import asyncio import time import traceback import random from typing import List, Optional, Dict, Any, Tuple from rich.traceback import install from src.config.config import global_config from src.common.logger import get_logger from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager from src.chat.utils.prompt_builder import global_prompt_manager from src.chat.utils.timer_calculator import Timer from src.chat.planner_actions.planner import ActionPlanner from src.chat.planner_actions.action_modifier import ActionModifier from src.chat.planner_actions.action_manager import ActionManager from src.chat.chat_loop.hfc_utils import CycleDetail from src.person_info.relationship_builder_manager import relationship_builder_manager from src.chat.express.expression_learner import expression_learner_manager from src.person_info.person_info import get_person_info_manager from src.plugin_system.base.component_types import ActionInfo, ChatMode, EventType from src.plugin_system.core import events_manager from src.plugin_system.apis import generator_api, send_api, message_api, database_api from src.chat.willing.willing_manager import get_willing_manager from src.mais4u.mai_think import mai_thinking_manager from src.mais4u.constant_s4u import ENABLE_S4U from src.chat.chat_loop.hfc_utils import send_typing, stop_typing ERROR_LOOP_INFO = { "loop_plan_info": { "action_result": { "action_type": "error", "action_data": {}, "reasoning": "循环处理失败", }, }, "loop_action_info": { "action_taken": False, "reply_text": "", "command": "", "taken_time": time.time(), }, } NO_ACTION = { "action_result": { "action_type": "no_action", "action_data": {}, "reasoning": "规划器初始化默认", "is_parallel": True, }, "chat_context": "", "action_prompt": "", } install(extra_lines=3) # 注释：原来的动作修改超时常量已移除，因为改为顺序执行 logger = get_logger("hfc") # Logger Name Changed class HeartFChatting: """ 管理一个连续的Focus Chat循环 用于在特定聊天流中生成回复。 其生命周期现在由其关联的 SubHeartflow 的 FOCUSED 状态控制。 """ def __init__( self, chat_id: str, ): """ HeartFChatting 初始化函数 参数: chat_id: 聊天流唯一标识符(如stream_id) on_stop_focus_chat: 当收到stop_focus_chat命令时调用的回调函数 performance_version: 性能记录版本号，用于区分不同启动版本 """ # 基础属性 self.stream_id: str = chat_id # 聊天流ID self.chat_stream: ChatStream = get_chat_manager().get_stream(self.stream_id) # type: ignore if not self.chat_stream: raise ValueError(f"无法找到聊天流: {self.stream_id}") self.log_prefix = f"[{get_chat_manager().get_stream_name(self.stream_id) or self.stream_id}]" self.relationship_builder = relationship_builder_manager.get_or_create_builder(self.stream_id) self.expression_learner = expression_learner_manager.get_expression_learner(self.stream_id) self.loop_mode = ChatMode.NORMAL # 初始循环模式为普通模式 self.last_action = "no_action" self.action_manager = ActionManager() self.action_planner = ActionPlanner(chat_id=self.stream_id, action_manager=self.action_manager) self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.stream_id) # 循环控制内部状态 self.running: bool = False self._loop_task: Optional[asyncio.Task] = None # 主循环任务 self._energy_task: Optional[asyncio.Task] = None # 添加循环信息管理相关的属性 self.history_loop: List[CycleDetail] = [] self._cycle_counter = 0 self._current_cycle_detail: CycleDetail = None # type: ignore self.reply_timeout_count = 0 self.plan_timeout_count = 0 self.last_read_time = time.time() - 1 self.willing_manager = get_willing_manager() logger.info(f"{self.log_prefix} HeartFChatting 初始化完成") self.energy_value = 5 # 根据配置初始化聊天模式和能量值 is_group_chat = self.chat_stream.group_info is not None if is_group_chat and global_config.chat.group_chat_mode != "auto": if global_config.chat.group_chat_mode == "focus": self.loop_mode = ChatMode.FOCUS self.energy_value = 35 logger.info(f"{self.log_prefix} 群聊强制专注模式已启用，能量值设置为35") elif global_config.chat.group_chat_mode == "normal": self.loop_mode = ChatMode.NORMAL