import asyncio import time import traceback import random from typing import List, Optional, Dict, Any, Tuple, TYPE_CHECKING from rich.traceback import install from src.config.config import global_config from src.common.logger import get_logger from src.common.data_models.info_data_model import ActionPlannerInfo from src.common.data_models.message_data_model import ReplyContentType 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.heart_flow.hfc_utils import CycleDetail from src.express.expression_learner import expression_learner_manager from src.chat.frequency_control.frequency_control import frequency_control_manager from src.express.reflect_tracker import reflect_tracker_manager from src.express.expression_reflector import expression_reflector_manager from src.jargon import extract_and_store_jargon from src.person_info.person_info import Person from src.plugin_system.base.component_types import EventType, ActionInfo 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.utils.chat_message_builder import ( build_readable_messages_with_id, get_raw_msg_before_timestamp_with_chat, ) from src.hippo_memorizer.chat_history_summarizer import ChatHistorySummarizer if TYPE_CHECKING: from src.common.data_models.database_data_model import DatabaseMessages from src.common.data_models.message_data_model import ReplySetModel 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(), }, } 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.expression_learner = expression_learner_manager.get_expression_learner(self.stream_id) 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 # 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.last_read_time = time.time() - 2 self.no_reply_until_call = False self.is_mute = False self.last_active_time = time.time() # 记录上一次非noreply时间 self.question_probability_multiplier = 1 self.questioned = False # 跟踪连续 no_reply 次数，用于动态调整阈值 self.consecutive_no_reply_count = 0 # 聊天内容概括器 self.chat_history_summarizer = ChatHistorySummarizer(chat_id=self.stream_id) 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._loop_task = asyncio.create_task(self._main_chat_loop()) self._loop_task.add_done_callback(self._handle_loop_completion) # 启动聊天内容概括器的后台定期检查循环 await self.chat_history_summarizer.start() 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) -> Tuple[Dict[str, float], str]: 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 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(): if elapsed < 0.1: # 不显示小于0.1秒的计时器 continue formatted_time = 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"详情: {'; '.join(timer_strings)}" if timer_strings else "") ) async def _loopbody(self): recent_messages_list = message_api.get_messages_by_time_in_chat( chat_id=self.stream_id, start_time=self.last_read_time, end_time=time.time(), limit=20, limit_mode="latest", filter_mai=True, filter_command=False, filter_no_read_command=True, ) # 根据连续 no_reply 次数动态调整阈值 # 3次 no_reply 时，阈值调高到 1.5（50%概率为1，50%概率为2） # 5次 no_reply 时，提高到 2（大于等于两条消息的阈值） if self.consecutive_no_reply_count >= 5: threshold = 2 elif self.consecutive_no_reply_count >= 3: # 1.5 的含义：50%概率为1，50%概率为2 threshold = 2 if random.random() < 0.5 else 1 else: threshold = 1 if len(recent_messages_list) >= threshold: # for message in recent_messages_list: # print(message.processed_plain_text) # !处理no_reply_until_call逻辑 if self.no_reply_until_call: for message in recent_messages_list: if ( message.is_mentioned or message.is_at or len(recent_messages_list) >= 8 or time.time() - self.last_read_time > 600 ): self.no_reply_until_call = False self.last_read_time = time.time() break # 没有提到，继续保持沉默 if self.no_reply_until_call: # logger.info(f"{self.log_prefix} 没有提到，继续保持沉默") await asyncio.sleep(1) return True self.last_read_time = time.time() # !