From a2333f9f82b346f61a3b31db11794f7db5fe650c Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Thu, 17 Apr 2025 16:51:35 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E5=AE=8C=E5=85=A8=E5=88=86=E7=A6=BB?= =?UTF-8?q?=E5=9B=9E=E5=A4=8D=20=E5=85=B4=E8=B6=A3=E5=92=8C=20=E6=B6=88?= =?UTF-8?q?=E6=81=AF=E9=98=85=E8=AF=BB=EF=BC=9B=E6=B7=BB=E5=8A=A0=E6=A6=82?= =?UTF-8?q?=E7=8E=87=E5=9B=9E=E5=A4=8D=E6=9C=BA=E5=88=B6=EF=BC=8C=E4=BC=98?= =?UTF-8?q?=E5=8C=96=E5=85=B4=E8=B6=A3=E7=9B=91=E6=8E=A7=E9=80=BB=E8=BE=91?= =?UTF-8?q?=EF=BC=8C=E9=87=8D=E6=9E=84=E7=9B=B8=E5=85=B3=E5=8A=9F=E8=83=BD?= =?UTF-8?q?=E4=BB=A5=E6=94=AF=E6=8C=81=E6=9B=B4=E7=81=B5=E6=B4=BB=E7=9A=84?= =?UTF-8?q?=E5=9B=9E=E5=A4=8D=E8=A7=A6=E5=8F=91=E6=9D=A1=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- interest_monitor_gui.py | 106 ++++- src/do_tool/tool_use.py | 39 +- src/heart_flow/sub_heartflow.py | 274 +++++++----- .../heartFC_chat/heartFC__generator.py | 46 +- .../chat_module/heartFC_chat/heartFC_chat.py | 413 +++++++++++------- .../heartFC_chat/heartFC_processor.py | 2 +- .../chat_module/heartFC_chat/interest.py | 226 +++++++--- 7 files changed, 730 insertions(+), 376 deletions(-) diff --git a/interest_monitor_gui.py b/interest_monitor_gui.py index e28b6f7ac..147c3635c 100644 --- a/interest_monitor_gui.py +++ b/interest_monitor_gui.py @@ -2,7 +2,7 @@ import tkinter as tk from tkinter import ttk import time import os -from datetime import datetime +from datetime import datetime, timedelta import random from collections import deque import json # 引入 json @@ -37,24 +37,59 @@ class InterestMonitorApp: # 使用 deque 来存储有限的历史数据点 # key: stream_id, value: deque([(timestamp, interest_level), ...]) self.stream_history = {} + # key: stream_id, value: deque([(timestamp, reply_probability), ...]) # <--- 新增：存储概率历史 + self.probability_history = {} self.stream_colors = {} # 为每个 stream 分配颜色 self.stream_display_names = {} # *** New: Store display names (group_name) *** + self.selected_stream_id = tk.StringVar() # 用于 Combobox 绑定 # --- UI 元素 --- + # 创建 Notebook (选项卡控件) + self.notebook = ttk.Notebook(root) + self.notebook.pack(pady=10, padx=10, fill=tk.BOTH, expand=1) + + # --- 第一个选项卡：所有流 --- + self.frame_all = ttk.Frame(self.notebook, padding="5 5 5 5") + self.notebook.add(self.frame_all, text='所有聊天流') + # 状态标签 self.status_label = tk.Label(root, text="Initializing...", anchor="w", fg="grey") self.status_label.pack(side=tk.BOTTOM, fill=tk.X, padx=5, pady=2) - # Matplotlib 图表设置 + # Matplotlib 图表设置 (用于第一个选项卡) self.fig = Figure(figsize=(5, 4), dpi=100) self.ax = self.fig.add_subplot(111) # 配置在 update_plot 中进行，避免重复 - # 创建 Tkinter 画布嵌入 Matplotlib 图表 - self.canvas = FigureCanvasTkAgg(self.fig, master=root) + # 创建 Tkinter 画布嵌入 Matplotlib 图表 (用于第一个选项卡) + self.canvas = FigureCanvasTkAgg(self.fig, master=self.frame_all) # <--- 放入 frame_all self.canvas_widget = self.canvas.get_tk_widget() self.canvas_widget.pack(side=tk.TOP, fill=tk.BOTH, expand=1) + # --- 第二个选项卡：单个流 --- + self.frame_single = ttk.Frame(self.notebook, padding="5 5 5 5") + self.notebook.add(self.frame_single, text='单个聊天流详情') + + # 单个流选项卡的上部控制区域 + self.control_frame_single = ttk.Frame(self.frame_single) + self.control_frame_single.pack(side=tk.TOP, fill=tk.X, pady=5) + + ttk.Label(self.control_frame_single, text="选择聊天流:").pack(side=tk.LEFT, padx=(0, 5)) + self.stream_selector = ttk.Combobox(self.control_frame_single, textvariable=self.selected_stream_id, state="readonly", width=50) + self.stream_selector.pack(side=tk.LEFT, fill=tk.X, expand=True) + self.stream_selector.bind("<>", self.on_stream_selected) + + # Matplotlib 图表设置 (用于第二个选项卡) + self.fig_single = Figure(figsize=(5, 4), dpi=100) + # 修改：创建两个子图，一个显示兴趣度，一个显示概率 + self.ax_single_interest = self.fig_single.add_subplot(211) # 2行1列的第1个 + self.ax_single_probability = self.fig_single.add_subplot(212, sharex=self.ax_single_interest) # 2行1列的第2个，共享X轴 + + # 创建 Tkinter 画布嵌入 Matplotlib 图表 (用于第二个选项卡) + self.canvas_single = FigureCanvasTkAgg(self.fig_single, master=self.frame_single) # <--- 放入 frame_single + self.canvas_widget_single = self.canvas_single.get_tk_widget() + self.canvas_widget_single.pack(side=tk.TOP, fill=tk.BOTH, expand=1) + # --- 初始化和启动刷新 --- self.update_display() # 首次加载并开始刷新循环 @@ -72,6 +107,7 @@ class InterestMonitorApp: # *** Reset display names each time we reload *** new_stream_history = {} new_stream_display_names = {} + new_probability_history = {} # <--- 重置概率历史 read_count = 0 error_count = 0 # *** Calculate the timestamp threshold for the last 30 minutes *** @@ -93,6 +129,7 @@ class InterestMonitorApp: stream_id = log_entry.get("stream_id") interest_level = log_entry.get("interest_level") group_name = log_entry.get("group_name", stream_id) # *** Get group_name, fallback to stream_id *** + reply_probability = log_entry.get("reply_probability") # <--- 获取概率值 # *** Check other required fields AFTER time filtering *** if stream_id is None or interest_level is None: @@ -102,6 +139,7 @@ class InterestMonitorApp: # 如果是第一次读到这个 stream_id，则创建 deque if stream_id not in new_stream_history: new_stream_history[stream_id] = deque(maxlen=MAX_HISTORY_POINTS) + new_probability_history[stream_id] = deque(maxlen=MAX_HISTORY_POINTS) # <--- 创建概率 deque # 检查是否已有颜色，没有则分配 if stream_id not in self.stream_colors: self.stream_colors[stream_id] = self.get_random_color() @@ -111,6 +149,13 @@ class InterestMonitorApp: # 添加数据点 new_stream_history[stream_id].append((float(timestamp), float(interest_level))) + # 添加概率数据点 (如果存在) + if reply_probability is not None: + try: + new_probability_history[stream_id].append((float(timestamp), float(reply_probability))) + except (TypeError, ValueError): + # 如果概率值无效，可以跳过或记录一个默认值，这里跳过 + pass except json.JSONDecodeError: error_count += 1 @@ -124,6 +169,7 @@ class InterestMonitorApp: # 读取完成后，用新数据替换旧数据 self.stream_history = new_stream_history self.stream_display_names = new_stream_display_names # *** Update display names *** + self.probability_history = new_probability_history # <--- 更新概率历史 status_msg = f"Data loaded at {datetime.now().strftime('%H:%M:%S')}. Lines read: {read_count}." if error_count > 0: status_msg += f" Skipped {error_count} invalid lines." @@ -136,12 +182,39 @@ class InterestMonitorApp: except Exception as e: self.set_status(f"An unexpected error occurred during loading: {e}", "red") + # --- 更新 Combobox --- + self.update_stream_selector() - def update_plot(self): - """更新 Matplotlib 图表""" + def update_stream_selector(self): + """更新单个流选项卡中的 Combobox 列表""" + # 创建 (display_name, stream_id) 对的列表，按 display_name 排序 + available_streams = sorted( + [(name, sid) for sid, name in self.stream_display_names.items() if sid in self.stream_history and self.stream_history[sid]], + key=lambda item: item[0] # 按显示名称排序 + ) + + # 更新 Combobox 的值 (仅显示 display_name) + self.stream_selector['values'] = [name for name, sid in available_streams] + + # 检查当前选中的 stream_id 是否仍然有效 + current_selection_name = self.selected_stream_id.get() + current_selection_valid = any(name == current_selection_name for name, sid in available_streams) + + if not current_selection_valid and available_streams: + # 如果当前选择无效，并且有可选流，则默认选中第一个 + self.selected_stream_id.set(available_streams[0][0]) + # 手动触发一次更新，因为 set 不会触发 <> + self.update_single_stream_plot() + elif not available_streams: + # 如果没有可选流，清空选择 + self.selected_stream_id.set("") + self.update_single_stream_plot() # 清空图表 + + def update_all_streams_plot(self): + """更新第一个选项卡的 Matplotlib 图表 (显示所有流)""" self.ax.clear() # 清除旧图 # *** 设置中文标题和标签 *** - self.ax.set_title("兴趣度随时间变化图") + self.ax.set_title("兴趣度随时间变化图 (所有活跃流)") self.ax.set_xlabel("时间") self.ax.set_ylabel("兴趣度") self.ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S')) @@ -213,6 +286,25 @@ class InterestMonitorApp: self.canvas.draw() # 重绘画布 + def update_single_stream_plot(self): + """更新第二个选项卡的 Matplotlib 图表 (显示单个选定的流)""" + self.ax_single_interest.clear() + self.ax_single_probability.clear() + + # 设置子图标题和标签 + self.ax_single_interest.set_title("兴趣度") + self.ax_single_interest.set_ylabel("兴趣度") + self.ax_single_interest.grid(True) + self.ax_single_interest.set_ylim(0, 10) # 固定 Y 轴范围 0-10 + + self.ax_single_probability.set_title("回复评估概率") + self.ax_single_probability.set_xlabel("时间") + self.ax_single_probability.set_ylabel("概率") + self.ax_single_probability.grid(True) + self.ax_single_probability.set_ylim(0, 1.05) # 固定 Y 轴范围 0-1 + self.ax_single_probability.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S')) + + selected_name = self.selected_stream_id.get() def update_display(self): """主更新循环""" try: diff --git a/src/do_tool/tool_use.py b/src/do_tool/tool_use.py index 4cef79a37..0ee966f5f 100644 --- a/src/do_tool/tool_use.py +++ b/src/do_tool/tool_use.py @@ -26,13 +26,12 @@ class ToolUser: @staticmethod async def _build_tool_prompt( - message_txt: str, sender_name: str, chat_stream: ChatStream, subheartflow: SubHeartflow = None + self, message_txt: str, chat_stream: ChatStream, subheartflow: SubHeartflow = None ): """构建工具使用的提示词 Args: message_txt: 用户消息文本 - sender_name: 发送者名称 chat_stream: 聊天流对象 Returns: @@ -44,28 +43,28 @@ class ToolUser: else: mid_memory_info = "" - stream_id = chat_stream.stream_id - chat_talking_prompt = "" - if stream_id: - chat_talking_prompt = get_recent_group_detailed_plain_text( - stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True - ) - new_messages = list( - db.messages.find({"chat_id": chat_stream.stream_id, "time": {"$gt": time.time()}}).sort("time", 1).limit(15) - ) - new_messages_str = "" - for msg in new_messages: - if "detailed_plain_text" in msg: - new_messages_str += f"{msg['detailed_plain_text']}" + # stream_id = chat_stream.stream_id + # chat_talking_prompt = "" + # if stream_id: + # chat_talking_prompt = get_recent_group_detailed_plain_text( + # stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True + # ) + # new_messages = list( + # db.messages.find({"chat_id": chat_stream.stream_id, "time": {"$gt": time.time()}}).sort("time", 1).limit(15) + # ) + # new_messages_str = "" + # for msg in new_messages: + # if "detailed_plain_text" in msg: + # new_messages_str += f"{msg['detailed_plain_text']}" # 这些信息应该从调用者传入，而不是从self获取 bot_name = global_config.BOT_NICKNAME prompt = "" prompt += mid_memory_info prompt += "你正在思考如何回复群里的消息。\n" - prompt += "之前群里进行了如下讨论:\n" - prompt += chat_talking_prompt - prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n" + prompt += f"之前群里进行了如下讨论:\n" + prompt += message_txt + # prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n" prompt += f"注意你就是{bot_name}，{bot_name}是你的名字。根据之前的聊天记录补充问题信息，搜索时避开你的名字。\n" prompt += "你现在需要对群里的聊天内容进行回复，现在选择工具来对消息和你的回复进行处理，你是否需要额外的信息，比如回忆或者搜寻已有的知识，改变关系和情感，或者了解你现在正在做什么。" return prompt @@ -119,7 +118,7 @@ class ToolUser: return None async def use_tool( - self, message_txt: str, sender_name: str, chat_stream: ChatStream, sub_heartflow: SubHeartflow = None + self, message_txt: str, chat_stream: ChatStream, sub_heartflow: SubHeartflow = None ): """使用工具辅助思考，判断是否需要额外信息 @@ -134,7 +133,7 @@ class ToolUser: """ try: # 构建提示词 - prompt = await self._build_tool_prompt(message_txt, sender_name, chat_stream, sub_heartflow) + prompt = await self._build_tool_prompt(message_txt, chat_stream, sub_heartflow) # 定义可用工具 tools = self._define_tools() diff --git a/src/heart_flow/sub_heartflow.py b/src/heart_flow/sub_heartflow.py index 767a36bec..998c7a8ba 100644 --- a/src/heart_flow/sub_heartflow.py +++ b/src/heart_flow/sub_heartflow.py @@ -4,6 +4,9 @@ from src.plugins.moods.moods import MoodManager from src.plugins.models.utils_model import LLMRequest from src.config.config import global_config import time +from typing import Optional +from datetime import datetime +import traceback from src.plugins.chat.message import UserInfo from src.plugins.chat.utils import parse_text_timestamps @@ -113,6 +116,8 @@ class SubHeartflow: self.running_knowledges = [] + self._thinking_lock = asyncio.Lock() # 添加思考锁，防止并发思考 + self.bot_name = global_config.BOT_NICKNAME def add_observation(self, observation: Observation): @@ -138,144 +143,172 @@ class SubHeartflow: """清空所有observation对象""" self.observations.clear() + def _get_primary_observation(self) -> Optional[ChattingObservation]: + """获取主要的（通常是第一个）ChattingObservation实例""" + if self.observations and isinstance(self.observations[0], ChattingObservation): + return self.observations[0] + logger.warning(f"SubHeartflow {self.subheartflow_id} 没有找到有效的 ChattingObservation") + return None + async def subheartflow_start_working(self): while True: current_time = time.time() - if ( - current_time - self.last_reply_time > global_config.sub_heart_flow_freeze_time - ): # 120秒无回复/不在场，冻结 - self.is_active = False - await asyncio.sleep(global_config.sub_heart_flow_update_interval) # 每60秒检查一次 - else: - self.is_active = True - self.last_active_time = current_time # 更新最后激活时间 + # --- 调整后台任务逻辑 --- # + # 这个后台循环现在主要负责检查是否需要自我销毁 + # 不再主动进行思考或状态更新，这些由 HeartFC_Chat 驱动 - self.current_state.update_current_state_info() + # 检查是否需要冻结（这个逻辑可能需要重新审视，因为激活状态现在由外部驱动） + # if current_time - self.last_reply_time > global_config.sub_heart_flow_freeze_time: + # self.is_active = False + # else: + # self.is_active = True + # self.last_active_time = current_time # 由外部调用（如 thinking）更新 - # await self.do_a_thinking() - # await self.judge_willing() - await asyncio.sleep(global_config.sub_heart_flow_update_interval) + # 检查是否超过指定时间没有激活 (例如，没有被调用进行思考) + if current_time - self.last_active_time > global_config.sub_heart_flow_stop_time: # 例如 5 分钟 + logger.info(f"子心流 {self.subheartflow_id} 超过 {global_config.sub_heart_flow_stop_time} 秒没有激活，正在销毁..." + f" (Last active: {datetime.fromtimestamp(self.last_active_time).strftime('%Y-%m-%d %H:%M:%S')})") + # 在这里添加实际的销毁逻辑，例如从主 Heartflow 管理器中移除自身 + # heartflow.remove_subheartflow(self.subheartflow_id) # 假设有这样的方法 + break # 退出循环以停止任务 - # 检查是否超过10分钟没有激活 - if ( - current_time - self.last_active_time > global_config.sub_heart_flow_stop_time - ): # 5分钟无回复/不在场，销毁 - logger.info(f"子心流 {self.subheartflow_id} 已经5分钟没有激活，正在销毁...") - break # 退出循环，销毁自己 + # 不再需要内部驱动的状态更新和思考 + # self.current_state.update_current_state_info() + # await self.do_a_thinking() + # await self.judge_willing() + + await asyncio.sleep(global_config.sub_heart_flow_update_interval) # 定期检查销毁条件 + + async def ensure_observed(self): + """确保在思考前执行了观察""" + observation = self._get_primary_observation() + if observation: + try: + await observation.observe() + logger.trace(f"[{self.subheartflow_id}] Observation updated before thinking.") + except Exception as e: + logger.error(f"[{self.subheartflow_id}] Error during pre-thinking observation: {e}") + logger.error(traceback.format_exc()) async def do_observe(self): - observation = self.observations[0] - await observation.observe() + # 现在推荐使用 ensure_observed()，但保留此方法以兼容旧用法（或特定场景） + observation = self._get_primary_observation() + if observation: + await observation.observe() + else: + 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: int = None + 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] ): - current_thinking_info = self.current_mind - mood_info = self.current_state.mood - # mood_info = "你很生气，很愤怒" - observation = self.observations[0] - if obs_id: - print(f"11111111111有id,开始获取观察信息{obs_id}") - chat_observe_info = observation.get_observe_info(obs_id) - else: - chat_observe_info = observation.get_observe_info() + async with self._thinking_lock: # 获取思考锁 + # --- 在思考前确保观察已执行 --- # + await self.ensure_observed() - extra_info_prompt = "" - for tool_name, tool_data in extra_info.items(): - extra_info_prompt += f"{tool_name} 相关信息:\n" - for item in tool_data: - extra_info_prompt += f"- {item['name']}: {item['content']}\n" + self.last_active_time = time.time() # 更新最后激活时间戳 - # 开始构建prompt - prompt_personality = f"你的名字是{self.bot_name},你" - # person - individuality = Individuality.get_instance() + current_thinking_info = self.current_mind + mood_info = self.current_state.mood + observation = self._get_primary_observation() + if not observation: + logger.error(f"[{self.subheartflow_id}] Cannot perform thinking without observation.") + return "", [] # 返回空结果 - personality_core = individuality.personality.personality_core - prompt_personality += personality_core + # --- 获取观察信息 --- # + chat_observe_info = "" + if obs_id: + try: + chat_observe_info = observation.get_observe_info(obs_id) + logger.debug(f"[{self.subheartflow_id}] Using specific observation IDs: {obs_id}") + except Exception as e: + logger.error(f"[{self.subheartflow_id}] Error getting observe info with IDs {obs_id}: {e}. Falling back.") + chat_observe_info = observation.get_observe_info() # 出错时回退到默认观察 + else: + chat_observe_info = observation.get_observe_info() + logger.debug(f"[{self.subheartflow_id}] Using default observation info.") - personality_sides = individuality.personality.personality_sides - random.shuffle(personality_sides) - prompt_personality += f",{personality_sides[0]}" - identity_detail = individuality.identity.identity_detail - random.shuffle(identity_detail) - prompt_personality += f",{identity_detail[0]}" + # --- 构建 Prompt (基本逻辑不变) --- # + extra_info_prompt = "" + if extra_info: + for tool_name, tool_data in extra_info.items(): + extra_info_prompt += f"{tool_name} 相关信息:\n" + for item in tool_data: + extra_info_prompt += f"- {item['name']}: {item['content']}\n" + else: + extra_info_prompt = "无工具信息。\n" # 提供默认值 - # 关系 - who_chat_in_group = [ - (chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname) - ] - who_chat_in_group += get_recent_group_speaker( - chat_stream.stream_id, - (chat_stream.user_info.platform, chat_stream.user_info.user_id), - limit=global_config.MAX_CONTEXT_SIZE, - ) + individuality = Individuality.get_instance() + prompt_personality = f"你的名字是{self.bot_name},你" + prompt_personality += individuality.personality.personality_core + personality_sides = individuality.personality.personality_sides + if personality_sides: random.shuffle(personality_sides); prompt_personality += f",{personality_sides[0]}" + identity_detail = individuality.identity.identity_detail + if identity_detail: random.shuffle(identity_detail); prompt_personality += f",{identity_detail[0]}" - relation_prompt = "" - for person in who_chat_in_group: - relation_prompt += await relationship_manager.build_relationship_info(person) + 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_all = ( - # f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，" - # f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。" - # ) - relation_prompt_all = (await global_prompt_manager.get_prompt_async("relationship_prompt")).format( - relation_prompt, sender_info.user_nickname - ) + 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) - sender_name_sign = ( - f"<{chat_stream.platform}:{sender_info.user_id}:{sender_info.user_nickname}:{sender_info.user_cardname}>" - ) + relation_prompt_all = (await global_prompt_manager.get_prompt_async("relationship_prompt")).format( + relation_prompt, sender_info.user_nickname + ) - # prompt = "" - # # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n" - # if tool_result.get("used_tools", False): - # prompt += f"{collected_info}\n" - # prompt += f"{relation_prompt_all}\n" - # prompt += f"{prompt_personality}\n" - # prompt += f"刚刚你的想法是{current_thinking_info}。如果有新的内容，记得转换话题\n" - # prompt += "-----------------------------------\n" - # prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n" - # prompt += f"你现在{mood_info}\n" - # prompt += f"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\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" - # prompt += f"记得结合上述的消息，生成内心想法，文字不要浮夸，注意你就是{self.bot_name}，{self.bot_name}指的就是你。" + 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()) + 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_prompt, - # prompt_schedule, - relation_prompt_all, - prompt_personality, - current_thinking_info, - time_now, - chat_observe_info, - mood_info, - sender_name_sign, - message_txt, - self.bot_name, - ) + prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_before")).format( + extra_info=extra_info_prompt, + 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, + bot_name=self.bot_name, + ) - prompt = await relationship_manager.convert_all_person_sign_to_person_name(prompt) - prompt = parse_text_timestamps(prompt, mode="lite") + prompt = await relationship_manager.convert_all_person_sign_to_person_name(prompt) + prompt = parse_text_timestamps(prompt, mode="lite") - try: - response, reasoning_content = await self.llm_model.generate_response_async(prompt) - except Exception as e: - logger.error(f"回复前内心独白获取失败: {e}") - response = "" - self.update_current_mind(response) + logger.debug(f"[{self.subheartflow_id}] Thinking Prompt:\n{prompt}") - self.current_mind = response + try: + response, reasoning_content = await self.llm_model.generate_response_async(prompt) + if not response: # 如果 LLM 返回空，给一个默认想法 + response = "(不知道该想些什么...)" + logger.warning(f"[{self.subheartflow_id}] LLM returned empty response for thinking.") + except Exception as e: + logger.error(f"[{self.subheartflow_id}] 内心独白获取失败: {e}") + response = "(思考时发生错误...)" # 错误时的默认想法 + + self.update_current_mind(response) + + # self.current_mind 已经在 update_current_mind 中更新 + + logger.info(f"[{self.subheartflow_id}] 思考前脑内状态：{self.current_mind}") + return self.current_mind, self.past_mind - logger.info(f"prompt:\n{prompt}\n") - logger.info(f"麦麦的思考前脑内状态：{self.current_mind}") - return self.current_mind, self.past_mind - async def do_thinking_after_observe( self, message_txt: str, sender_info: UserInfo, chat_stream: ChatStream, extra_info: str, obs_id: int = None ): @@ -337,7 +370,6 @@ class SubHeartflow: f"<{chat_stream.platform}:{sender_info.user_id}:{sender_info.user_nickname}:{sender_info.user_cardname}>" ) - time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_after_observe")).format( @@ -436,6 +468,24 @@ class SubHeartflow: self.past_mind.append(self.current_mind) self.current_mind = response + async def check_reply_trigger(self) -> bool: + """根据观察到的信息和内部状态，判断是否应该触发一次回复。 + TODO: 实现具体的判断逻辑。 + 例如：检查 self.observations[0].now_message_info 是否包含提及、问题， + 或者 self.current_mind 中是否包含强烈的回复意图等。 + """ + # Placeholder: 目前始终返回 False，需要后续实现 + logger.trace(f"[{self.subheartflow_id}] check_reply_trigger called. (Logic Pending)") + # --- 实现触发逻辑 --- # + # 示例：如果观察到的最新消息包含自己的名字，则有一定概率触发 + # observation = self._get_primary_observation() + # if observation and self.bot_name in observation.now_message_info[-100:]: # 检查最后100个字符 + # if random.random() < 0.3: # 30% 概率触发 + # logger.info(f"[{self.subheartflow_id}] Triggering reply based on mention.") + # return True + # ------------------ # + return False # 默认不触发 + init_prompt() # subheartflow = SubHeartflow() diff --git a/src/plugins/chat_module/heartFC_chat/heartFC__generator.py b/src/plugins/chat_module/heartFC_chat/heartFC__generator.py index 66b8b3335..f04eeb862 100644 --- a/src/plugins/chat_module/heartFC_chat/heartFC__generator.py +++ b/src/plugins/chat_module/heartFC_chat/heartFC__generator.py @@ -48,45 +48,21 @@ class ResponseGenerator: arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier() with Timer() as t_generate_response: - checked = False - if random.random() > 0: - checked = False - current_model = self.model_normal - current_model.temperature = ( - global_config.llm_normal["temp"] * arousal_multiplier - ) # 激活度越高，温度越高 - model_response = await self._generate_response_with_model( - message, current_model, thinking_id, mode="normal" - ) - model_checked_response = model_response - else: - checked = True - current_model = self.model_normal - current_model.temperature = ( - global_config.llm_normal["temp"] * arousal_multiplier - ) # 激活度越高，温度越高 - print(f"生成{message.processed_plain_text}回复温度是：{current_model.temperature}") - model_response = await self._generate_response_with_model( - message, current_model, thinking_id, mode="simple" - ) + current_model = self.model_normal + current_model.temperature = ( + global_config.llm_normal["temp"] * arousal_multiplier + ) # 激活度越高，温度越高 + model_response = await self._generate_response_with_model( + message, current_model, thinking_id, mode="normal" + ) - current_model.temperature = global_config.llm_normal["temp"] - model_checked_response = await self._check_response_with_model( - message, model_response, current_model, thinking_id - ) if model_response: - if checked: - logger.info( - f"{global_config.BOT_NICKNAME}的回复是：{model_response}，思忖后，回复是：{model_checked_response},生成回复时间: {t_generate_response.human_readable}" - ) - else: - logger.info( - f"{global_config.BOT_NICKNAME}的回复是：{model_response},生成回复时间: {t_generate_response.human_readable}" - ) - - model_processed_response = await self._process_response(model_checked_response) + logger.info( + f"{global_config.BOT_NICKNAME}的回复是：{model_response},生成回复时间: {t_generate_response.human_readable}" + ) + model_processed_response = await self._process_response(model_response) return model_processed_response else: diff --git a/src/plugins/chat_module/heartFC_chat/heartFC_chat.py b/src/plugins/chat_module/heartFC_chat/heartFC_chat.py index 4020f9ba3..0e6d95e23 100644 --- a/src/plugins/chat_module/heartFC_chat/heartFC_chat.py +++ b/src/plugins/chat_module/heartFC_chat/heartFC_chat.py @@ -18,6 +18,8 @@ from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager 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 # 定义日志配置 chat_config = LogConfig( @@ -28,7 +30,6 @@ chat_config = LogConfig( logger = get_module_logger("heartFC_chat", config=chat_config) # 新增常量 -INTEREST_LEVEL_REPLY_THRESHOLD = 4.0 INTEREST_MONITOR_INTERVAL_SECONDS = 1 class HeartFC_Chat: @@ -41,87 +42,105 @@ class HeartFC_Chat: self._interest_monitor_task: Optional[asyncio.Task] = None async def start(self): - """Starts asynchronous tasks like the interest monitor.""" - logger.info("HeartFC_Chat starting asynchronous tasks...") + """启动异步任务,如兴趣监控器""" + logger.info("HeartFC_Chat 正在启动异步任务...") await self.interest_manager.start_background_tasks() self._initialize_monitor_task() - logger.info("HeartFC_Chat asynchronous tasks started.") + logger.info("HeartFC_Chat 异步任务启动完成") def _initialize_monitor_task(self): - """启动后台兴趣监控任务""" + """启动后台兴趣监控任务，可以检查兴趣是否足以开启心流对话""" if self._interest_monitor_task is None or self._interest_monitor_task.done(): try: loop = asyncio.get_running_loop() self._interest_monitor_task = loop.create_task(self._interest_monitor_loop()) - logger.info(f"Interest monitor task created. Interval: {INTEREST_MONITOR_INTERVAL_SECONDS}s, Level Threshold: {INTEREST_LEVEL_REPLY_THRESHOLD}") + logger.info(f"兴趣监控任务已创建。监控间隔: {INTEREST_MONITOR_INTERVAL_SECONDS}秒。") except RuntimeError: - logger.error("Failed to create interest monitor task: No running event loop.") + logger.error("创建兴趣监控任务失败：没有运行中的事件循环。") raise else: - logger.warning("Interest monitor task creation skipped: already running or exists.") + logger.warning("跳过兴趣监控任务创建：任务已存在或正在运行。") 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("Interest monitor loop starting...") - await asyncio.sleep(0.3) + logger.info("兴趣监控循环开始...") while True: await asyncio.sleep(INTEREST_MONITOR_INTERVAL_SECONDS) try: - interest_items_snapshot: List[tuple[str, InterestChatting]] = [] - stream_ids = list(self.interest_manager.interest_dict.keys()) - for stream_id in stream_ids: - chatting_instance = self.interest_manager.get_interest_chatting(stream_id) - if chatting_instance: - interest_items_snapshot.append((stream_id, chatting_instance)) + # --- 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 + 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, chatting_instance in interest_items_snapshot: - triggering_message = chatting_instance.last_triggering_message - current_interest = chatting_instance.get_interest() + for stream_id in active_stream_ids: + sub_hf = heartflow.get_subheartflow(stream_id) + if not sub_hf: + logger.warning(f"监控循环: 无法获取活跃流 {stream_id} 的 sub_hf") + continue - # 添加调试日志，检查触发条件 - # logger.debug(f"[兴趣监控][{stream_id}] 当前兴趣: {current_interest:.2f}, 阈值: {INTEREST_LEVEL_REPLY_THRESHOLD}, 触发消息存在: {triggering_message is not None}") + # --- 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 --- # - if current_interest > INTEREST_LEVEL_REPLY_THRESHOLD and triggering_message is not None: - logger.info(f"[{stream_id}] 检测到高兴趣度 ({current_interest:.2f} > {INTEREST_LEVEL_REPLY_THRESHOLD}). 基于消息 ID: {triggering_message.message_info.message_id} 的上下文触发回复") # 更新日志信息使其更清晰 + 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 + except Exception as e: + logger.error(f"错误调用 check_reply_trigger 流 {stream_id}: {e}") + logger.error(traceback.format_exc()) - chatting_instance.reset_trigger_info() - logger.debug(f"[{stream_id}] Trigger info reset before starting reply task.") + 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)) - asyncio.create_task(self._process_triggered_reply(stream_id, triggering_message)) except asyncio.CancelledError: - logger.info("Interest monitor loop cancelled.") + logger.info("兴趣监控循环已取消。") break except Exception as e: - logger.error(f"Error in interest monitor loop: {e}") + logger.error(f"兴趣监控循环错误: {e}") logger.error(traceback.format_exc()) - await asyncio.sleep(5) + await asyncio.sleep(5) # 发生错误时等待 - async def _process_triggered_reply(self, stream_id: str, triggering_message: MessageRecv): - """Helper coroutine to handle the processing of a triggered reply based on interest level.""" + 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}] Starting level-triggered reply generation for message ID: {triggering_message.message_info.message_id}...") - await self.trigger_reply_generation(triggering_message) + 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) - # 在回复处理后降低兴趣度，降低固定值：新阈值的一半 - decrease_value = INTEREST_LEVEL_REPLY_THRESHOLD / 2 - self.interest_manager.decrease_interest(stream_id, value=decrease_value) - post_trigger_interest = self.interest_manager.get_interest(stream_id) - # 更新日志以反映降低的是基于新阈值的固定值 - logger.info(f"[{stream_id}] Interest decreased by fixed value {decrease_value:.2f} (LevelThreshold/2) after processing level-triggered reply. Current interest: {post_trigger_interest:.2f}") + # --- 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 level-triggered reply for stream_id {stream_id}, context message_id {triggering_message.message_info.message_id}: {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, message: MessageRecv): - """创建思考消息 (从 message 获取信息)""" - chat = message.chat_stream - if not chat: - logger.error(f"Cannot create thinking message, chat_stream is None for message ID: {message.message_info.message_id}") - return None - userinfo = message.message_info.user_info # 发起思考的用户（即原始消息发送者） - messageinfo = message.message_info # 原始消息信息 + 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: + logger.error("无法创建思考消息，缺少有效的锚点消息或聊天流。") + return None + + chat = anchor_message.chat_stream + messageinfo = anchor_message.message_info bot_user_info = UserInfo( user_id=global_config.BOT_QQ, user_nickname=global_config.BOT_NICKNAME, @@ -133,17 +152,21 @@ class HeartFC_Chat: thinking_message = MessageThinking( message_id=thinking_id, chat_stream=chat, - bot_user_info=bot_user_info, # 思考消息的发出者是 bot - reply=message, # 回复的是原始消息 + bot_user_info=bot_user_info, + reply=anchor_message, # 回复的是锚点消息 thinking_start_time=thinking_time_point, ) MessageManager().add_message(thinking_message) - return thinking_id - async def _send_response_messages(self, message: MessageRecv, response_set: List[str], thinking_id) -> MessageSending: - chat = message.chat_stream + async def _send_response_messages(self, anchor_message: Optional[MessageRecv], response_set: List[str], thinking_id) -> Optional[MessageSending]: + """发送回复消息 (尝试锚定到 anchor_message)""" + if not anchor_message or not anchor_message.chat_stream: + logger.error("无法发送回复，缺少有效的锚点消息或聊天流。") + return None + + chat = anchor_message.chat_stream container = MessageManager().get_container(chat.stream_id) thinking_message = None for msg in container.messages: @@ -152,26 +175,26 @@ class HeartFC_Chat: container.messages.remove(msg) break if not thinking_message: - logger.warning("未找到对应的思考消息，可能已超时被移除") + logger.warning(f"[{chat.stream_id}] 未找到对应的思考消息 {thinking_id}，可能已超时被移除") return None thinking_start_time = thinking_message.thinking_start_time message_set = MessageSet(chat, thinking_id) mark_head = False first_bot_msg = None - for msg in response_set: - message_segment = Seg(type="text", data=msg) + for msg_text in response_set: + message_segment = Seg(type="text", data=msg_text) bot_message = MessageSending( - message_id=thinking_id, + message_id=thinking_id, # 使用 thinking_id 作为批次标识 chat_stream=chat, bot_user_info=UserInfo( user_id=global_config.BOT_QQ, user_nickname=global_config.BOT_NICKNAME, - platform=message.message_info.platform, # 从传入的 message 获取 platform + platform=anchor_message.message_info.platform, ), - sender_info=message.message_info.user_info, # 发送给谁 + sender_info=anchor_message.message_info.user_info, # 发送给锚点消息的用户 message_segment=message_segment, - reply=message, # 回复原始消息 + reply=anchor_message, # 回复锚点消息 is_head=not mark_head, is_emoji=False, thinking_start_time=thinking_start_time, @@ -180,185 +203,277 @@ class HeartFC_Chat: mark_head = True first_bot_msg = bot_message message_set.add_message(bot_message) - MessageManager().add_message(message_set) - return first_bot_msg - async def _handle_emoji(self, message: MessageRecv, response_set, send_emoji=""): - """处理表情包 (从 message 获取信息)""" - chat = message.chat_stream + if message_set.messages: # 确保有消息才添加 + MessageManager().add_message(message_set) + return first_bot_msg + else: + logger.warning(f"[{chat.stream_id}] 没有生成有效的回复消息集，无法发送。") + return None + + async def _handle_emoji(self, anchor_message: Optional[MessageRecv], response_set, send_emoji=""): + """处理表情包 (尝试锚定到 anchor_message)""" + if not anchor_message or not anchor_message.chat_stream: + logger.error("无法处理表情包，缺少有效的锚点消息或聊天流。") + return + + chat = anchor_message.chat_stream if send_emoji: emoji_raw = await emoji_manager.get_emoji_for_text(send_emoji) else: emoji_text_source = "".join(response_set) if response_set else "" emoji_raw = await emoji_manager.get_emoji_for_text(emoji_text_source) + if emoji_raw: emoji_path, description = emoji_raw emoji_cq = image_path_to_base64(emoji_path) - thinking_time_point = round(message.message_info.time, 2) + # 使用当前时间戳，因为没有原始消息的时间戳 + thinking_time_point = round(time.time(), 2) message_segment = Seg(type="emoji", data=emoji_cq) bot_message = MessageSending( - message_id="mt" + str(thinking_time_point), + message_id="me" + str(thinking_time_point), # 使用不同的 ID 前缀? chat_stream=chat, bot_user_info=UserInfo( user_id=global_config.BOT_QQ, user_nickname=global_config.BOT_NICKNAME, - platform=message.message_info.platform, + platform=anchor_message.message_info.platform, ), - sender_info=message.message_info.user_info, # 发送给谁 + sender_info=anchor_message.message_info.user_info, message_segment=message_segment, - reply=message, # 回复原始消息 + reply=anchor_message, # 回复锚点消息 is_head=False, is_emoji=True, ) MessageManager().add_message(bot_message) - async def _update_relationship(self, message: MessageRecv, response_set): - """更新关系情绪""" + async def _update_relationship(self, anchor_message: Optional[MessageRecv], response_set): + """更新关系情绪 (尝试基于 anchor_message)""" + if not anchor_message or not anchor_message.chat_stream: + logger.error("无法更新关系情绪，缺少有效的锚点消息或聊天流。") + return + + # 关系更新依赖于理解回复是针对谁的，以及原始消息的上下文 + # 这里的实现可能需要调整，取决于关系管理器如何工作 ori_response = ",".join(response_set) - stance, emotion = await self.gpt._get_emotion_tags(ori_response, message.processed_plain_text) + # 注意：anchor_message.processed_plain_text 是锚点消息的文本，不一定是思考的全部上下文 + stance, emotion = await self.gpt._get_emotion_tags(ori_response, anchor_message.processed_plain_text) await relationship_manager.calculate_update_relationship_value( - chat_stream=message.chat_stream, label=emotion, stance=stance + chat_stream=anchor_message.chat_stream, # 使用锚点消息的流 + label=emotion, + stance=stance ) self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor) - async def trigger_reply_generation(self, message: MessageRecv): - """根据意愿阈值触发的实际回复生成和发送逻辑 (V3 - 简化参数)""" - chat = message.chat_stream - userinfo = message.message_info.user_info - messageinfo = message.message_info + async def trigger_reply_generation(self, stream_id: str, observed_messages: List[dict]): + """根据 SubHeartflow 的触发信号生成回复 (基于观察)""" + chat = None + 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 timing_results = {} + current_mind = None response_set = None thinking_id = None info_catcher = None try: + # --- 1. 获取核心对象：ChatStream 和 SubHeartflow --- try: - with Timer("观察", timing_results): - sub_hf = heartflow.get_subheartflow(chat.stream_id) - if not sub_hf: - logger.warning(f"尝试观察时未找到 stream_id {chat.stream_id} 的 subheartflow") + with Timer("获取聊天流和子心流", timing_results): + chat = chat_manager.get_stream(stream_id) + if not chat: + logger.error(f"[{stream_id}] 无法找到聊天流对象，无法生成回复。") + return + sub_hf = heartflow.get_subheartflow(stream_id) + if not sub_hf: + logger.error(f"[{stream_id}] 无法找到子心流对象，无法生成回复。") return - await sub_hf.do_observe() except Exception as e: - logger.error(f"心流观察失败: {e}") - logger.error(traceback.format_exc()) + logger.error(f"[{stream_id}] 获取 ChatStream 或 SubHeartflow 时出错: {e}") + logger.error(traceback.format_exc()) + return - container = MessageManager().get_container(chat.stream_id) - thinking_count = container.count_thinking_messages() - max_thinking_messages = getattr(global_config, 'max_concurrent_thinking_messages', 3) - if thinking_count >= max_thinking_messages: - logger.warning(f"聊天流 {chat.stream_id} 已有 {thinking_count} 条思考消息，取消回复。触发消息: {message.processed_plain_text[:30]}...") + # --- 2. 尝试从 observed_messages 重建最后一条消息作为锚点 --- # + try: + with Timer("获取最后消息锚点", timing_results): + 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 + 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 + 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 + + # --- 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 + # --- 4. 