self.energy_value = 15 logger.info(f"{self.log_prefix} 群聊强制普通模式已启用，能量值设置为15") self.focus_energy = 1 # 能量值日志时间控制 self.last_energy_log_time = 0 # 上次记录能量值日志的时间 self.energy_log_interval = 90 # 能量值日志间隔（秒） # 主动思考功能相关属性 self.last_message_time = time.time() # 最后一条消息的时间 self._proactive_thinking_task: Optional[asyncio.Task] = None # 主动思考任务 async def start(self): """检查是否需要启动主循环，如果未激活则启动。""" # 如果循环已经激活，直接返回 if self.running: logger.debug(f"{self.log_prefix} HeartFChatting 已激活，无需重复启动") return try: # 标记为活动状态，防止重复启动 self.running = True self._energy_task = asyncio.create_task(self._energy_loop()) self._energy_task.add_done_callback(self._handle_energy_completion) # 启动主动思考任务（仅在群聊且启用的情况下） if (global_config.chat.enable_proactive_thinking and self.chat_stream.group_info is not None): self._proactive_thinking_task = asyncio.create_task(self._proactive_thinking_loop()) self._proactive_thinking_task.add_done_callback(self._handle_proactive_thinking_completion) self._loop_task = asyncio.create_task(self._main_chat_loop()) self._loop_task.add_done_callback(self._handle_loop_completion) logger.info(f"{self.log_prefix} HeartFChatting 启动完成") except Exception as e: # 启动失败时重置状态 self.running = False self._loop_task = None logger.error(f"{self.log_prefix} HeartFChatting 启动失败: {e}") raise def _handle_loop_completion(self, task: asyncio.Task): """当 _hfc_loop 任务完成时执行的回调。""" try: if exception := task.exception(): logger.error(f"{self.log_prefix} HeartFChatting: 脱离了聊天(异常): {exception}") logger.error(traceback.format_exc()) # Log full traceback for exceptions else: logger.info(f"{self.log_prefix} HeartFChatting: 脱离了聊天 (外部停止)") except asyncio.CancelledError: logger.info(f"{self.log_prefix} HeartFChatting: 结束了聊天") def start_cycle(self): self._cycle_counter += 1 self._current_cycle_detail = CycleDetail(self._cycle_counter) self._current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}" cycle_timers = {} return cycle_timers, self._current_cycle_detail.thinking_id def end_cycle(self, loop_info, cycle_timers): self._current_cycle_detail.set_loop_info(loop_info) self.history_loop.append(self._current_cycle_detail) self._current_cycle_detail.timers = cycle_timers self._current_cycle_detail.end_time = time.time() def _handle_energy_completion(self, task: asyncio.Task): """当 energy_loop 任务完成时执行的回调。""" try: if exception := task.exception(): logger.error(f"{self.log_prefix} 能量循环异常: {exception}") else: logger.info(f"{self.log_prefix} 能量循环正常结束") except asyncio.CancelledError: logger.info(f"{self.log_prefix} 能量循环被取消") def _handle_proactive_thinking_completion(self, task: asyncio.Task): """当 proactive_thinking_loop 任务完成时执行的回调。""" try: if exception := task.exception(): logger.error(f"{self.log_prefix} 主动思考循环异常: {exception}") else: logger.info(f"{self.log_prefix} 主动思考循环正常结束") except asyncio.CancelledError: logger.info(f"{self.log_prefix} 主动思考循环被取消") """处理能量循环任务的完成""" if task.cancelled(): logger.info(f"{self.log_prefix} 能量循环任务被取消") elif task.exception(): logger.error(f"{self.log_prefix} 能量循环任务发生异常: {task.exception()}") def _should_log_energy(self) -> bool: """判断是否应该记录能量值日志（基于时间间隔控制）""" current_time = time.time() if current_time - self.last_energy_log_time >= self.energy_log_interval: self.last_energy_log_time = current_time return True return False def _log_energy_change(self, action: str, reason: str = ""): """记录能量值变化日志（受时间间隔控制）""" if self._should_log_energy(): if reason: logger.info(f"{self.log_prefix} {action}，{reason}，当前能量值：{self.energy_value:.1f}") else: logger.info(f"{self.log_prefix} {action}，当前能量值：{self.energy_value:.1f}") else: # 仍然以debug级别记录，便于调试 if reason: logger.debug(f"{self.log_prefix} {action}，{reason}，当前能量值：{self.energy_value:.1f}") else: logger.debug(f"{self.log_prefix} {action}，当前能量值：{self.energy_value:.1f}") async def _energy_loop(self): while self.running: await asyncio.sleep(10) # 检查是否为群聊且配置了强制模式 is_group_chat = self.chat_stream.group_info is not None if is_group_chat and global_config.chat.group_chat_mode != "auto": # 强制模式下固定能量值和聊天模式 if global_config.chat.group_chat_mode == "focus": self.loop_mode = ChatMode.FOCUS self.energy_value = 35 # 强制设置为35 elif global_config.chat.group_chat_mode == "normal": self.loop_mode = ChatMode.NORMAL self.energy_value = 15 # 强制设置为15 continue # 跳过正常的能量值衰减逻辑 # 原有的自动模式逻辑 if self.loop_mode == ChatMode.NORMAL: self.energy_value -= 0.3 self.energy_value = max(self.energy_value, 0.3) if self.loop_mode == ChatMode.FOCUS: self.energy_value -= 0.6 self.energy_value = max(self.energy_value, 0.3) async def _proactive_thinking_loop(self): """主动思考循环，仅在focus模式下生效""" while self.running: await asyncio.sleep(30) # 每30秒检查一次 # 只在focus模式下进行主动思考 if self.loop_mode != ChatMode.FOCUS: continue current_time = time.time() silence_duration = current_time - self.last_message_time # 检查是否达到主动思考的时间间隔 if silence_duration >= global_config.chat.proactive_thinking_interval: try: await self._execute_proactive_thinking(silence_duration) # 重置计时器，避免频繁触发 self.last_message_time = current_time except Exception as e: logger.error(f"{self.log_prefix} 主动思考执行出错: {e}") logger.error(traceback.format_exc()) def _format_duration(self, seconds: float) -> str: """格式化时间间隔为易读格式""" hours = int(seconds // 3600) minutes = int((seconds % 3600) // 60) secs = int(seconds % 60) parts = [] if hours > 0: parts.append(f"{hours}小时") if minutes > 0: parts.append(f"{minutes}分") if secs > 0 or not parts: # 如果没有小时和分钟，显示秒 parts.append(f"{secs}秒") return "".join(parts) async def _execute_proactive_thinking(self, silence_duration: float): """执行主动思考""" formatted_time = self._format_duration(silence_duration) logger.info(f"{self.log_prefix} 触发主动思考，已沉默{formatted_time}") try: # 构建主动思考的prompt proactive_prompt = global_config.chat.proactive_thinking_prompt_template.format( time=formatted_time ) # 创建一个虚拟的消息数据用于主动思考 """ 因为主动思考是在没有用户消息的情况下触发的 但规划器仍然需要一个"消息"作为输入来工作 所以需要"伪造"一个消息来触发思考流程，本质上是系统与自己的对话，让AI能够主动思考和决策。 """ thinking_message = { "processed_plain_text": proactive_prompt, "user_id": "system_proactive_thinking", "user_platform": "system", "timestamp": time.time(), "message_type": "proactive_thinking", "user_nickname": "系统主动思考", "chat_info_platform": "system", "message_id": f"proactive_{int(time.time())}" } # 使用现有的_observe方法来处理主动思考 # 这样可以复用现有的完整思考流程 logger.info(f"{self.log_prefix} 开始主动思考...") await self._observe(message_data=thinking_message) logger.info(f"{self.log_prefix} 主动思考完成") except Exception as e: logger.error(f"{self.log_prefix} 主动思考执行异常: {e}") logger.error(traceback.format_exc()) def print_cycle_info(self, cycle_timers): # 记录循环信息和计时器结果 timer_strings = [] for name, elapsed in cycle_timers.items(): formatted_time = f"{elapsed * 1000:.2f}毫秒" if elapsed < 1 else f"{elapsed:.2f}秒" timer_strings.append(f"{name}: {formatted_time}") logger.info( f"{self.log_prefix} 第{self._current_cycle_detail.cycle_id}次思考," f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒, " # type: ignore f"选择动作: {self._current_cycle_detail.loop_plan_info.get('action_result', {}).get('action_type', '未知动作')}" + (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "") ) async def _loopbody(self): recent_messages_dict = message_api.get_messages_by_time_in_chat( chat_id=self.stream_id, start_time=self.last_read_time, end_time=time.time(), limit = 10, limit_mode="latest", filter_mai=True, filter_command=True, ) new_message_count = len(recent_messages_dict) # 如果有新消息，更新最后消息时间（用于主动思考计时） if new_message_count > 0: current_time = time.time() self.last_message_time = current_time if self.loop_mode == ChatMode.FOCUS: # focus模式下，在有新消息时进行观察思考 # 主动思考由独立的 _proactive_thinking_loop 处理 if new_message_count > 0: self.last_read_time = time.time() if await