此处使at或者提及必定回复 mentioned_message = None for message in recent_messages_list: if (message.is_mentioned or message.is_at) and global_config.chat.mentioned_bot_reply: mentioned_message = message # logger.info(f"{self.log_prefix} 当前talk_value: {global_config.chat.get_talk_value(self.stream_id)}") # *控制频率用 if mentioned_message: await self._observe(recent_messages_list=recent_messages_list, force_reply_message=mentioned_message) elif ( random.random() < global_config.chat.get_talk_value(self.stream_id) * frequency_control_manager.get_or_create_frequency_control(self.stream_id).get_talk_frequency_adjust() ): await self._observe(recent_messages_list=recent_messages_list) else: # 没有提到，继续保持沉默，等待5秒防止频繁触发 await asyncio.sleep(10) return True else: await asyncio.sleep(0.2) return True return True async def _send_and_store_reply( self, response_set: "ReplySetModel", action_message: "DatabaseMessages", cycle_timers: Dict[str, float], thinking_id, actions, selected_expressions: Optional[List[int]] = None, ) -> Tuple[Dict[str, Any], str, Dict[str, float]]: with Timer("回复发送", cycle_timers): reply_text = await self._send_response( reply_set=response_set, message_data=action_message, selected_expressions=selected_expressions, ) # 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值 platform = action_message.chat_info.platform if platform is None: platform = getattr(self.chat_stream, "platform", "unknown") person = Person(platform=platform, user_id=action_message.user_info.user_id) person_name = person.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}, action_name="reply", ) # 构建循环信息 loop_info: Dict[str, Any] = { "loop_plan_info": { "action_result": actions, }, "loop_action_info": { "action_taken": True, "reply_text": reply_text, "command": "", "taken_time": time.time(), }, } return loop_info, reply_text, cycle_timers async def _run_planner_without_reply( self, available_actions: Dict[str, ActionInfo], cycle_timers: Dict[str, float], ) -> List[ActionPlannerInfo]: """执行planner，但不包含reply动作（用于并行执行场景，提及时使用简化版提示词）""" try: with Timer("规划器", cycle_timers): action_to_use_info = await self.action_planner.plan( loop_start_time=self.last_read_time, available_actions=available_actions, is_mentioned=True, # 标记为提及时，使用简化版提示词 ) # 过滤掉reply动作（虽然提及时不应该有reply，但为了安全还是过滤一下） return [action for action in action_to_use_info if action.action_type != "reply"] except Exception as e: logger.error(f"{self.log_prefix} Planner执行失败: {e}") traceback.print_exc() return [] async def _generate_mentioned_reply( self, force_reply_message: "DatabaseMessages", thinking_id: str, cycle_timers: Dict[str, float], available_actions: Dict[str, ActionInfo], ) -> Dict[str, Any]: """当被提及时，独立生成回复的任务""" try: self.questioned = False # 重置连续 no_reply 计数 self.consecutive_no_reply_count = 0 reason = "" await database_api.store_action_info( chat_stream=self.chat_stream, action_build_into_prompt=False, action_prompt_display=reason, action_done=True, thinking_id=thinking_id, action_data={}, action_name="reply", action_reasoning=reason, ) with Timer("提及回复生成", cycle_timers): success, llm_response = await generator_api.generate_reply( chat_stream=self.chat_stream, reply_message=force_reply_message, available_actions=available_actions, chosen_actions=[], # 独立回复，不依赖planner的动作 reply_reason=reason, enable_tool=global_config.tool.enable_tool, request_type="replyer", from_plugin=False, reply_time_point=self.last_read_time, ) if not success or not llm_response or not llm_response.reply_set: logger.warning(f"{self.log_prefix} 提及回复生成失败") return {"action_type": "reply", "success": False, "result": "提及回复生成失败", "loop_info": None} response_set = llm_response.reply_set selected_expressions = llm_response.selected_expressions loop_info, reply_text, _ = await self._send_and_store_reply( response_set=response_set, action_message=force_reply_message, cycle_timers=cycle_timers, thinking_id=thinking_id, actions=[], # 独立回复，不依赖planner的动作 selected_expressions=selected_expressions, ) self.last_active_time = time.time() return { "action_type": "reply", "success": True, "result": f"你回复内容{reply_text}", "loop_info": loop_info, } except Exception as e: logger.error(f"{self.log_prefix} 提及回复生成异常: {e}") traceback.print_exc() return {"action_type": "reply", "success": False, "result": f"提及回复生成异常: {e}", "loop_info": None} async