import asyncio import contextlib import time import traceback from collections import deque from typing import List, Optional, Dict, Any, Deque, Callable, Awaitable from src.chat.message_receive.chat_stream import ChatStream from src.chat.message_receive.chat_stream import chat_manager from rich.traceback import install from src.chat.utils.prompt_builder import global_prompt_manager from src.common.logger_manager import get_logger from src.chat.utils.timer_calculator import Timer from src.chat.heart_flow.observation.observation import Observation from src.chat.focus_chat.heartFC_Cycleinfo import CycleDetail from src.chat.focus_chat.info.info_base import InfoBase from src.chat.focus_chat.info_processors.chattinginfo_processor import ChattingInfoProcessor from src.chat.focus_chat.info_processors.mind_processor import MindProcessor from src.chat.focus_chat.info_processors.working_memory_processor import WorkingMemoryProcessor # from src.chat.focus_chat.info_processors.action_processor import ActionProcessor from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation from src.chat.heart_flow.observation.working_observation import WorkingMemoryObservation from src.chat.heart_flow.observation.structure_observation import StructureObservation from src.chat.heart_flow.observation.actions_observation import ActionObservation from src.chat.focus_chat.info_processors.tool_processor import ToolProcessor from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor from src.chat.focus_chat.replyer.default_replyer import DefaultReplyer from src.chat.focus_chat.memory_activator import MemoryActivator from src.chat.focus_chat.info_processors.base_processor import BaseProcessor from src.chat.focus_chat.info_processors.self_processor import SelfProcessor from src.chat.focus_chat.planners.planner_factory import PlannerFactory from src.chat.focus_chat.planners.modify_actions import ActionModifier from src.chat.focus_chat.planners.action_manager import ActionManager from src.chat.focus_chat.working_memory.working_memory import WorkingMemory from src.config.config import global_config install(extra_lines=3) # 定义处理器映射：键是处理器名称，值是 (处理器类, 可选的配置键名) # 如果配置键名为 None，则该处理器默认启用且不能通过 focus_chat_processor 配置禁用 PROCESSOR_CLASSES = { "ChattingInfoProcessor": (ChattingInfoProcessor, None), "MindProcessor": (MindProcessor, "mind_processor"), "ToolProcessor": (ToolProcessor, "tool_use_processor"), "WorkingMemoryProcessor": (WorkingMemoryProcessor, "working_memory_processor"), "SelfProcessor": (SelfProcessor, "self_identify_processor"), } logger = get_logger("hfc") # Logger Name Changed async def _handle_cycle_delay(action_taken_this_cycle: bool, cycle_start_time: float, log_prefix: str): """处理循环延迟""" cycle_duration = time.monotonic() - cycle_start_time try: sleep_duration = 0.0 if not action_taken_this_cycle and cycle_duration < 1: sleep_duration = 1 - cycle_duration elif cycle_duration < 0.2: sleep_duration = 0.2 if sleep_duration > 0: await asyncio.sleep(sleep_duration) except asyncio.CancelledError: logger.info(f"{log_prefix} Sleep interrupted, loop likely cancelling.") raise 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, observations: list[Observation], on_stop_focus_chat: Optional[Callable[[], Awaitable[None]]] = None, ): """ HeartFChatting 初始化函数 参数: chat_id: 聊天流唯一标识符(如stream_id) observations: 关联的观察列表 on_stop_focus_chat: 当收到stop_focus_chat命令时调用的回调函数 """ # 基础属性 self.stream_id: str = chat_id # 聊天流ID self.chat_stream: Optional[ChatStream] = None # 关联的聊天流 self.log_prefix: str = str(chat_id) # Initial default, will be updated self.hfcloop_observation = HFCloopObservation(observe_id=self.stream_id) self.chatting_observation = observations[0] self.structure_observation = StructureObservation(observe_id=self.stream_id) self.memory_activator = MemoryActivator() self.working_memory = WorkingMemory(chat_id=self.stream_id) self.working_observation = WorkingMemoryObservation( observe_id=self.stream_id, working_memory=self.working_memory ) # 根据配置文件和默认规则确定启用的处理器 self.enabled_processor_names: List[str] = [] config_processor_settings = global_config.focus_chat_processor for proc_name, (_proc_class, config_key) in PROCESSOR_CLASSES.items(): if config_key: # 此处理器可通过配置控制 if getattr(config_processor_settings, config_key, True): # 默认启用 (如果配置中未指定该键) self.enabled_processor_names.append(proc_name) else: # 此处理器不在配置映射中 (config_key is None)，默认启用 self.enabled_processor_names.append(proc_name) logger.info(f"{self.log_prefix} 将启用的处理器: {self.enabled_processor_names}") self.processors: List[BaseProcessor] = [] self._register_default_processors() self.expressor = DefaultExpressor(chat_id=self.stream_id) self.replyer = DefaultReplyer(chat_id=self.stream_id) self.action_manager = ActionManager() self.action_planner = PlannerFactory.create_planner(log_prefix=self.log_prefix, action_manager=self.action_manager) self.action_modifier = ActionModifier(action_manager=self.action_manager) self.action_observation = ActionObservation(observe_id=self.stream_id) self.action_observation.set_action_manager(self.action_manager) self.all_observations = observations # 初始化状态控制 self._initialized = False self._processing_lock = asyncio.Lock() # 循环控制内部状态 self._loop_active: bool = False # 循环是否正在运行 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._cycle_counter = 0 self._cycle_history: Deque[CycleDetail] = deque(maxlen=10) # 保留最近10个循环的信息 self._current_cycle_detail: Optional[CycleDetail] = None self._shutting_down: bool = False # 关闭标志位 # 存储回调函数 self.on_stop_focus_chat = on_stop_focus_chat async def _initialize(self) -> bool: """ 执行懒初始化操作 功能: 1. 获取聊天类型(群聊/私聊)和目标信息 2. 获取聊天流对象 3. 设置日志前缀 返回: bool: 初始化是否成功 注意: - 如果已经初始化过会直接返回True - 需要获取chat_stream对象才能继续后续操作 """ # 如果已经初始化过，直接返回成功 if self._initialized: return True try: await self.expressor.initialize() await self.replyer.initialize() self.chat_stream = await asyncio.to_thread(chat_manager.get_stream, self.stream_id) self.expressor.chat_stream = self.chat_stream self.replyer.chat_stream = self.chat_stream self.log_prefix = f"[{chat_manager.get_stream_name(self.stream_id) or self.stream_id}]" except Exception as e: logger.error(f"[HFC:{self.stream_id}] 初始化HFC时发生错误: {e}") return False # 标记初始化完成 self._initialized = True logger.debug(f"{self.log_prefix} 初始化完成，准备开始处理消息") return True def _register_default_processors(self): """根据 self.enabled_processor_names 注册信息处理器""" self.processors = [] # 清空已有的 for name in self.enabled_processor_names: # 'name' is "ChattingInfoProcessor", etc. processor_info = PROCESSOR_CLASSES.get(name) # processor_info is (ProcessorClass, config_key) if processor_info: processor_actual_class = processor_info[0] # 获取实际的类定义 # 根据处理器类名判断是否需要 subheartflow_id if name in ["MindProcessor", "ToolProcessor", "WorkingMemoryProcessor", "SelfProcessor"]: self.processors.append(processor_actual_class(subheartflow_id=self.stream_id)) elif name == "ChattingInfoProcessor": self.processors.append(processor_actual_class()) else: # 对于PROCESSOR_CLASSES中定义但此处未明确处理构造的处理器 # (例如, 新增了一个处理器到PROCESSOR_CLASSES, 它不需要id, 也不叫ChattingInfoProcessor) try: self.processors.append(processor_actual_class()) # 尝试无参构造 logger.debug(f"{self.log_prefix} 注册处理器 {name} (尝试无参构造).") except TypeError: logger.error( f"{self.log_prefix} 处理器 {name} 构造失败。它可能需要参数（如 subheartflow_id）但未在注册逻辑中明确处理。" ) else: # 这理论上不应该发生，因为 enabled_processor_names 是从 PROCESSOR_CLASSES 的键生成的 logger.warning( f"{self.log_prefix} 在 PROCESSOR_CLASSES 中未找到名为 '{name}' 的处理器定义，将跳过注册。" ) if self.processors: logger.info( f"{self.log_prefix} 已根据配置和默认规则注册处理器: {[p.__class__.__name__ for p in self.processors]}" ) else: logger.warning(f"{self.log_prefix} 没有注册任何处理器。这可能是由于配置错误或所有处理器都被禁用了。") async def start(self): """ 启动 HeartFChatting 的主循环。 注意：调用此方法前必须确保已经成功初始化。 """ logger.info(f"{self.log_prefix} 开始认真聊天(HFC)...") await self._start_loop_if_needed() async def _start_loop_if_needed(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._loop_active: return # 标记为活动状态，防止重复启动 self._loop_active = True # 检查是否已有任务在运行（理论上不应该，因为 _loop_active=False） if self._loop_task and not self._loop_task.done(): logger.warning(f"{self.log_prefix} 发现之前的循环任务仍在运行（不符合预期）。取消旧任务。") self._loop_task.cancel() try: # 等待旧任务确实被取消 await asyncio.wait_for(self._loop_task, timeout=0.5) except (asyncio.CancelledError, asyncio.TimeoutError): pass # 忽略取消或超时错误 self._loop_task = None # 清理旧任务引用 self._loop_task = asyncio.create_task(self._run_focus_chat()) self._loop_task.add_done_callback(self._handle_loop_completion) def _handle_loop_completion(self, task: asyncio.Task): """当 _hfc_loop 任务完成时执行的回调。""" try: exception = task.exception() if 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: 脱离了聊天(任务取消)") finally: self._loop_active = False self._loop_task = None if self._processing_lock.locked(): logger.warning(f"{self.log_prefix} HeartFChatting: 处理锁在循环结束时仍被锁定，强制释放。") self._processing_lock.release() async def _run_focus_chat(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 True: # 主循环 logger.debug(f"{self.log_prefix} 开始第{self._cycle_counter}次循环") if self._shutting_down: logger.info(f"{self.log_prefix} 检测到关闭标志，退出 Focus Chat 循环。") break # 创建新的循环信息 self._cycle_counter += 1 self._current_cycle_detail = CycleDetail(self._cycle_counter) self._current_cycle_detail.prefix = self.log_prefix # 初始化周期状态 cycle_timers = {} loop_cycle_start_time = time.monotonic() # 执行规划和处理阶段 async with self._get_cycle_context(): thinking_id = "tid" + str(round(time.time(), 2)) self._current_cycle_detail.set_thinking_id(thinking_id) # 主循环：思考->决策->执行 async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()): logger.debug(f"模板 {self.chat_stream.context.get_template_name()}") loop_info = await self._observe_process_plan_action_loop(cycle_timers, thinking_id) if loop_info["loop_action_info"]["command"] == "stop_focus_chat": logger.info(f"{self.log_prefix} 麦麦决定停止专注聊天") # 如果设置了回调函数，则调用它 if self.on_stop_focus_chat: try: await self.on_stop_focus_chat() logger.info(f"{self.log_prefix} 成功调用回调函数处理停止专注聊天") except Exception as e: logger.error(f"{self.log_prefix} 调用停止专注聊天回调函数时出错: {e}") logger.error(traceback.format_exc()) break self._current_cycle_detail.set_loop_info(loop_info) self.hfcloop_observation.add_loop_info(self._current_cycle_detail) self._current_cycle_detail.timers = cycle_timers # 防止循环过快消耗资源 await _handle_cycle_delay( loop_info["loop_action_info"]["action_taken"], loop_cycle_start_time, self.log_prefix ) # 完成当前循环并保存历史 self._current_cycle_detail.complete_cycle() self._cycle_history.append(self._current_cycle_detail) # 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}") # 新增：输出每个处理器的耗时 processor_time_costs = self._current_cycle_detail.loop_processor_info.get("processor_time_costs", {}) processor_time_strings = [] for pname, ptime in processor_time_costs.items(): formatted_ptime = f"{ptime * 1000:.2f}毫秒" if ptime < 1 else f"{ptime:.2f}秒" processor_time_strings.append(f"{pname}: {formatted_ptime}") processor_time_log = ( ("\n各处理器耗时: " + "; ".join(processor_time_strings)) if processor_time_strings else "" ) 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, " f"动作: {self._current_cycle_detail.loop_plan_info['action_result']['action_type']}" + (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "") + processor_time_log ) await asyncio.sleep(global_config.focus_chat.think_interval) except asyncio.CancelledError: # 设置了关闭标志位后被取消是正常流程 if not self._shutting_down: logger.warning(f"{self.log_prefix} 麦麦Focus聊天模式意外被取消") else: logger.info(f"{self.log_prefix} 麦麦已离开Focus聊天模式") except Exception as e: logger.error(f"{self.log_prefix} 麦麦Focus聊天模式意外错误: {e}") print(traceback.format_exc()) @contextlib.asynccontextmanager async def _get_cycle_context(self): """ 循环周期的上下文管理器 用于确保资源的正确获取和释放： 1. 获取处理锁 2. 