self._observe(): # 在强制模式下，能量值不会因观察而增加 is_group_chat = self.chat_stream.group_info is not None if not (is_group_chat and global_config.chat.group_chat_mode != "auto"): self.energy_value += 1 / global_config.chat.focus_value self._log_energy_change("能量值增加") # 检查是否应该退出专注模式 # 如果开启了强制私聊专注模式且当前为私聊，则不允许退出专注状态 is_private_chat = self.chat_stream.group_info is None is_group_chat = self.chat_stream.group_info is not None if global_config.chat.force_focus_private and is_private_chat: # 强制私聊专注模式下，保持专注状态，但重置能量值防止过低 if self.energy_value <= 1: self.energy_value = 5 # 重置为较低但足够的能量值 return True # 群聊强制专注模式下，不允许退出专注状态 if is_group_chat and global_config.chat.group_chat_mode == "focus": return True if self.energy_value <= 1: self.energy_value = 1 self.loop_mode = ChatMode.NORMAL return True return True elif self.loop_mode == ChatMode.NORMAL: # 检查是否应该强制进入专注模式（私聊且开启强制专注） is_private_chat = self.chat_stream.group_info is None is_group_chat = self.chat_stream.group_info is not None if global_config.chat.force_focus_private and is_private_chat: self.loop_mode = ChatMode.FOCUS self.energy_value = 10 # 设置初始能量值 return True # 群聊强制普通模式下，不允许进入专注状态 if is_group_chat and global_config.chat.group_chat_mode == "normal": # 在强制普通模式下，即使满足条件也不进入专注模式 pass elif global_config.chat.focus_value != 0: if new_message_count > 3 / pow(global_config.chat.focus_value, 0.5): self.loop_mode = ChatMode.FOCUS self.energy_value = ( 10 + (new_message_count / (3 / pow(global_config.chat.focus_value, 0.5))) * 10 ) return True if self.energy_value >= 30: self.loop_mode = ChatMode.FOCUS return True if new_message_count >= self.focus_energy: earliest_messages_data = recent_messages_dict[0] self.last_read_time = earliest_messages_data.get("time") if_think = await self.normal_response(earliest_messages_data) # 在强制模式下，能量值变化逻辑需要特殊处理 is_group_chat = self.chat_stream.group_info is not None if is_group_chat and global_config.chat.group_chat_mode != "auto": # 强制模式下不改变能量值 pass elif if_think: factor = max(global_config.chat.focus_value, 0.1) self.energy_value *= 1.1 * factor self._log_energy_change("进行了思考，能量值按倍数增加") else: self.energy_value += 0.1 * global_config.chat.focus_value self._log_energy_change("没有进行思考，能量值线性增加") # 这个可以保持debug级别，因为它是总结性信息 logger.debug(f"{self.log_prefix} 当前能量值：{self.energy_value:.1f}") return True await asyncio.sleep(0.5) return True async def build_reply_to_str(self, message_data: dict): person_info_manager = get_person_info_manager() # 获取平台信息，优先使用chat_info_platform，如果为None则使用user_platform platform = message_data.get("chat_info_platform") or message_data.get("user_platform") or self.chat_stream.platform user_id = message_data.get("user_id") person_id = person_info_manager.get_person_id(platform, user_id) person_name = await person_info_manager.get_value(person_id, "person_name") return f"{person_name}:{message_data.get('processed_plain_text')}" async def _send_and_store_reply( self, response_set, reply_to_str, loop_start_time, action_message, cycle_timers: Dict[str, float], thinking_id, plan_result, ) -> Tuple[Dict[str, Any], str, Dict[str, float]]: with Timer("回复发送", cycle_timers): reply_text = await self._send_response(response_set, reply_to_str, loop_start_time, action_message) # 存储reply action信息 person_info_manager = get_person_info_manager() # 获取平台信息，优先使用chat_info_platform，如果为空则使用user_platform platform = action_message.get("chat_info_platform") or action_message.get("user_platform") or self.chat_stream.platform user_id = action_message.get("user_id", "") person_id = person_info_manager.get_person_id(platform, user_id) person_name = await person_info_manager.get_value(person_id, "person_name") action_prompt_display = f"你对{person_name}进行了回复：{reply_text}" await