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.brain_chat.brain_planner import BrainPlanner 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.bw_learner.expression_learner import expression_learner_manager from src.bw_learner.message_recorder import extract_and_distribute_messages 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, ) 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("bc") # Logger Name Changed class BrainChatting: """ 管理一个连续的私聊Brain Chat循环 用于在特定聊天流中生成回复。 """ def __init__(self, chat_id: str): """ BrainChatting 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 = BrainPlanner(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.more_plan = False # 最近一次是否成功进行了 reply，用于选择 BrainPlanner 的 Prompt self._last_successful_reply: bool = False async def start(self): """检查是否需要启动主循环，如果未激活则启动。""" # 如果循环已经激活，直接返回 if self.running: logger.debug(f"{self.log_prefix} BrainChatting 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) logger.info(f"{self.log_prefix} BrainChatting 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} BrainChatting 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} BrainChatting: 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} BrainChatting: 脱离了聊天 (外部停止)") except asyncio.CancelledError: logger.info(f"{self.log_prefix} BrainChatting: 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(): 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"\n详情: {'; '.join(timer_strings)}" if timer_strings else "") ) async def _loopbody(self): # sourcery skip: hoist-if-from-if # 获取最新消息（用于上下文，但不影响是否调用 observe） recent_messages_list = message_api.get_messages_by_time_in_chat( chat_id=self.stream_id, start_time=self.last_read_time, end_time=time.time(), limit=20, limit_mode="latest", filter_mai=True, filter_command=False, filter_intercept_message_level=1, ) # 如果有新消息，更新 last_read_time if len(recent_messages_list) >= 1: self.last_read_time = time.time() # 总是执行一次思考迭代（不管有没有新消息） # wait 动作会在其内部等待，不需要在这里处理 should_continue = await self._observe(recent_messages_list=recent_messages_list) if not should_continue: # 选择了 complete_talk，返回 False 表示需要等待新消息 return False # 继续下一次迭代（除非选择了 complete_talk） # 短暂等待后再继续，避免过于频繁的循环 await asyncio.sleep(0.1) return True async def _send_and_store_reply( 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 _observe( self, # interest_value: float = 0.0, recent_messages_list: Optional[List["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 # ------------------------------------------------------------------------- from src.bw_learner.reflect_tracker import reflect_tracker_manager 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.") # ------------------------------------------------------------------------- # Expression Reflection Check # 检查是否需要提问表达反思 # ------------------------------------------------------------------------- from src.bw_learner.expression_reflector import expression_reflector_manager reflector = expression_reflector_manager.get_or_create_reflector(self.stream_id) asyncio.create_task(reflector.check_and_ask()) async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()): # 通过 MessageRecorder 统一提取消息并分发给 expression_learner 和 jargon_miner # 在 replyer 执行时触发，统一管理时间窗口，避免重复获取消息 asyncio.create_task(extract_and_distribute_messages(self.stream_id)) cycle_timers, thinking_id = self.start_cycle() logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考") # 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}") # 获取必要信息 is_group_chat, chat_target_info, _ = self.action_planner.get_necessary_info() # 一次思考迭代：Think - Act - Observe # 获取聊天上下文 message_list_before_now = get_raw_msg_before_timestamp_with_chat( chat_id=self.stream_id, timestamp=time.time(), limit=int(global_config.chat.max_context_size * 0.6), filter_intercept_message_level=1, ) 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( chat_target_info=chat_target_info, current_available_actions=available_actions, chat_content_block=chat_content_block, message_id_list=message_id_list, prompt_key="brain_planner_prompt_react", ) continue_flag, modified_message = await events_manager.handle_mai_events( EventType.ON_PLAN, None, prompt_info[0], None, self.chat_stream.stream_id ) 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, ) # 检查是否有 complete_talk 动作（会停止后续迭代） has_complete_talk = any( action.action_type == "complete_talk" for action in action_to_use_info ) # 并行执行所有动作 action_tasks = [ asyncio.create_task( self._execute_action(action, action_to_use_info, thinking_id, available_actions, cycle_timers) ) for action in action_to_use_info ] # 并行执行所有任务 results = await asyncio.gather(*action_tasks, return_exceptions=True) # 处理执行结果 reply_loop_info = None reply_text_from_reply = "" action_success = False action_reply_text = "" for result in results: 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["reply_text"] elif result["action_type"] == "reply": if result["success"]: reply_loop_info = result["loop_info"] reply_text_from_reply = result["reply_text"] else: logger.warning(f"{self.log_prefix} 回复动作执行失败") # 更新观察时间标记 self.action_planner.last_obs_time_mark = time.time() # 如果选择了 complete_talk，标记为完成，不再继续迭代 if has_complete_talk: logger.info(f"{self.log_prefix} 检测到 complete_talk 动作，本次思考完成") # 构建循环信息 if reply_loop_info: # 如果有回复信息，使用回复的loop_info作为基础 loop_info = reply_loop_info # 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 # 如果选择了 complete_talk，返回 False 以停止 _loopbody 的循环 # 否则返回 True，让 _loopbody 继续下一次迭代 should_continue = not has_complete_talk self.end_cycle(loop_info, cycle_timers) self.print_cycle_info(cycle_timers) # 如果选择了 complete_talk，返回 False 停止循环 # 否则返回 True，继续下一次思考迭代 return should_continue async def _main_chat_loop(self): """主循环，持续进行计划并可能回复消息，直到被外部取消。""" try: while self.running: # 主循环 success = await self._loopbody() if not success: # 选择了 complete，等待新消息 logger.info(f"{self.log_prefix} 选择了 complete，等待新消息...") await self._wait_for_new_message() # 有新消息后继续循环 continue await asyncio.sleep(0.1) except asyncio.CancelledError: # 设置了关闭标志位后被取消是正常流程 logger.info(f"{self.log_prefix} 麦麦已关闭聊天") 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 _wait_for_new_message(self): """等待新消息到达""" last_check_time = self.last_read_time check_interval = 1.0 # 每秒检查一次 while self.running: # 检查是否有新消息 recent_messages_list = message_api.get_messages_by_time_in_chat( chat_id=self.stream_id, start_time=last_check_time, end_time=time.time(), limit=20, limit_mode="latest", filter_mai=True, filter_command=False, filter_intercept_message_level=1, ) # 如果有新消息，更新 last_read_time 并返回 if len(recent_messages_list) >= 1: self.last_read_time = time.time() logger.info(f"{self.log_prefix} 检测到新消息，恢复循环") return # 等待一段时间后再次检查 await asyncio.sleep(check_interval) async def _handle_action( self, action: str, 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 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, action_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（固定记录一次动作信息） # BaseAction 定义了异步方法 execute() 作为统一执行入口 # 这里调用 execute() 以兼容所有 Action 实现 result = await action_handler.execute() success, action_text = result command = "" return success, action_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 _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, 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") 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): if action_planner_info.action_type == "complete_talk": # 直接处理complete_talk逻辑，不再通过动作系统 reason = action_planner_info.reasoning or "选择完成对话" logger.info(f"{self.log_prefix} 选择完成对话，原因: {reason}") # 存储complete_talk信息到数据库 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={"reason": reason}, action_name="complete_talk", ) return {"action_type": "complete_talk", "success": True, "reply_text": "", "command": ""} elif action_planner_info.action_type == "reply": try: 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=action_planner_info.reasoning or "", enable_tool=global_config.tool.enable_tool, request_type="replyer", from_plugin=False, ) 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, "reply_text": "", "loop_info": None, } except asyncio.CancelledError: logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消") return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None} response_set = llm_response.reply_set selected_expressions = llm_response.selected_expressions loop_info, reply_text, _ = await self._send_and_store_reply( 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, ) # 标记这次循环已经成功进行了回复 self._last_successful_reply = True return { "action_type": "reply", "success": True, "reply_text": reply_text, "loop_info": loop_info, } # 其他动作 else: # 内建 wait / listening：不通过插件系统，直接在这里处理 if action_planner_info.action_type in ["wait", "listening"]: reason = action_planner_info.reasoning or "" action_data = action_planner_info.action_data or {} if action_planner_info.action_type == "wait": # 获取等待时间（必填） wait_seconds = action_data.get("wait_seconds") if wait_seconds is None: logger.warning(f"{self.log_prefix} wait 动作缺少 wait_seconds 参数，使用默认值 5 秒") wait_seconds = 5 else: try: wait_seconds = float(wait_seconds) if wait_seconds < 0: logger.warning(f"{self.log_prefix} wait_seconds 不能为负数，使用默认值 5 秒") wait_seconds = 5 except (ValueError, TypeError): logger.warning(f"{self.log_prefix} wait_seconds 参数格式错误，使用默认值 5 秒") wait_seconds = 5 logger.info(f"{self.log_prefix} 执行 wait 动作，等待 {wait_seconds} 秒") # 记录动作信息 await database_api.store_action_info( chat_stream=self.chat_stream, action_build_into_prompt=False, action_prompt_display=reason or f"等待 {wait_seconds} 秒", action_done=True, thinking_id=thinking_id, action_data={"reason": reason, "wait_seconds": wait_seconds}, action_name="wait", ) # 等待指定时间 await asyncio.sleep(wait_seconds) logger.info(f"{self.log_prefix} wait 动作完成，继续下一次思考") # 这些动作本身不产生文本回复 self._last_successful_reply = False return { "action_type": "wait", "success": True, "reply_text": "", "command": "", } # listening 已合并到 wait，如果遇到则转换为 wait（向后兼容） elif action_planner_info.action_type == "listening": logger.debug(f"{self.log_prefix} 检测到 listening 动作，已合并到 wait，自动转换") # 使用默认等待时间 wait_seconds = 3 logger.info(f"{self.log_prefix} 执行 listening（转换为 wait）动作，等待 {wait_seconds} 秒") # 记录动作信息 await database_api.store_action_info( chat_stream=self.chat_stream, action_build_into_prompt=False, action_prompt_display=reason or f"倾听并等待 {wait_seconds} 秒", action_done=True, thinking_id=thinking_id, action_data={"reason": reason, "wait_seconds": wait_seconds}, action_name="listening", ) # 等待指定时间 await asyncio.sleep(wait_seconds) logger.info(f"{self.log_prefix} listening 动作完成，继续下一次思考") # 这些动作本身不产生文本回复 self._last_successful_reply = False return { "action_type": "listening", "success": True, "reply_text": "", "command": "", } # 其余动作：走原有插件 Action 体系 with Timer("动作执行", cycle_timers): success, reply_text, command = await self._handle_action( action_planner_info.action_type, action_planner_info.reasoning or "", action_planner_info.action_data or {}, cycle_timers, thinking_id, action_planner_info.action_message, ) # 非 reply 类动作执行成功时，清空最近成功回复标记，让下一轮回到 initial Prompt if success and action_planner_info.action_type != "reply": self._last_successful_reply = False return { "action_type": action_planner_info.action_type, "success": success, "reply_text": reply_text, "command": command, } 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, "reply_text": "", "loop_info": None, "error": str(e), }