执行操作 3. 释放锁 """ acquired = False try: await self._processing_lock.acquire() acquired = True yield acquired finally: if acquired and self._processing_lock.locked(): self._processing_lock.release() async def _process_processors( self, observations: List[Observation], running_memorys: List[Dict[str, Any]] ) -> tuple[List[InfoBase], Dict[str, float]]: # 记录并行任务开始时间 parallel_start_time = time.time() logger.debug(f"{self.log_prefix} 开始信息处理器并行任务") processor_tasks = [] task_to_name_map = {} processor_time_costs = {} # 新增: 记录每个处理器耗时 for processor in self.processors: processor_name = processor.__class__.log_prefix async def run_with_timeout(proc=processor): return await asyncio.wait_for( proc.process_info(observations=observations, running_memorys=running_memorys), timeout=global_config.focus_chat.processor_max_time, ) task = asyncio.create_task(run_with_timeout()) processor_tasks.append(task) task_to_name_map[task] = processor_name logger.debug(f"{self.log_prefix} 启动处理器任务: {processor_name}") pending_tasks = set(processor_tasks) all_plan_info: List[InfoBase] = [] while pending_tasks: done, pending_tasks = await asyncio.wait(pending_tasks, return_when=asyncio.FIRST_COMPLETED) for task in done: processor_name = task_to_name_map[task] task_completed_time = time.time() duration_since_parallel_start = task_completed_time - parallel_start_time try: result_list = await task logger.info(f"{self.log_prefix} 处理器 {processor_name} 已完成!") if result_list is not None: all_plan_info.extend(result_list) else: logger.warning(f"{self.log_prefix} 处理器 {processor_name} 返回了 None") # 记录耗时 processor_time_costs[processor_name] = duration_since_parallel_start except asyncio.TimeoutError: logger.info( f"{self.log_prefix} 处理器 {processor_name} 超时（>{global_config.focus_chat.processor_max_time}s），已跳过" ) processor_time_costs[processor_name] = global_config.focus_chat.processor_max_time except Exception as e: logger.error( f"{self.log_prefix} 处理器 {processor_name} 执行失败，耗时 (自并行开始): {duration_since_parallel_start:.2f}秒. 错误: {e}", exc_info=True, ) traceback.print_exc() processor_time_costs[processor_name] = duration_since_parallel_start if pending_tasks: current_progress_time = time.time() elapsed_for_log = current_progress_time - parallel_start_time pending_names_for_log = [task_to_name_map[t] for t in pending_tasks] logger.info( f"{self.log_prefix} 信息处理已进行 {elapsed_for_log:.2f}秒，待完成任务: {', '.join(pending_names_for_log)}" ) # 所有任务完成后的最终日志 parallel_end_time = time.time() total_duration = parallel_end_time - parallel_start_time logger.info(f"{self.log_prefix} 所有处理器任务全部完成，总耗时: {total_duration:.2f}秒") # logger.debug(f"{self.log_prefix} 所有信息处理器处理后的信息: {all_plan_info}") return all_plan_info, processor_time_costs async def _observe_process_plan_action_loop(self, cycle_timers: dict, thinking_id: str) -> dict: try: with Timer("观察", cycle_timers): await self.chatting_observation.observe() await self.working_observation.observe() await self.hfcloop_observation.observe() await self.structure_observation.observe() observations: List[Observation] = [] observations.append(self.chatting_observation) observations.append(self.working_observation) observations.append(self.hfcloop_observation) observations.append(self.structure_observation) loop_observation_info = { "observations": observations, } self.all_observations = observations with Timer("调整动作", cycle_timers): # 处理特殊的观察 await self.action_modifier.modify_actions(observations=observations) await self.action_observation.observe() observations.append(self.action_observation) # 根据配置决定是否并行执行回忆和处理器阶段 # print(global_config.focus_chat.parallel_processing) if global_config.focus_chat.parallel_processing: # 并行执行回忆和处理器阶段 with Timer("并行回忆和处理", cycle_timers): memory_task = asyncio.create_task(self.memory_activator.activate_memory(observations)) processor_task = asyncio.create_task(self._process_processors(observations, [])) # 