database_api.store_action_info( chat_stream=self.chat_stream, action_build_into_prompt=False, action_prompt_display=action_prompt_display, action_done=True, thinking_id=thinking_id, action_data={"reply_text": reply_text, "reply_to": reply_to_str}, action_name="reply", ) # 构建循环信息 loop_info: Dict[str, Any] = { "loop_plan_info": { "action_result": plan_result.get("action_result", {}), }, "loop_action_info": { "action_taken": True, "reply_text": reply_text, "command": "", "taken_time": time.time(), }, } return loop_info, reply_text, cycle_timers async def _observe(self, message_data: Optional[Dict[str, Any]] = None) -> bool: if not message_data: message_data = {} action_type = "no_action" reply_text = "" # 初始化reply_text变量，避免UnboundLocalError gen_task = None # 初始化gen_task变量，避免UnboundLocalError reply_to_str = "" # 初始化reply_to_str变量 # 创建新的循环信息 cycle_timers, thinking_id = self.start_cycle() logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考[模式：{self.loop_mode}]") if ENABLE_S4U: await send_typing() async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()): loop_start_time = time.time() await self.relationship_builder.build_relation() await self.expression_learner.trigger_learning_for_chat() available_actions = {} # 第一步：动作修改 with Timer("动作修改", cycle_timers): try: await self.action_modifier.modify_actions() available_actions = self.action_manager.get_using_actions() except Exception as e: logger.error(f"{self.log_prefix} 动作修改失败: {e}") # 检查是否在normal模式下没有可用动作（除了reply相关动作） skip_planner = False if self.loop_mode == ChatMode.NORMAL: # 过滤掉reply相关的动作，检查是否还有其他动作 non_reply_actions = { k: v for k, v in available_actions.items() if k not in ["reply", "no_reply", "no_action"] } if not non_reply_actions: skip_planner = True logger.info(f"{self.log_prefix} Normal模式下没有可用动作，直接回复") # 直接设置为reply动作 action_type = "reply" reasoning = "" action_data = {"loop_start_time": loop_start_time} is_parallel = False # 构建plan_result用于后续处理 plan_result = { "action_result": { "action_type": action_type, "action_data": action_data, "reasoning": reasoning, "timestamp": time.time(), "is_parallel": is_parallel, }, "action_prompt": "", } target_message = message_data # 如果normal模式且不跳过规划器，开始一个回复生成进程，先准备好回复（其实是和planer同时进行的） if not skip_planner: reply_to_str = await self.build_reply_to_str(message_data) gen_task = asyncio.create_task( self._generate_response( message_data=message_data, available_actions=available_actions, reply_to=reply_to_str, request_type="chat.replyer.normal", ) ) if not skip_planner: planner_info = self.action_planner.get_necessary_info() prompt_info = await self.action_planner.build_planner_prompt( is_group_chat=planner_info[0], chat_target_info=planner_info[1], current_available_actions=planner_info[2], ) if not await events_manager.handle_mai_events( EventType.ON_PLAN, None, prompt_info[0], None, self.chat_stream.stream_id ): return False with Timer("规划器", cycle_timers): plan_result, target_message = await self.action_planner.plan(mode=self.loop_mode) action_result: Dict[str, Any] = plan_result.get("action_result", {}) # type: ignore action_type, action_data, reasoning, is_parallel = ( action_result.get("action_type", "error"), action_result.get("action_data", {}), action_result.get("reasoning", "未提供理由"), action_result.get("is_parallel", True), ) action_data["loop_start_time"] = loop_start_time # 在私聊的专注模式下，如果规划动作为no_reply，则强制改为reply is_private_chat = self.chat_stream.group_info is None if self.loop_mode == ChatMode.FOCUS and is_private_chat and action_type == "no_reply": action_type = "reply" logger.info(f"{self.log_prefix} 私聊专注模式下强制回复") if action_type == "reply": logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复") elif is_parallel: logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复, 同时执行{action_type}动作") else: # 只有在gen_task存在时才进行相关操作 if gen_task: if not gen_task.done(): gen_task.cancel() logger.debug(f"{self.log_prefix} 