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 get_chat_manager from rich.traceback import install from src.chat.utils.prompt_builder import global_prompt_manager from src.common.logger import get_logger from src.chat.utils.timer_calculator import Timer from src.chat.focus_chat.focus_loop_info import FocusLoopInfo from src.chat.planner_actions.planner_focus import ActionPlanner from src.chat.planner_actions.action_modifier import ActionModifier from src.chat.planner_actions.action_manager import ActionManager from src.config.config import global_config from src.chat.focus_chat.hfc_performance_logger import HFCPerformanceLogger from src.chat.focus_chat.hfc_version_manager import get_hfc_version from src.person_info.relationship_builder_manager import relationship_builder_manager from src.chat.focus_chat.hfc_utils import CycleDetail 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, 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) 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 = get_chat_manager().get_stream(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) # 新增：消息计数器和疲惫阈值 self._message_count = 0 # 发送的消息计数 # 基于exit_focus_threshold动态计算疲惫阈值 # 基础值30条，通过exit_focus_threshold调节：threshold越小，越容易疲惫 self._message_threshold = max(10, int(30 * global_config.chat.exit_focus_threshold)) self._fatigue_triggered = False # 是否已触发疲惫退出 self.loop_info: FocusLoopInfo = FocusLoopInfo(observe_id=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) 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 self.reply_timeout_count = 0 self.plan_timeout_count = 0 # 初始化性能记录器 # 如果没有指定版本号，则使用全局版本管理器的版本号 actual_version = get_hfc_version() self.performance_logger = HFCPerformanceLogger(chat_id, actual_version) logger.info( f"{self.log_prefix} HeartFChatting 初始化完成，消息疲惫阈值: {self._message_threshold}条（基于exit_focus_threshold={global_config.chat.exit_focus_threshold}计算，仅在auto模式下生效）" ) 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._loop_active: logger.debug(f"{self.log_prefix} HeartFChatting 已激活，无需重复启动") return try: # 重置消息计数器，开始新的focus会话 self.reset_message_count() # 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=5.0) except Exception as e: logger.warning(f"{self.log_prefix} 等待旧任务取消时出错: {e}") self._loop_task = None # 清理旧任务引用 logger.debug(f"{self.log_prefix} 创建新的 HeartFChatting 主循环任务") self._loop_task = asyncio.create_task(self._run_focus_chat()) self._loop_task.add_done_callback(self._handle_loop_completion) logger.debug(f"{self.log_prefix} HeartFChatting 启动完成") except Exception as e: # 启动失败时重置状态 self._loop_active = 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: 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") # 检查关闭标志 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 = {} # 执行规划和处理阶段 try: async with self._get_cycle_context(): thinking_id = "tid" + str(round(time.time(), 2)) self._current_cycle_detail.set_thinking_id(thinking_id) # 使用异步上下文管理器处理消息 try: async with global_prompt_manager.async_message_scope( self.chat_stream.context.get_template_name() ): # 在上下文内部检查关闭状态 if self._shutting_down: logger.info(f"{self.log_prefix} 在处理上下文中检测到关闭信号，退出") break 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") # 如果是私聊，则不停止，而是重置疲劳度并继续 if not self.chat_stream.group_info: logger.info(f"{self.log_prefix} 私聊模式下收到停止请求，不退出。") continue # 继续下一次循环，而不是退出 # 如果是群聊，则执行原来的停止逻辑 # 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 except asyncio.CancelledError: logger.info(f"{self.log_prefix} 处理上下文时任务被取消") break except Exception as e: logger.error(f"{self.log_prefix} 处理上下文时出错: {e}") # 为当前循环设置错误状态，防止后续重复报错 error_loop_info = { "loop_plan_info": { "action_result": { "action_type": "error", "action_data": {}, }, }, "loop_action_info": { "action_taken": False, "reply_text": "", "command": "", "taken_time": time.time(), }, } self._current_cycle_detail.set_loop_info(error_loop_info) self._current_cycle_detail.complete_cycle() # 上下文处理失败，跳过当前循环 await asyncio.sleep(1) continue self._current_cycle_detail.set_loop_info(loop_info) self.loop_info.add_loop_info(self._current_cycle_detail) self._current_cycle_detail.timers = cycle_timers # 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}") 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"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 "") ) # 记录性能数据 try: action_result = self._current_cycle_detail.loop_plan_info.get("action_result", {}) cycle_performance_data = { "cycle_id": self._current_cycle_detail.cycle_id, "action_type": action_result.get("action_type", "unknown"), "total_time": self._current_cycle_detail.end_time - self._current_cycle_detail.start_time, "step_times": cycle_timers.copy(), "reasoning": action_result.get("reasoning", ""), "success": self._current_cycle_detail.loop_action_info.get("action_taken", False), } self.performance_logger.record_cycle(cycle_performance_data) except