等待两个任务完成 running_memorys, (all_plan_info, processor_time_costs) = await asyncio.gather( memory_task, processor_task ) else: # 串行执行 with Timer("回忆", cycle_timers): running_memorys = await self.memory_activator.activate_memory(observations) with Timer("执行 信息处理器", cycle_timers): all_plan_info, processor_time_costs = await self._process_processors(observations, running_memorys) loop_processor_info = { "all_plan_info": all_plan_info, "processor_time_costs": processor_time_costs, } with Timer("规划器", cycle_timers): plan_result = await self.action_planner.plan(all_plan_info, running_memorys) loop_plan_info = { "action_result": plan_result.get("action_result", {}), "current_mind": plan_result.get("current_mind", ""), "observed_messages": plan_result.get("observed_messages", ""), } with Timer("执行动作", cycle_timers): action_type, action_data, reasoning = ( plan_result.get("action_result", {}).get("action_type", "error"), plan_result.get("action_result", {}).get("action_data", {}), plan_result.get("action_result", {}).get("reasoning", "未提供理由"), ) if action_type == "reply": action_str = "回复" elif action_type == "no_reply": action_str = "不回复" else: action_str = action_type logger.debug(f"{self.log_prefix} 麦麦想要：'{action_str}', 原因'{reasoning}'") success, reply_text, command = await self._handle_action( action_type, reasoning, action_data, cycle_timers, thinking_id ) loop_action_info = { "action_taken": success, "reply_text": reply_text, "command": command, "taken_time": time.time(), } loop_info = { "loop_observation_info": loop_observation_info, "loop_processor_info": loop_processor_info, "loop_plan_info": loop_plan_info, "loop_action_info": loop_action_info, } return loop_info except Exception as e: logger.error(f"{self.log_prefix} FOCUS聊天处理失败: {e}") logger.error(traceback.format_exc()) return { "loop_observation_info": {}, "loop_processor_info": {}, "loop_plan_info": {}, "loop_action_info": {"action_taken": False, "reply_text": "", "command": ""}, } async def _handle_action( self, action: str, reasoning: str, action_data: dict, cycle_timers: dict, thinking_id: str, ) -> 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, observations=self.all_observations, expressor=self.expressor, replyer=self.replyer, chat_stream=self.chat_stream, log_prefix=self.log_prefix, shutting_down=self._shutting_down, ) 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}, 原因: {reasoning}") return False, "", "" # 处理动作并获取结果 result = await action_handler.handle_action() if len(result) == 3: success, reply_text, command = result else: success, reply_text = result command = "" logger.debug( f"{self.log_prefix} 麦麦执行了'{action}', 原因'{reasoning}'，返回结果'{success}', '{reply_text}', '{command}'" ) 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 shutdown(self): """优雅关闭HeartFChatting实例，取消活动循环任务""" logger.info(f"{self.log_prefix} 正在关闭HeartFChatting...") self._shutting_down = True # <-- 在开始关闭时设置标志位 # 取消循环任务 if self._loop_task and not self._loop_task.done(): logger.info(f"{self.log_prefix} 正在取消HeartFChatting循环任务") self._loop_task.cancel() try: await asyncio.wait_for(self._loop_task, timeout=1.0) logger.info(f"{self.log_prefix} HeartFChatting循环任务已取消") except (asyncio.CancelledError, asyncio.TimeoutError): pass except Exception as e: logger.error(f"{self.log_prefix} 取消循环任务出错: {e}") else: logger.info(f"{self.log_prefix} 没有活动的HeartFChatting循环任务") # 清理状态 self._loop_active = False self._loop_task = None if self._processing_lock.locked(): self._processing_lock.release() logger.warning(f"{self.log_prefix} 已释放处理锁") logger.info(f"{self.log_prefix} HeartFChatting关闭完成") def get_cycle_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]: """获取循环历史记录 参数: last_n: 获取最近n个循环的信息，如果为None则获取所有历史记录 返回: List[Dict[str, Any]]: 循环历史记录列表 """ history = list(self._cycle_history) if last_n is not None: history = history[-last_n:] return [cycle.to_dict() for cycle in history]