已取消预生成的回复任务") logger.info( f"{self.log_prefix}{global_config.bot.nickname} 原本想要回复，但选择执行{action_type}，不发表回复" ) elif generation_result := gen_task.result(): content = " ".join([item[1] for item in generation_result if item[0] == "text"]) logger.debug(f"{self.log_prefix} 预生成的回复任务已完成") logger.info( f"{self.log_prefix}{global_config.bot.nickname} 原本想要回复：{content}，但选择执行{action_type}，不发表回复" ) else: logger.warning(f"{self.log_prefix} 预生成的回复任务未生成有效内容") action_message = target_message or message_data if action_type == "reply": # 等待回复生成完毕 if self.loop_mode == ChatMode.NORMAL: # 只有在gen_task存在时才等待 if not gen_task: reply_to_str = await self.build_reply_to_str(message_data) gen_task = asyncio.create_task( self._generate_response( message_data=message_data, available_actions=available_actions, reply_to=reply_to_str, request_type="chat.replyer.normal", ) ) gather_timeout = global_config.chat.thinking_timeout try: response_set = await asyncio.wait_for(gen_task, timeout=gather_timeout) except asyncio.TimeoutError: logger.warning(f"{self.log_prefix} 回复生成超时>{global_config.chat.thinking_timeout}s，已跳过") response_set = None # 模型炸了或超时，没有回复内容生成 if not response_set: logger.warning(f"{self.log_prefix}模型未生成回复内容") return False else: logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复 (focus模式)") # 构建reply_to字符串 reply_to_str = await self.build_reply_to_str(action_message) # 生成回复 with Timer("回复生成", cycle_timers): response_set = await self._generate_response( message_data=action_message, available_actions=available_actions, reply_to=reply_to_str, request_type="chat.replyer.focus", ) if not response_set: logger.warning(f"{self.log_prefix}模型未生成回复内容") return False loop_info, reply_text, cycle_timers = await self._send_and_store_reply( response_set, reply_to_str, loop_start_time, action_message, cycle_timers, thinking_id, plan_result ) return True else: # 并行执行：同时进行回复发送和动作执行 # 先置空防止未定义错误 background_reply_task = None background_action_task = None # 如果是并行执行且在normal模式下，需要等待预生成的回复任务完成并发送回复 if self.loop_mode == ChatMode.NORMAL and is_parallel and gen_task: async def handle_reply_task() -> Tuple[Optional[Dict[str, Any]], str, Dict[str, float]]: # 等待预生成的回复任务完成 gather_timeout = global_config.chat.thinking_timeout try: response_set = await asyncio.wait_for(gen_task, timeout=gather_timeout) except asyncio.TimeoutError: logger.warning( f"{self.log_prefix} 并行执行：回复生成超时>{global_config.chat.thinking_timeout}s，已跳过" ) return None, "", {} except asyncio.CancelledError: logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消") return None, "", {} if not response_set: logger.warning(f"{self.log_prefix} 模型超时或生成回复内容为空") return None, "", {} reply_to_str = await self.build_reply_to_str(action_message) loop_info, reply_text, cycle_timers_reply = await self._send_and_store_reply( response_set, reply_to_str, loop_start_time, action_message, cycle_timers, thinking_id, plan_result, ) return loop_info, reply_text, cycle_timers_reply # 执行回复任务并赋值到变量 background_reply_task = asyncio.create_task(handle_reply_task()) # 动作执行任务 async def handle_action_task(): with Timer("动作执行", cycle_timers): success, reply_text, command = await self._handle_action( action_type, reasoning, action_data, cycle_timers, thinking_id, action_message ) return success, reply_text, command # 执行动作任务并赋值到变量 background_action_task = asyncio.create_task(handle_action_task()) reply_loop_info = None reply_text_from_reply = "" action_success = False action_reply_text = "" action_command = "" # 并行执行所有任务 if background_reply_task: results = await asyncio.gather( background_reply_task, background_action_task, return_exceptions=True ) # 处理回复任务结果 reply_result = results[0] if isinstance(reply_result, BaseException): logger.error(f"{self.log_prefix} 回复任务执行异常: {reply_result}") elif reply_result and reply_result[0] is not None: reply_loop_info, reply_text_from_reply, _ = reply_result # 处理动作任务结果 action_task_result = results[1] if isinstance(action_task_result, BaseException): logger.error(f"{self.log_prefix} 动作任务执行异常: {action_task_result}") else: action_success, action_reply_text, action_command = action_task_result else: results = await asyncio.gather(background_action_task, return_exceptions=True) # 只有动作任务 action_task_result = results[0] if isinstance(action_task_result, BaseException): logger.error(f"{self.log_prefix} 动作任务执行异常: {action_task_result}") else: action_success, action_reply_text, action_command = action_task_result # 构建最终的循环信息 if reply_loop_info: # 如果有回复信息，使用回复的loop_info作为基础 loop_info = reply_loop_info # 更新动作执行信息 loop_info["loop_action_info"].update( { "action_taken": action_success, "command": action_command, "taken_time": time.time(), } ) reply_text = reply_text_from_reply else: # 没有回复信息，构建纯动作的loop_info loop_info = { "loop_plan_info": { "action_result": plan_result.get("action_result", {}), }, "loop_action_info": { "action_taken": action_success, "reply_text": action_reply_text, "command": action_command, "taken_time": time.time(), }, } reply_text = action_reply_text self.last_action = action_type if ENABLE_S4U: await stop_typing() await mai_thinking_manager.get_mai_think(self.stream_id).do_think_after_response(reply_text) self.end_cycle(loop_info, cycle_timers) self.print_cycle_info(cycle_timers) if self.loop_mode == ChatMode.NORMAL: await self.willing_manager.after_generate_reply_handle(message_data.get("message_id", "")) # 管理动作状态：当执行了非no_reply动作时进行记录 if action_type != "no_reply" and action_type != "no_action": logger.info(f"{self.log_prefix} 执行了{action_type}动作") return True elif action_type == "no_action": logger.info(f"{self.log_prefix} 执行了回复动作") return True async def _main_chat_loop(self): """主循环，持续进行计划并可能回复消息，直到被外部取消。""" try: while self.running: # 主循环 success = await self._loopbody() await asyncio.sleep(0.1) if not success: break except asyncio.CancelledError: # 设置了关闭标志位后被取消是正常流程 logger.info(f"{self.log_prefix} 麦麦已关闭聊天") except Exception: logger.error(f"{self.log_prefix} 麦麦聊天意外错误，将于3s后尝试重新启动") print(traceback.format_exc()) await asyncio.sleep(3) self._loop_task = asyncio.create_task(self._main_chat_loop()) logger.error(f"{self.log_prefix} 结束了当前聊天循环") async def _handle_action( self, action: str, reasoning: str, action_data: dict, cycle_timers: Dict[str, float], thinking_id: str, action_message: dict, ) -> tuple[bool, str, str]: """ 处理规划动作，使用动作工厂创建相应的动作处理器 参数: action: 动作类型 reasoning: 决策理由 action_data: 动作数据，包含不同动作需要的参数 cycle_timers: 计时器字典 thinking_id: 思考ID 返回: tuple[bool, str, str]: (是否执行了动作, 思考消息ID, 命令) """ try: # 使用工厂创建动作处理器实例 try: action_handler = self.action_manager.create_action( action_name=action, action_data=action_data, reasoning=reasoning, cycle_timers=cycle_timers, thinking_id=thinking_id, chat_stream=self.chat_stream, log_prefix=self.log_prefix, action_message=action_message, ) except Exception as e: logger.error(f"{self.log_prefix} 创建动作处理器时出错: {e}") traceback.print_exc() return False, "", "" if not action_handler: logger.warning(f"{self.log_prefix} 未能创建动作处理器: {action}") return False, "", "" # 处理动作并获取结果 result = await action_handler.handle_action() success, reply_text = result command = "" if reply_text == "timeout": self.reply_timeout_count += 1 if self.reply_timeout_count > 5: logger.warning( f"[{self.log_prefix} ] 连续回复超时次数过多，{global_config.chat.thinking_timeout}秒 内大模型没有返回有效内容，请检查你的api是否速度过慢或配置错误。建议不要使用推理模型，推理模型生成速度过慢。或者尝试拉高thinking_timeout参数，这可能导致回复时间过长。" ) logger.warning(f"{self.log_prefix} 回复生成超时{global_config.chat.thinking_timeout}s，已跳过") return False, "", "" return success, reply_text, command except Exception as e: logger.error(f"{self.log_prefix} 处理{action}时出错: {e}") traceback.print_exc() return False, "", "" async def normal_response(self, message_data: dict) -> bool: """ 处理接收到的消息。 