检查并发思考限制 (使用 anchor_message 简化获取) --- + try: + container = MessageManager().get_container(chat.stream_id) + thinking_count = container.count_thinking_messages() + max_thinking_messages = getattr(global_config, 'max_concurrent_thinking_messages', 3) + if thinking_count >= max_thinking_messages: + logger.warning(f"聊天流 {chat.stream_id} 已有 {thinking_count} 条思考消息，取消回复。") + return + except Exception as e: + logger.error(f"[{stream_id}] 检查并发思考限制时出错: {e}") + return + + # --- 5. 创建思考消息 (使用 anchor_message) --- try: with Timer("创建思考消息", timing_results): - thinking_id = await self._create_thinking_message(message) + # 注意：这里传递 anchor_message 给 _create_thinking_message + thinking_id = await self._create_thinking_message(anchor_message) except Exception as e: - logger.error(f"心流创建思考消息失败: {e}") + logger.error(f"[{stream_id}] 创建思考消息失败: {e}") return if not thinking_id: - logger.error("未能成功创建思考消息 ID，无法继续回复流程。") + logger.error(f"[{stream_id}] 未能成功创建思考消息 ID，无法继续回复流程。") return - logger.trace(f"创建捕捉器，thinking_id:{thinking_id}") + # --- 6. 信息捕捉器 (使用 anchor_message) --- + logger.trace(f"[{stream_id}] 创建捕捉器，thinking_id:{thinking_id}") info_catcher = info_catcher_manager.get_info_catcher(thinking_id) - info_catcher.catch_decide_to_response(message) + info_catcher.catch_decide_to_response(anchor_message) + # --- 7. 思考前使用工具 --- # get_mid_memory_id = [] tool_result_info = {} send_emoji = "" + observation_context_text = "" # 从 observation 获取上下文文本 try: - with Timer("思考前使用工具", timing_results): - tool_result = await self.tool_user.use_tool( - message.processed_plain_text, - userinfo.user_nickname, - chat, - heartflow.get_subheartflow(chat.stream_id), - ) - if tool_result.get("used_tools", False): - if "structured_info" in tool_result: - tool_result_info = tool_result["structured_info"] - get_mid_memory_id = [] - for tool_name, tool_data in tool_result_info.items(): - if tool_name == "mid_chat_mem": - for mid_memory in tool_data: - get_mid_memory_id.append(mid_memory["content"]) - if tool_name == "send_emoji": - send_emoji = tool_data[0]["content"] - except Exception as e: - logger.error(f"思考前工具调用失败: {e}") - logger.error(traceback.format_exc()) + # --- 使用传入的 observed_messages 构建上下文文本 --- # + if observed_messages: + # 可以选择转换全部消息，或只转换最后几条 + # 这里示例转换全部消息 + context_texts = [] + for msg_dict in observed_messages: + # 假设 detailed_plain_text 字段包含所需文本 + # 你可能需要更复杂的逻辑来格式化，例如添加发送者和时间 + text = msg_dict.get('detailed_plain_text', '') + if text: context_texts.append(text) + observation_context_text = "\n".join(context_texts) + logger.debug(f"[{stream_id}] Context for tools:\n{observation_context_text[-200:]}...") # 打印部分上下文 + else: + logger.warning(f"[{stream_id}] observed_messages 列表为空，无法为工具提供上下文。") - current_mind, past_mind = "", "" - try: - with Timer("思考前脑内状态", timing_results): - sub_hf = heartflow.get_subheartflow(chat.stream_id) - if sub_hf: - current_mind, past_mind = await sub_hf.do_thinking_before_reply( - message_txt=message.processed_plain_text, - sender_info=userinfo, + if observation_context_text: + with Timer("思考前使用工具", timing_results): + tool_result = await self.tool_user.use_tool( + message_txt=observation_context_text, # <--- 使用观察上下文 chat_stream=chat, - obs_id=get_mid_memory_id, - extra_info=tool_result_info, + sub_heartflow=sub_hf ) - else: - logger.warning(f"尝试思考前状态时未找到 stream_id {chat.stream_id} 的 subheartflow") + if tool_result.get("used_tools", False): + if "structured_info" in tool_result: + tool_result_info = tool_result["structured_info"] + get_mid_memory_id = [] + for tool_name, tool_data in tool_result_info.items(): + if tool_name == "mid_chat_mem": + for mid_memory in tool_data: + get_mid_memory_id.append(mid_memory["content"]) + if tool_name == "send_emoji": + send_emoji = tool_data[0]["content"] except Exception as e: - logger.error(f"心流思考前脑内状态失败: {e}") + logger.error(f"[{stream_id}] 思考前工具调用失败: {e}") logger.error(traceback.format_exc()) - if info_catcher: - info_catcher.catch_afer_shf_step(timing_results.get("思考前脑内状态"), past_mind, current_mind) + # --- 8. 调用 SubHeartflow 进行思考 (不传递具体消息文本和发送者) --- try: - with Timer("生成回复", timing_results): - response_set = await self.gpt.generate_response(message, thinking_id) + 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( + chat_stream=chat, + extra_info=tool_result_info, + obs_id=get_mid_memory_id, + ) + logger.info(f"[{stream_id}] SubHeartflow 思考完成: {current_mind}") except Exception as e: - logger.error(f"GPT 生成回复失败: {e}") + logger.error(f"[{stream_id}] SubHeartflow 思考失败: {e}") + logger.error(traceback.format_exc()) + if info_catcher: info_catcher.done_catch() + return # 思考失败则不继续 + if info_catcher: + info_catcher.catch_afer_shf_step(timing_results.get("生成内心想法(SubHF)"), past_mind, current_mind) + + # --- 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) + except Exception as e: + logger.error(f"[{stream_id}] GPT 生成回复失败: {e}") logger.error(traceback.format_exc()) if info_catcher: info_catcher.done_catch() return if info_catcher: - info_catcher.catch_after_generate_response(timing_results.get("生成回复")) + info_catcher.catch_after_generate_response(timing_results.get("生成最终回复(GPT)")) if not response_set: - logger.info("回复生成失败，返回为空") + logger.info(f"[{stream_id}] 回复生成失败或为空。") if info_catcher: info_catcher.done_catch() return + # --- 10. 发送消息 (使用 anchor_message) --- first_bot_msg = None try: with Timer("发送消息", timing_results): - first_bot_msg = await self._send_response_messages(message, response_set, thinking_id) + first_bot_msg = await self._send_response_messages(anchor_message, response_set, thinking_id) except Exception as e: - logger.error(f"心流发送消息失败: {e}") + logger.error(f"[{stream_id}] 发送消息失败: {e}") + logger.error(traceback.format_exc()) if info_catcher: info_catcher.catch_after_response(timing_results.get("发送消息"), response_set, first_bot_msg) - info_catcher.done_catch() + info_catcher.done_catch() # 完成捕捉 + # --- 11. 处理表情包 (使用 anchor_message) --- try: with Timer("处理表情包", timing_results): if send_emoji: - logger.info(f"麦麦决定发送表情包{send_emoji}") - await self._handle_emoji(message, response_set, send_emoji) + logger.info(f"[{stream_id}] 决定发送表情包 {send_emoji}") + await self._handle_emoji(anchor_message, response_set, send_emoji) except Exception as e: - logger.error(f"心流处理表情包失败: {e}") + logger.error(f"[{stream_id}] 处理表情包失败: {e}") + logger.error(traceback.format_exc()) + # --- 12. 记录性能日志 --- # timing_str = " | ".join([f"{step}: {duration:.2f}秒" for step, duration in timing_results.items()]) - trigger_msg = message.processed_plain_text response_msg = " ".join(response_set) if response_set else "无回复" - logger.info(f"回复任务完成: 触发消息: {trigger_msg[:20]}... | 思维消息: {response_msg[:20]}... | 性能计时: {timing_str}") + logger.info(f"[{stream_id}] 回复任务完成 (Observation Triggered): | 思维消息: {response_msg[:30]}... | 性能计时: {timing_str}") - if first_bot_msg: + # --- 13. 更新关系情绪 (使用 anchor_message) --- + if first_bot_msg: # 仅在成功发送消息后 try: with Timer("更新关系情绪", timing_results): - await self._update_relationship(message, response_set) + await self._update_relationship(anchor_message, response_set) except Exception as e: - logger.error(f"更新关系情绪失败: {e}") + logger.error(f"[{stream_id}] 更新关系情绪失败: {e}") logger.error(traceback.format_exc()) except Exception as e: - logger.error(f"回复生成任务失败 (trigger_reply_generation V3): {e}") + logger.error(f"回复生成任务失败 (trigger_reply_generation V4 - Observation Triggered): {e}") logger.error(traceback.format_exc()) finally: - pass + # 可以在这里添加清理逻辑，如果有的话 + pass + # --- 结束重构 --- + + # _create_thinking_message, _send_response_messages, _handle_emoji, _update_relationship + # 这几个辅助方法目前仍然依赖 MessageRecv 对象。 + # 如果无法可靠地从 Observation 获取并重建最后一条消息的 MessageRecv， + # 或者希望回复不锚定具体消息，那么这些方法也需要进一步重构。 diff --git a/src/plugins/chat_module/heartFC_chat/heartFC_processor.py b/src/plugins/chat_module/heartFC_chat/heartFC_processor.py index 1e84361c7..ea27aa77f 100644 --- a/src/plugins/chat_module/heartFC_chat/heartFC_processor.py +++ b/src/plugins/chat_module/heartFC_chat/heartFC_processor.py @@ -120,7 +120,7 @@ class HeartFC_Processor: # 更新兴趣度 try: - self.interest_manager.increase_interest(chat.stream_id, value=interested_rate, message=message) + self.interest_manager.increase_interest(chat.stream_id, value=interested_rate) current_interest = self.interest_manager.get_interest(chat.stream_id) # 获取更新后的值用于日志 logger.trace(f"使用激活率 {interested_rate:.2f} 更新后 (通过缓冲后)，当前兴趣度: {current_interest:.2f}") diff --git a/src/plugins/chat_module/heartFC_chat/interest.py b/src/plugins/chat_module/heartFC_chat/interest.py index 0b2a3f290..ea6e92e72 100644 --- a/src/plugins/chat_module/heartFC_chat/interest.py +++ b/src/plugins/chat_module/heartFC_chat/interest.py @@ -6,6 +6,7 @@ import json # 引入 json import os # 引入 os import traceback # <--- 添加导入 from typing import Optional # <--- 添加导入 +import random # <--- 添加导入 random from src.common.logger import get_module_logger, LogConfig, DEFAULT_CONFIG # 引入 DEFAULT_CONFIG from src.plugins.chat.chat_stream import chat_manager # *** Import ChatManager *** from ...chat.message import MessageRecv # 导入 MessageRecv @@ -20,7 +21,6 @@ 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%) -# DEFAULT_INCREASE_AMOUNT = 10.0 # 不再需要固定增加值 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) CLEANUP_INTERVAL_SECONDS = 3600 # 清理任务运行间隔 (例如：1小时) @@ -32,16 +32,39 @@ HISTORY_LOG_FILENAME = "interest_history.log" # 新的历史日志文件名 # 移除阈值，将移至 HeartFC_Chat # 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) +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) +# --- 结束：概率回复相关常量 --- + class InterestChatting: - def __init__(self, decay_rate=DEFAULT_DECAY_RATE_PER_SECOND, max_interest=MAX_INTEREST): + def __init__(self, + decay_rate=DEFAULT_DECAY_RATE_PER_SECOND, + max_interest=MAX_INTEREST, + trigger_threshold=REPLY_TRIGGER_THRESHOLD, + base_reply_probability=BASE_REPLY_PROBABILITY, + increase_rate=PROBABILITY_INCREASE_RATE_PER_SECOND, + decay_factor=PROBABILITY_DECAY_FACTOR_PER_SECOND, + max_probability=MAX_REPLY_PROBABILITY): self.interest_level: float = 0.0 - self.last_update_time: float = time.time() + self.last_update_time: float = time.time() # 同时作为兴趣和概率的更新时间基准 self.decay_rate_per_second: float = decay_rate - # self.increase_amount: float = increase_amount # 移除固定的 increase_amount self.max_interest: float = max_interest - # 新增：用于追踪最后一次显著增加的信息，供外部监控任务使用 self.last_increase_amount: float = 0.0 - self.last_triggering_message: MessageRecv | None = None + self.last_interaction_time: float = self.last_update_time # 新增：最后交互时间 + + # --- 新增：概率回复相关属性 --- + self.trigger_threshold: float = trigger_threshold + self.base_reply_probability: float = base_reply_probability + self.probability_increase_rate: float = increase_rate + self.probability_decay_factor: float = decay_factor + self.max_reply_probability: float = max_probability + self.current_reply_probability: float = 0.0 + self.is_above_threshold: bool = False # 标记兴趣值是否高于阈值 + # --- 结束：概率回复相关属性 --- def _calculate_decay(self, current_time: float): """计算从上次更新到现在的衰减""" @@ -49,6 +72,7 @@ class InterestChatting: if time_delta > 0: # 指数衰减: interest = interest * (decay_rate ^ time_delta) # 添加处理极小兴趣值避免 math domain error + old_interest = self.interest_level if self.interest_level < 1e-9: self.interest_level = 0.0 else: @@ -71,46 +95,141 @@ class InterestChatting: # 防止低于阈值 (如果需要) # self.interest_level = max(self.interest_level, MIN_INTEREST_THRESHOLD) - self.last_update_time = current_time + + # 只有在兴趣值发生变化时才更新时间戳 + if old_interest != self.interest_level: + self.last_update_time = current_time - def increase_interest(self, current_time: float, value: float, message: Optional[MessageRecv]): - """根据传入的值增加兴趣值，并记录增加量和关联消息""" - self._calculate_decay(current_time) # 先计算衰减 - # 记录这次增加的具体数值和消息，供外部判断是否触发 + def _update_reply_probability(self, current_time: float): + """根据当前兴趣是否超过阈值及时间差，更新回复概率""" + time_delta = current_time - self.last_update_time + if time_delta <= 0: + return # 时间未前进，无需更新 + + currently_above = self.interest_level >= self.trigger_threshold + + if currently_above: + if not self.is_above_threshold: + # 刚跨过阈值，重置为基础概率 + self.current_reply_probability = self.base_reply_probability + logger.debug(f"兴趣跨过阈值 ({self.trigger_threshold}). 概率重置为基础值: {self.base_reply_probability:.4f}") + else: + # 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}") + + # 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}") + # else: # 持续低于阈值，继续衰减 + # pass # 不需要特殊处理 + + # 指数衰减概率 + # 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 + 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})") + 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: + logger.warning(f"无效的衰减因子 ({self.probability_decay_factor}). 设置概率为0.") + self.current_reply_probability = 0.0 + # else: decay_factor >= 1, probability will not decay or increase, which might be intended in some cases. + + # 确保概率不低于0 + self.current_reply_probability = max(self.current_reply_probability, 0.0) + + # 更新状态标记 + self.is_above_threshold = currently_above + # 更新时间戳放在调用者处，确保 interest 和 probability 基于同一点更新 + + def increase_interest(self, current_time: float, value: float): + """根据传入的值增加兴趣值，并记录增加量""" + # 先更新概率和计算衰减（基于上次更新时间） + self._update_reply_probability(current_time) + self._calculate_decay(current_time) + # 记录这次增加的具体数值，供外部判断是否触发 self.last_increase_amount = value - self.last_triggering_message = message # 应用增加 self.interest_level += value self.interest_level = min(self.interest_level, self.max_interest) # 不超过最大值 self.last_update_time = current_time # 更新时间戳 + self.last_interaction_time = current_time # 更新最后交互时间 def decrease_interest(self, current_time: float, value: float): """降低兴趣值并更新时间 (确保不低于0)""" + # 先更新概率（基于上次更新时间） + self._update_reply_probability(current_time) # 注意：降低兴趣度是否需要先衰减？取决于具体逻辑，这里假设不衰减直接减 self.interest_level -= value self.interest_level = max(self.interest_level, 0.0) # 确保不低于0 self.last_update_time = current_time # 降低也更新时间戳 + self.last_interaction_time = current_time # 更新最后交互时间 def reset_trigger_info(self): """重置触发相关信息，在外部任务处理后调用""" self.last_increase_amount = 0.0 - self.last_triggering_message = None def get_interest(self) -> float: - """获取当前兴趣值 (由后台任务更新)""" + """获取当前兴趣值 (计算衰减后)""" + # 注意：这个方法现在会触发概率和兴趣的更新 + current_time = time.time() + self._update_reply_probability(current_time) + self._calculate_decay(current_time) + self.last_update_time = current_time # 更新时间戳 return self.interest_level def get_state(self) -> dict: """获取当前状态字典""" - # 不再需要传入 current_time 来计算，直接获取 - interest = self.get_interest() # 使用修改后的 get_interest + # 调用 get_interest 来确保状态已更新 + interest = self.get_interest() return { "interest_level": round(interest, 2), "last_update_time": self.last_update_time, + "current_reply_probability": round(self.current_reply_probability, 4), # 添加概率到状态 + "is_above_threshold": self.is_above_threshold, # 添加阈值状态 + "last_interaction_time": self.last_interaction_time # 新增：添加最后交互时间到状态 # 可以选择性地暴露 last_increase_amount 给状态，方便调试 # "last_increase_amount": round(self.last_increase_amount, 2) } + def should_evaluate_reply(self) -> bool: + """ + 判断是否应该触发一次回复评估。 + 首先更新概率状态，然后根据当前概率进行随机判断。 + """ + current_time = time.time() + # 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 + + if self.is_above_threshold and 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}") + # 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 + return trigger + else: + # logger.debug(f"Reply evaluation check: Below threshold or zero probability. Probability: {self.current_reply_probability:.4f}") + return False + class InterestManager: _instance = None @@ -156,14 +275,14 @@ class InterestManager: """后台清理任务的异步函数""" while True: await asyncio.sleep(interval_seconds) - logger.info(f"Running periodic cleanup (interval: {interval_seconds}s)...") + 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) 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)""" while True: await asyncio.sleep(interval_seconds) - logger.debug(f"Running periodic history logging (interval: {interval_seconds}s)...") + logger.debug(f"运行定期历史记录 (间隔: {interval_seconds}秒)...") try: current_timestamp = time.time() all_states = self.get_all_interest_states() # 获取当前所有状态 @@ -190,7 +309,11 @@ class InterestManager: "timestamp": round(current_timestamp, 2), "stream_id": stream_id, "interest_level": state.get("interest_level", 0.0), # 确保有默认值 - "group_name": group_name # *** Add group_name *** + "group_name": group_name, # *** Add group_name *** + # --- 新增：记录概率相关信息 --- + "reply_probability": state.get("current_reply_probability", 0.0), + "is_above_threshold": state.get("is_above_threshold", False) + # --- 结束新增 --- } # 将每个条目作为单独的 JSON 行写入 f.write(json.dumps(log_entry, ensure_ascii=False) + '\n') @@ -230,7 +353,7 @@ class InterestManager: # logger.debug(f"Applied decay to {count} streams.") async def start_background_tasks(self): - """Starts the background cleanup, logging, and decay tasks.""" + """启动清理，启动衰减，启动记录，启动启动启动启动启动""" if self._cleanup_task is None or self._cleanup_task.done(): self._cleanup_task = asyncio.create_task( self._periodic_cleanup_task( @@ -239,26 +362,26 @@ class InterestManager: max_age_seconds=INACTIVE_THRESHOLD_SECONDS ) ) - logger.info(f"Periodic cleanup task created. Interval: {CLEANUP_INTERVAL_SECONDS}s, Inactive Threshold: {INACTIVE_THRESHOLD_SECONDS}s") + logger.info(f"已创建定期清理任务。间隔时间: {CLEANUP_INTERVAL_SECONDS}秒, 不活跃阈值: {INACTIVE_THRESHOLD_SECONDS}秒") else: - logger.warning("Cleanup task creation skipped: already running or exists.") + logger.warning("跳过创建清理任务:任务已在运行或存在。") if self._logging_task is None or self._logging_task.done(): self._logging_task = asyncio.create_task( self._periodic_log_task(interval_seconds=LOG_INTERVAL_SECONDS) ) - logger.info(f"Periodic logging task created. Interval: {LOG_INTERVAL_SECONDS}s") + logger.info(f"已创建定期日志任务。间隔时间: {LOG_INTERVAL_SECONDS}秒") else: - logger.warning("Logging task creation skipped: already running or exists.") + logger.warning("跳过创建日志任务:任务已在运行或存在。") # 启动新的衰减任务 if self._decay_task is None or self._decay_task.done(): self._decay_task = asyncio.create_task( self._periodic_decay_task() ) - logger.info("Periodic decay task created. Interval: 1s") + logger.info("已创建定期衰减任务。间隔时间: 1秒") else: - logger.warning("Decay task creation skipped: already running or exists.") + logger.warning("跳过创建衰减任务:任务已在运行或存在。") def get_all_interest_states(self) -> dict[str, dict]: """获取所有聊天流的当前兴趣状态""" @@ -287,7 +410,16 @@ class InterestManager: # with self._lock: if stream_id not in self.interest_dict: logger.debug(f"Creating new InterestChatting for stream_id: {stream_id}") - self.interest_dict[stream_id] = InterestChatting() + # --- 修改：创建时传入概率相关参数 (如果需要定制化，否则使用默认值) --- + self.interest_dict[stream_id] = InterestChatting( + # decay_rate=..., max_interest=..., # 可以从配置读取 + trigger_threshold=REPLY_TRIGGER_THRESHOLD, # 使用全局常量 + base_reply_probability=BASE_REPLY_PROBABILITY, + increase_rate=PROBABILITY_INCREASE_RATE_PER_SECOND, + decay_factor=PROBABILITY_DECAY_FACTOR_PER_SECOND, + max_probability=MAX_REPLY_PROBABILITY + ) + # --- 结束修改 --- # 首次创建时兴趣为 0，由第一次消息的 activate rate 决定初始值 return self.interest_dict[stream_id] @@ -298,13 +430,13 @@ class InterestManager: # 直接调用修改后的 get_interest，不传入时间 return interest_chatting.get_interest() - def increase_interest(self, stream_id: str, value: float, message: MessageRecv): - """当收到消息时，增加指定聊天流的兴趣度，并传递关联消息""" + def increase_interest(self, stream_id: str, value: float): + """当收到消息时，增加指定聊天流的兴趣度""" current_time = time.time() interest_chatting = self._get_or_create_interest_chatting(stream_id) - # 调用修改后的 increase_interest，传入 message - interest_chatting.increase_interest(current_time, value, message) - logger.debug(f"Increased interest for stream_id: {stream_id} by {value:.2f} to {interest_chatting.interest_level:.2f}") # 更新日志 + # 调用修改后的 increase_interest，不再传入 message + interest_chatting.increase_interest(current_time, value) + logger.debug(f"增加了聊天流 {stream_id} 的兴趣度 {value:.2f}，当前值为 {interest_chatting.interest_level:.2f}") # 更新日志 def decrease_interest(self, stream_id: str, value: float): """降低指定聊天流的兴趣度""" @@ -313,13 +445,13 @@ class InterestManager: interest_chatting = self.get_interest_chatting(stream_id) if interest_chatting: interest_chatting.decrease_interest(current_time, value) - logger.debug(f"Decreased interest for stream_id: {stream_id} by {value:.2f} to {interest_chatting.interest_level:.2f}") + logger.debug(f"降低了聊天流 {stream_id} 的兴趣度 {value:.2f}，当前值为 {interest_chatting.interest_level:.2f}") else: - logger.warning(f"Attempted to decrease interest for non-existent stream_id: {stream_id}") + 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): """ - 清理长时间不活跃或兴趣度过低的聊天流记录 + 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 """ @@ -334,37 +466,27 @@ class InterestManager: # 先计算当前兴趣，确保是最新的 # 加锁保护 chatting 对象状态的读取和可能的修改 # with self._lock: # 如果 InterestChatting 内部操作不是原子的 - interest = chatting.get_interest() - last_update = chatting.last_update_time - + last_interaction = chatting.last_interaction_time # 使用最后交互时间 should_remove = False reason = "" - if interest < threshold: - should_remove = True - reason = f"interest ({interest:.2f}) < threshold ({threshold})" # 只有设置了 max_age_seconds 才检查时间 - if max_age_seconds is not None and (current_time - last_update) > max_age_seconds: + if max_age_seconds is not None and (current_time - last_interaction) > max_age_seconds: # 使用 last_interaction should_remove = True - reason = f"inactive time ({current_time - last_update:.0f}s) > max age ({max_age_seconds}s)" + (f", {reason}" if reason else "") # 附加之前的理由 + reason = f"inactive time ({current_time - last_interaction:.0f}s) > max age ({max_age_seconds}s)" # 更新日志信息 if should_remove: keys_to_remove.append(stream_id) logger.debug(f"Marking stream_id {stream_id} for removal. Reason: {reason}") if keys_to_remove: - logger.info(f"Cleanup identified {len(keys_to_remove)} inactive/low-interest streams.") + logger.info(f"清理识别到 {len(keys_to_remove)} 个不活跃/低兴趣的流。") # with self._lock: # 确保删除操作的原子性 for key in keys_to_remove: # 再次检查 key 是否存在，以防万一在迭代和删除之间状态改变 if key in self.interest_dict: del self.interest_dict[key] - logger.debug(f"Removed stream_id: {key}") + logger.debug(f"移除了流_id: {key}") final_count = initial_count - len(keys_to_remove) - logger.info(f"Cleanup finished. Removed {len(keys_to_remove)} streams. Current count: {final_count}") + logger.info(f"清理完成。移除了 {len(keys_to_remove)} 个流。当前数量: {final_count}") else: - logger.info(f"Cleanup finished. No streams met removal criteria. Current count: {initial_count}") - - -# 不再需要手动创建实例和任务 -# manager = InterestManager() -# asyncio.create_task(periodic_cleanup(manager, 3600)) \ No newline at end of file + logger.info(f"清理完成。没有流符合移除条件。当前数量: {initial_count}") \ No newline at end of file