sender-{thinking_id}] Error during sending process: {e}")
+            logger.error(traceback.format_exc())
+            # Ensure thinking message is cleaned up if send failed mid-way and wasn't handled
+            if not send_success:
+                self._cleanup_thinking_message(thinking_id)
+            raise # Re-raise the exception to signal failure to the loop
+
+        # No finally block needed for lock management
+
+
+    async def shutdown(self):
+        """
+        Gracefully shuts down the PFChatting instance by cancelling the active loop task.
+        """
+        logger.info(f"[{self.stream_id}] Shutting down PFChatting...")
+        if self._loop_task and not self._loop_task.done():
+            logger.info(f"[{self.stream_id}] Cancelling active PF loop task.")
+            self._loop_task.cancel()
+            try:
+                # Wait briefly for the task to acknowledge cancellation
+                await asyncio.wait_for(self._loop_task, timeout=5.0)
+            except asyncio.CancelledError:
+                 logger.info(f"[{self.stream_id}] PF loop task cancelled successfully.")
+            except asyncio.TimeoutError:
+                 logger.warning(f"[{self.stream_id}] Timeout waiting for PF loop task cancellation.")
+            except Exception as e:
+                 logger.error(f"[{self.stream_id}] Error during loop task cancellation: {e}")
+        else:
+             logger.info(f"[{self.stream_id}] No active PF loop task found to cancel.")
+
+        # Ensure loop state is reset even if task wasn't running or cancellation failed
+        self._loop_active = False
+        self._loop_task = None
+
+        # Double-check lock state (should be released by loop completion/cancellation handler)
+        if self._processing_lock.locked():
+            logger.warning(f"[{self.stream_id}] Releasing processing lock during shutdown.")
+            self._processing_lock.release()
+
+        logger.info(f"[{self.stream_id}] PFChatting shutdown complete.")
+
+    def _build_planner_prompt(self, observed_messages: List[dict], current_mind: Optional[str]) -> str:
+        """构建 Planner LLM 的提示词 (现在包含 current_mind)"""
+        prompt = "你是一个聊天机器人助手，正在决定是否以及如何回应当前的聊天。\n"
+        prompt += f"你的名字是 {global_config.BOT_NICKNAME}。\n"
+
+        # Add current mind state if available
+        if current_mind:
+             prompt += f"\n你当前的内部想法是：\n---\n{current_mind}\n---\n\n"
+        else:
+             prompt += "\n你当前没有特别的内部想法。\n"
+
+        if observed_messages:
+            context_text = "\n".join([msg.get('detailed_plain_text', '') for msg in observed_messages if msg.get('detailed_plain_text')])
+            prompt += "观察到的最新聊天内容如下：\n---\n"
+            prompt += context_text[:1500] # Limit context length
+            prompt += "\n---\n"
+        else:
+            prompt += "当前没有观察到新的聊天内容。\n"
+
+        prompt += "\n请结合你的内部想法和观察到的聊天内容，分析情况并使用 'decide_reply_action' 工具来决定你的最终行动。\n"
+        prompt += "决策依据：\n"
+        prompt += "1. 如果聊天内容无聊、与你无关、或者你的内部想法认为不适合回复，选择 'no_reply'。\n"
+        prompt += "2. 如果聊天内容值得回应，且适合用文字表达（参考你的内部想法），选择 'text_reply'。\n"
+        prompt += "3. 如果聊天内容或你的内部想法适合用一个表情来回应，选择 'emoji_reply' 并提供表情主题 'emoji_query'。\n"
+        prompt += "必须调用 'decide_reply_action' 工具并提供 'action' 和 'reasoning'。"
+
+        return prompt 
+
+    # --- 回复器 (Replier) 的定义 --- #
+    async def _replier_work(self, observed_messages: List[dict], anchor_message: MessageRecv, thinking_id: str, current_mind: Optional[str], send_emoji: str) -> Optional[Dict[str, Any]]:
+        """
+        回复器 (Replier): 核心逻辑用于生成回复。
+        被 _run_pf_loop 直接调用和 await。
+        Returns dict with 'response_set' and 'send_emoji' or None on failure.
+        """
+        response_set: Optional[List[str]] = None
+        try:
+            # --- Tool Use and SubHF Thinking are now in _planner ---
+
+            # --- Generate Response with LLM ---
+            logger.debug(f"[{self.stream_id}][Replier-{thinking_id}] Calling LLM to generate response...")
+            # 注意：实际的生成调用是在 self.heartfc_chat.gpt.generate_response 中
+            response_set = await self.heartfc_chat.gpt.generate_response(
+                anchor_message,
+                thinking_id
+                # current_mind 不再直接传递给 gpt.generate_response，
+                # 因为 generate_response 内部会通过 thinking_id 或其他方式获取所需上下文
+            )
+
+            if not response_set:
+                logger.warning(f"[{self.stream_id}][Replier-{thinking_id}] LLM生成了一个空回复集。")
+                return None # Indicate failure
+
+            # --- 准备并返回结果 ---
+            logger.info(f"[{self.stream_id}][Replier-{thinking_id}] 成功生成了回复集: {' '.join(response_set)[:50]}...")
+            return {
+                "response_set": response_set,
+                "send_emoji": send_emoji, # Pass through the emoji determined earlier (usually by tools)
+            }
+
+        except Exception as e:
+            logger.error(f"[PFChatting-{self.stream_id}][Replier-{thinking_id}] Unexpected error in replier_work: {e}")
+            logger.error(traceback.format_exc())
+            return None # Indicate failure 

src/plugins/chat_module/heartFC_chat/pfchating.md

@@ -0,0 +1,22 @@
+新写一个类，叫做pfchating
+这个类初始化时会输入一个chat_stream或者stream_id
+这个类会包含对应的sub_hearflow和一个chat_stream
+
+pfchating有以下几个组成部分：
+规划器：决定是否要进行回复（根据sub_heartflow中的observe内容），可以选择不回复，回复文字或者回复表情包，你可以使用llm的工具调用来实现
+回复器：可以根据信息产生回复，这部分代码将大部分与trigger_reply_generation(stream_id, observed_messages)一模一样
+（回复器可能同时运行多个(0-3个)，这些回复器会根据不同时刻的规划器产生不同回复
+检查器：由于生成回复需要时间，检查器会检查在有了新的消息内容之后，回复是否还适合，如果合适就转给发送器
+如果一条消息被发送了，其他回复在检查时也要增加这条消息的信息，防止重复发送内容相近的回复
+发送器，将回复发送到聊天，这部分主体不需要再pfcchating中实现，只需要使用原有的self._send_response_messages(anchor_message, response_set, thinking_id)
+
+
+当_process_triggered_reply(self, stream_id: str, observed_messages: List[dict]):触发时，并不会单独进行一次回复
+
+
+问题：
+1.每个pfchating是否对应一个caht_stream，是否是唯一的？(fix)
+2.observe_text传入进来是纯str，是不是应该传进来message构成的list?(fix)
+3.检查失败的回复应该怎么处理？(先抛弃)
+4.如何比较相似度？
+5.planner怎么写？（好像可以先不加入这部分）