在"兴趣"模式下，判断是否回复并生成内容。 """ interested_rate = message_data.get("interest_value") or 0.0 self.willing_manager.setup(message_data, self.chat_stream) reply_probability = await self.willing_manager.get_reply_probability(message_data.get("message_id", "")) talk_frequency = -1.00 if reply_probability < 1: # 简化逻辑，如果未提及 (reply_probability 为 0)，则获取意愿概率 additional_config = message_data.get("additional_config", {}) if additional_config and "maimcore_reply_probability_gain" in additional_config: reply_probability += additional_config["maimcore_reply_probability_gain"] reply_probability = min(max(reply_probability, 0), 1) # 确保概率在 0-1 之间 talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id) reply_probability = talk_frequency * reply_probability # 处理表情包 if message_data.get("is_emoji") or message_data.get("is_picid"): reply_probability = 0 # 打印消息信息 mes_name = self.chat_stream.group_info.group_name if self.chat_stream.group_info else "私聊" # logger.info(f"[{mes_name}] 当前聊天频率: {talk_frequency:.2f},兴趣值: {interested_rate:.2f},回复概率: {reply_probability * 100:.1f}%") if reply_probability > 0.05: logger.info( f"[{mes_name}]" f"{message_data.get('user_nickname')}:" f"{message_data.get('processed_plain_text')}[兴趣:{interested_rate:.2f}][回复概率:{reply_probability * 100:.1f}%]" ) if random.random() < reply_probability: await self.willing_manager.before_generate_reply_handle(message_data.get("message_id", "")) await self._observe(message_data=message_data) return True # 意愿管理器：注销当前message信息 (无论是否回复，只要处理过就删除) self.willing_manager.delete(message_data.get("message_id", "")) return False async def _generate_response( self, message_data: dict, available_actions: Optional[Dict[str, ActionInfo]], reply_to: str, request_type: str = "chat.replyer.normal", ) -> Optional[list]: """生成普通回复""" try: success, reply_set, _ = await generator_api.generate_reply( chat_stream=self.chat_stream, reply_to=reply_to, available_actions=available_actions, enable_tool=global_config.tool.enable_tool, request_type=request_type, from_plugin=False, ) if not success or not reply_set: logger.info(f"对 {message_data.get('processed_plain_text')} 的回复生成失败") return None return reply_set except Exception as e: logger.error(f"{self.log_prefix}回复生成出现错误：{str(e)} {traceback.format_exc()}") return None async def _send_response(self, reply_set, reply_to, thinking_start_time, message_data) -> str: current_time = time.time() new_message_count = message_api.count_new_messages( chat_id=self.chat_stream.stream_id, start_time=thinking_start_time, end_time=current_time ) platform = message_data.get("user_platform", "") user_id = message_data.get("user_id", "") reply_to_platform_id = f"{platform}:{user_id}" need_reply = new_message_count >= random.randint(2, 4) if need_reply: logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，使用引用回复") else: logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，不使用引用回复") reply_text = "" # 检查是否为主动思考且决定沉默 is_proactive_thinking = message_data.get("message_type") == "proactive_thinking" first_replied = False for reply_seg in reply_set: data = reply_seg[1] reply_text += data # 如果是主动思考且回复内容是"沉默"，则不发送消息 if is_proactive_thinking and data.strip() == "沉默": logger.info(f"{self.log_prefix} 主动思考决定保持沉默，不发送消息") continue if not first_replied: if need_reply: await send_api.text_to_stream( text=data, stream_id=self.chat_stream.stream_id, reply_to=reply_to, reply_to_platform_id=reply_to_platform_id, typing=False, ) else: await send_api.text_to_stream( text=data, stream_id=self.chat_stream.stream_id, reply_to_platform_id=reply_to_platform_id, typing=False, ) first_replied = True else: await send_api.text_to_stream( text=data, stream_id=self.chat_stream.stream_id, reply_to_platform_id=reply_to_platform_id, typing=True, ) return reply_text