def _observe( self, # interest_value: float = 0.0, recent_messages_list: Optional[List["DatabaseMessages"]] = None, force_reply_message: Optional["DatabaseMessages"] = None, ) -> bool: # sourcery skip: merge-else-if-into-elif, remove-redundant-if if recent_messages_list is None: recent_messages_list = [] _reply_text = "" # 初始化reply_text变量，避免UnboundLocalError # ------------------------------------------------------------------------- # ReflectTracker Check # 在每次回复前检查一次上下文，看是否有反思问题得到了解答 # ------------------------------------------------------------------------- reflector = expression_reflector_manager.get_or_create_reflector(self.stream_id) await reflector.check_and_ask() tracker = reflect_tracker_manager.get_tracker(self.stream_id) if tracker: resolved = await tracker.trigger_tracker() if resolved: reflect_tracker_manager.remove_tracker(self.stream_id) logger.info(f"{self.log_prefix} ReflectTracker resolved and removed.") start_time = time.time() async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()): asyncio.create_task(self.expression_learner.trigger_learning_for_chat()) asyncio.create_task( frequency_control_manager.get_or_create_frequency_control(self.stream_id).trigger_frequency_adjust() ) # 添加curious检测任务 - 检测聊天记录中的矛盾、冲突或需要提问的内容 # asyncio.create_task(check_and_make_question(self.stream_id)) # 添加jargon提取任务 - 提取聊天中的黑话/俚语并入库（内部自行取消息并带冷却） asyncio.create_task(extract_and_store_jargon(self.stream_id)) # 添加聊天内容概括任务 - 累积、打包和压缩聊天记录 # 注意：后台循环已在start()中启动，这里作为额外触发点，在有思考时立即处理 # asyncio.create_task(self.chat_history_summarizer.process()) cycle_timers, thinking_id = self.start_cycle() logger.info( f"{self.log_prefix} 开始第{self._cycle_counter}次思考(频率: {global_config.chat.get_talk_value(self.stream_id)})" ) # 第一步：动作检查 available_actions: Dict[str, ActionInfo] = {} 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}") # 如果被提及，让回复生成和planner并行执行 if force_reply_message: logger.info(f"{self.log_prefix} 检测到提及，回复生成与planner并行执行") # 并行执行planner和回复生成 planner_task = asyncio.create_task( self._run_planner_without_reply( available_actions=available_actions, cycle_timers=cycle_timers, ) ) reply_task = asyncio.create_task( self._generate_mentioned_reply( force_reply_message=force_reply_message, thinking_id=thinking_id, cycle_timers=cycle_timers, available_actions=available_actions, ) ) # 等待两个任务完成 planner_result, reply_result = await asyncio.gather(planner_task, reply_task, return_exceptions=True) # 处理planner结果 if isinstance(planner_result, BaseException): logger.error(f"{self.log_prefix} Planner执行异常: {planner_result}") action_to_use_info = [] else: action_to_use_info = planner_result # 处理回复结果 if isinstance(reply_result, BaseException): logger.error(f"{self.log_prefix} 回复生成异常: {reply_result}") reply_result = { "action_type": "reply", "success": False, "result": "回复生成异常", "loop_info": None, } else: # 正常流程：只执行planner is_group_chat, chat_target_info, _ = self.action_planner.get_necessary_info() message_list_before_now = get_raw_msg_before_timestamp_with_chat( chat_id=self.stream_id, timestamp=time.time(), limit=int(global_config.chat.max_context_size * 0.6), filter_no_read_command=True, ) chat_content_block, message_id_list = build_readable_messages_with_id( messages=message_list_before_now, timestamp_mode="normal_no_YMD", read_mark=self.action_planner.last_obs_time_mark, truncate=True, show_actions=True, ) prompt_info = await self.action_planner.build_planner_prompt( is_group_chat=is_group_chat, chat_target_info=chat_target_info, current_available_actions=available_actions, chat_content_block=chat_content_block, message_id_list=message_id_list, interest=global_config.personality.interest, ) continue_flag, modified_message = await events_manager.handle_mai_events( EventType.ON_PLAN, None, prompt_info[0], None, self.chat_stream.stream_id ) if not continue_flag: return False if modified_message and modified_message._modify_flags.modify_llm_prompt: prompt_info = (modified_message.llm_prompt, prompt_info[1]) with Timer("规划器", cycle_timers): action_to_use_info = await self.action_planner.plan( loop_start_time=self.last_read_time, available_actions=available_actions, ) reply_result = None # 只在提及情况下过滤掉planner返回的reply动作（提及时已有独立回复生成） if force_reply_message: action_to_use_info = [action for action in action_to_use_info if action.action_type != "reply"] logger.info( f"{self.log_prefix} 决定执行{len(action_to_use_info)}个动作: {' '.join([a.action_type for a in action_to_use_info])}" ) # 3. 