Exception as perf_e: logger.warning(f"{self.log_prefix} 记录性能数据失败: {perf_e}") await asyncio.sleep(global_config.focus_chat.think_interval) except asyncio.CancelledError: logger.info(f"{self.log_prefix} 循环处理时任务被取消") break except Exception as e: logger.error(f"{self.log_prefix} 循环处理时出错: {e}") logger.error(traceback.format_exc()) # 如果_current_cycle_detail存在但未完成，为其设置错误状态 if self._current_cycle_detail and not hasattr(self._current_cycle_detail, "end_time"): error_loop_info = { "loop_plan_info": { "action_result": { "action_type": "error", "action_data": {}, "reasoning": f"循环处理失败: {e}", }, }, "loop_action_info": { "action_taken": False, "reply_text": "", "command": "", "taken_time": time.time(), }, } try: self._current_cycle_detail.set_loop_info(error_loop_info) self._current_cycle_detail.complete_cycle() except Exception as inner_e: logger.error(f"{self.log_prefix} 设置错误状态时出错: {inner_e}") await asyncio.sleep(1) # 出错后等待一秒再继续 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 _observe_process_plan_action_loop(self, cycle_timers: dict, thinking_id: str) -> dict: try: loop_start_time = time.time() await self.loop_info.observe() await self.relationship_builder.build_relation() # 顺序执行调整动作和处理器阶段 # 第一步：动作修改 with Timer("动作修改", cycle_timers): try: # 调用完整的动作修改流程 await self.action_modifier.modify_actions( loop_info=self.loop_info, mode="focus", ) except Exception as e: logger.error(f"{self.log_prefix} 动作修改失败: {e}") # 继续执行，不中断流程 with Timer("规划器", cycle_timers): plan_result = await self.action_planner.plan() loop_plan_info = { "action_result": plan_result.get("action_result", {}), } 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"), ) action_data["loop_start_time"] = loop_start_time 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}'，理由是：{reasoning}") # 动作执行计时 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 ) loop_action_info = { "action_taken": success, "reply_text": reply_text, "command": command, "taken_time": time.time(), } loop_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_plan_info": { "action_result": {"action_type": "error", "action_data": {}, "reasoning": f"处理失败: {e}"}, }, "loop_action_info": {"action_taken": False, "reply_text": "", "command": "", "taken_time": time.time()}, } 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, 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}") 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 = "" # 检查action_data中是否有系统命令，优先使用系统命令 if "_system_command" in action_data: command = action_data["_system_command"] logger.debug(f"{self.log_prefix} 从action_data中获取系统命令: {command}") # 新增：消息计数和疲惫检查 if action == "reply" and success: self._message_count += 1 current_threshold = self._get_current_fatigue_threshold() logger.info( f"{self.log_prefix} 已发送第 {self._message_count} 条消息（动态阈值: {current_threshold}, exit_focus_threshold: {global_config.chat.exit_focus_threshold}）" ) # 检查是否达到疲惫阈值（只有在auto模式下才会自动退出） if ( global_config.chat.chat_mode == "auto" and self._message_count >= current_threshold and not self._fatigue_triggered ): self._fatigue_triggered = True logger.info( f"{self.log_prefix} [auto模式] 已发送 {self._message_count} 条消息，达到疲惫阈值 {current_threshold}，麦麦感到疲惫了，准备退出专注聊天模式" ) # 设置系统命令，在下次循环检查时触发退出 command = "stop_focus_chat" elif self._message_count >= current_threshold and global_config.chat.chat_mode != "auto": logger.info( f"{self.log_prefix} [非auto模式] 已发送 {self._message_count} 条消息，达到疲惫阈值 {current_threshold}，但非auto模式不会自动退出" ) else: 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, "", "" def _get_current_fatigue_threshold(self) -> int: """动态获取当前的疲惫阈值，基于exit_focus_threshold配置 Returns: int: 当前的疲惫阈值 """ return max(10, int(30 / global_config.chat.exit_focus_threshold)) def get_message_count_info(self) -> dict: """获取消息计数信息 Returns: dict: 包含消息计数信息的字典 """ current_threshold = self._get_current_fatigue_threshold() return { "current_count": self._message_count, "threshold": current_threshold, "fatigue_triggered": self._fatigue_triggered, "remaining": max(0, current_threshold - self._message_count), } def reset_message_count(self): """重置消息计数器（用于重新启动focus模式时）""" self._message_count = 0 self._fatigue_triggered = False logger.info(f"{self.log_prefix} 消息计数器已重置") 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 # 记录最终的消息统计 if self._message_count > 0: logger.info(f"{self.log_prefix} 本次focus会话共发送了 {self._message_count} 条消息") if self._fatigue_triggered: logger.info(f"{self.log_prefix} 因疲惫而退出focus模式") # 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") # 完成性能统计 try: self.performance_logger.finalize_session() logger.info(f"{self.log_prefix} 性能统计已完成") except Exception as e: logger.warning(f"{self.log_prefix} 完成性能统计时出错: {e}") # 重置消息计数器，为下次启动做准备 self.reset_message_count() 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]