并行执行所有动作（不包括reply，reply已经独立执行） action_tasks = [ asyncio.create_task( self._execute_action(action, action_to_use_info, thinking_id, available_actions, cycle_timers) ) for action in action_to_use_info ] # 并行执行所有任务 results = await asyncio.gather(*action_tasks, return_exceptions=True) # 如果有独立的回复结果，添加到结果列表中 if reply_result: results = list(results) + [reply_result] # 处理执行结果 reply_loop_info = None reply_text_from_reply = "" action_success = False action_reply_text = "" excute_result_str = "" for result in results: excute_result_str += f"{result['action_type']} 执行结果:{result['result']}\n" if isinstance(result, BaseException): logger.error(f"{self.log_prefix} 动作执行异常: {result}") continue if result["action_type"] != "reply": action_success = result["success"] action_reply_text = result["result"] elif result["action_type"] == "reply": if result["success"]: reply_loop_info = result["loop_info"] reply_text_from_reply = result["result"] else: logger.warning(f"{self.log_prefix} 回复动作执行失败") self.action_planner.add_plan_excute_log(result=excute_result_str) # 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, "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": action_to_use_info, }, "loop_action_info": { "action_taken": action_success, "reply_text": action_reply_text, "taken_time": time.time(), }, } _reply_text = action_reply_text self.end_cycle(loop_info, cycle_timers) self.print_cycle_info(cycle_timers) end_time = time.time() if end_time - start_time < global_config.chat.planner_smooth: wait_time = global_config.chat.planner_smooth - (end_time - start_time) await asyncio.sleep(wait_time) else: await asyncio.sleep(0.1) 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, action_reasoning: str, action_data: dict, cycle_timers: Dict[str, float], thinking_id: str, action_message: Optional["DatabaseMessages"] = None, ) -> 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 action_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 action_message: 消息数据 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, cycle_timers=cycle_timers, thinking_id=thinking_id, chat_stream=self.chat_stream, log_prefix=self.log_prefix, action_reasoning=action_reasoning, 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, "" # 处理动作并获取结果（固定记录一次动作信息） result = await action_handler.execute() success, action_text = result return success, action_text except Exception as e: logger.error(f"{self.log_prefix} 处理{action}时出错: {e}") traceback.print_exc() return False, "" async def _send_response( self, reply_set: "ReplySetModel", message_data: "DatabaseMessages", selected_expressions: Optional[List[int]] = None, ) -> str: new_message_count = message_api.count_new_messages( chat_id=self.chat_stream.stream_id, start_time=self.last_read_time, end_time=time.time() ) need_reply = new_message_count >= random.randint(2, 3) 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") reply_text = "" first_replied = False for reply_content in reply_set.reply_data: if reply_content.content_type != ReplyContentType.TEXT: continue data: str = reply_content.content # type: ignore if not first_replied: await send_api.text_to_stream( text=data, stream_id=self.chat_stream.stream_id, reply_message=message_data, set_reply=need_reply, typing=False, selected_expressions=selected_expressions, ) first_replied = True else: await send_api.text_to_stream( text=data, stream_id=self.chat_stream.stream_id, reply_message=message_data, set_reply=False, typing=True, selected_expressions=selected_expressions, ) reply_text += data return reply_text async def _execute_action( self, action_planner_info: ActionPlannerInfo, chosen_action_plan_infos: List[ActionPlannerInfo], thinking_id: str, available_actions: Dict[str, ActionInfo], cycle_timers: Dict[str, float], ): """执行单个动作的通用函数""" try: with Timer(f"动作{action_planner_info.action_type}", cycle_timers): # 直接当场执行no_reply逻辑 if action_planner_info.action_type == "no_reply": # 直接处理no_reply逻辑，不再通过动作系统 reason = action_planner_info.reasoning or "选择不回复" # logger.info(f"{self.log_prefix} 选择不回复，原因: {reason}") # 增加连续 no_reply 计数 self.consecutive_no_reply_count += 1 await database_api.store_action_info( chat_stream=self.chat_stream, action_build_into_prompt=False, action_prompt_display=reason, action_done=True, thinking_id=thinking_id, action_data={}, action_name="no_reply", action_reasoning=reason, ) return {"action_type": "no_reply", "success": True, "result": "选择不回复", "command": ""} elif action_planner_info.action_type == "no_reply_until_call": # 直接当场执行no_reply_until_call逻辑 logger.info(f"{self.log_prefix} 保持沉默，直到有人直接叫的名字") reason = action_planner_info.reasoning or "选择不回复" # 增加连续 no_reply 计数 self.consecutive_no_reply_count += 1 self.no_reply_until_call = True await database_api.store_action_info( chat_stream=self.chat_stream, action_build_into_prompt=False, action_prompt_display=reason, action_done=True, thinking_id=thinking_id, action_data={}, action_name="no_reply_until_call", action_reasoning=reason, ) return { "action_type": "no_reply_until_call", "success": True, "result": "保持沉默，直到有人直接叫的名字", "command": "", } elif action_planner_info.action_type == "reply": # 直接当场执行reply逻辑 self.questioned = False # 刷新主动发言状态 # 重置连续 no_reply 计数 self.consecutive_no_reply_count = 0 reason = action_planner_info.reasoning or "" # 使用 action_reasoning（planner 的整体思考理由）作为 reply_reason planner_reasoning = action_planner_info.action_reasoning or reason await database_api.store_action_info( chat_stream=self.chat_stream, action_build_into_prompt=False, action_prompt_display=reason, action_done=True, thinking_id=thinking_id, action_data={}, action_name="reply", action_reasoning=reason, ) success, llm_response = await generator_api.generate_reply( chat_stream=self.chat_stream, reply_message=action_planner_info.action_message, available_actions=available_actions, chosen_actions=chosen_action_plan_infos, reply_reason=planner_reasoning, enable_tool=global_config.tool.enable_tool, request_type="replyer", from_plugin=False, reply_time_point=action_planner_info.action_data.get("loop_start_time", time.time()), ) if not success or not llm_response or not llm_response.reply_set: if action_planner_info.action_message: logger.info(f"对 {action_planner_info.action_message.processed_plain_text} 的回复生成失败") else: logger.info("回复生成失败") return {"action_type": "reply", "success": False, "result": "回复生成失败", "loop_info": None} response_set = llm_response.reply_set selected_expressions = llm_response.selected_expressions loop_info, reply_text, _ = await self._send_and_store_reply( response_set=response_set, action_message=action_planner_info.action_message, # type: ignore cycle_timers=cycle_timers, thinking_id=thinking_id, actions=chosen_action_plan_infos, selected_expressions=selected_expressions, ) self.last_active_time = time.time() return { "action_type": "reply", "success": True, "result": f"你回复内容{reply_text}", "loop_info": loop_info, } else: # 执行普通动作 with Timer("动作执行", cycle_timers): success, result = await self._handle_action( action=action_planner_info.action_type, action_reasoning=action_planner_info.action_reasoning or "", action_data=action_planner_info.action_data or {}, cycle_timers=cycle_timers, thinking_id=thinking_id, action_message=action_planner_info.action_message, ) self.last_active_time = time.time() return { "action_type": action_planner_info.action_type, "success": success, "result": result, } except Exception as e: logger.error(f"{self.log_prefix} 执行动作时出错: {e}") logger.error(f"{self.log_prefix} 错误信息: {traceback.format_exc()}") return { "action_type": action_planner_info.action_type, "success": False, "result": "", "loop_info": None, "error": str(e), }