import asyncio import time import traceback import random from typing import List, Optional, Dict, Any, Tuple from rich.traceback import install from collections import deque 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.mais4u.mai_think import mai_thinking_manager from src.mais4u.constant_s4u import ENABLE_S4U import math # no_reply逻辑已集成到heartFC_chat.py中，不再需要导入 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.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.reply_timeout_count = 0 self.plan_timeout_count = 0 self.last_read_time = time.time() - 1 self.focus_energy = 1 self.no_reply_consecutive = 0 # 最近三次no_reply的新消息兴趣度记录 self.recent_interest_records: deque = deque(maxlen=3) 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) 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 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}") # 获取动作类型，兼容新旧格式 action_type = "未知动作" if hasattr(self, '_current_cycle_detail') and self._current_cycle_detail: loop_plan_info = self._current_cycle_detail.loop_plan_info if isinstance(loop_plan_info, dict): action_result = loop_plan_info.get('action_result', {}) if isinstance(action_result, dict): # 旧格式：action_result是字典 action_type = action_result.get('action_type', '未知动作') elif isinstance(action_result, list) and action_result: # 新格式：action_result是actions列表 action_type = action_result[0].get('action_type', '未知动作') elif isinstance(loop_plan_info, list) and loop_plan_info: # 直接是actions列表的情况 action_type = loop_plan_info[0].get('action_type', '未知动作') 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: {action_type}" + (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "") ) def _determine_form_type(self) -> str: """判断使用哪种形式的no_reply""" # 如果连续no_reply次数少于3次，使用waiting形式 if self.no_reply_consecutive <= 3: self.focus_energy = 1 else: # 计算最近三次记录的兴趣度总和 total_recent_interest = sum(self.recent_interest_records) # 计算调整后的阈值 adjusted_threshold = 3 / global_config.chat.get_current_talk_frequency(self.stream_id) logger.info(f"{self.log_prefix} 最近三次兴趣度总和: {total_recent_interest:.2f}, 调整后阈值: {adjusted_threshold:.2f}") # 如果兴趣度总和小于阈值，进入breaking形式 if total_recent_interest < adjusted_threshold: logger.info(f"{self.log_prefix} 兴趣度不足，进入breaking形式") self.focus_energy = random.randint(3, 6) else: logger.info(f"{self.log_prefix} 兴趣度充足") self.focus_energy = 1 async def _should_process_messages(self, new_message: List[Dict[str, Any]]) -> tuple[bool,float]: """ 判断是否应该处理消息 Args: new_message: 新消息列表 mode: 当前聊天模式 Returns: bool: 是否应该处理消息 """ new_message_count = len(new_message) modified_exit_count_threshold = self.focus_energy / global_config.chat.focus_value modified_exit_interest_threshold = 3 / global_config.chat.focus_value total_interest = 0.0 for msg_dict in new_message: interest_value = msg_dict.get("interest_value", 0.0) if msg_dict.get("processed_plain_text", ""): total_interest += interest_value if new_message_count >= modified_exit_count_threshold: self.recent_interest_records.append(total_interest) logger.info( f"{self.log_prefix} 累计消息数量达到{new_message_count}条(>{modified_exit_count_threshold:.1f})，结束等待" ) # logger.info(self.last_read_time) # logger.info(new_message) return True,total_interest/new_message_count # 检查累计兴趣值 if new_message_count > 0: # 只在兴趣值变化时输出log if not hasattr(self, "_last_accumulated_interest") or total_interest != self._last_accumulated_interest: logger.info(f"{self.log_prefix} breaking形式当前累计兴趣值: {total_interest:.2f}, 专注度: {global_config.chat.focus_value:.1f}") self._last_accumulated_interest = total_interest if total_interest >= modified_exit_interest_threshold: # 记录兴趣度到列表 self.recent_interest_records.append(total_interest) logger.info( f"{self.log_prefix} 累计兴趣值达到{total_interest:.2f}(>{modified_exit_interest_threshold:.1f})，结束等待" ) return True,total_interest/new_message_count # 每10秒输出一次等待状态 if int(time.time() - self.last_read_time) > 0 and int(time.time() - self.last_read_time) % 10 == 0: logger.info( f"{self.log_prefix} 已等待{time.time() - self.last_read_time:.0f}秒，累计{new_message_count}条消息，累计兴趣{total_interest:.1f}，继续等待..." ) await asyncio.sleep(0.5) return False,0.0 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, ) # 统一的消息处理逻辑 should_process,interest_value = await self._should_process_messages(recent_messages_dict) if should_process: self.last_read_time = time.time() await self._observe(interest_value = interest_value) else: # Normal模式：消息数量不足，等待 await asyncio.sleep(0.5) return True return True async def _send_and_store_reply( self, response_set, action_message, cycle_timers: Dict[str, float], thinking_id, actions, ) -> Tuple[Dict[str, Any], str, Dict[str, float]]: with Timer("回复发送", cycle_timers): reply_text = await self._send_response(response_set, action_message) # 存储reply action信息 person_info_manager = get_person_info_manager() # 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.get("chat_info_platform") if platform is None: platform = getattr(self.chat_stream, "platform", "unknown") person_id = person_info_manager.get_person_id( platform, action_message.get("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}, 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) -> bool: action_type = "no_action" reply_text = "" # 初始化reply_text变量，避免UnboundLocalError # 使用sigmoid函数将interest_value转换为概率 # 当interest_value为0时，概率接近0（使用Focus模式） # 当interest_value很高时，概率接近1（使用Normal模式） def calculate_normal_mode_probability(interest_val: float) -> float: # 使用sigmoid函数，调整参数使概率分布更合理 # 当interest_value = 0时，概率约为0.1 # 当interest_value = 1时，概率约为0.5 # 当interest_value = 2时，概率约为0.8 # 当interest_value = 3时，概率约为0.95 k = 2.0 # 控制曲线陡峭程度 x0 = 1.0 # 控制曲线中心点 return 1.0 / (1.0 + math.exp(-k * (interest_val - x0))) normal_mode_probability = calculate_normal_mode_probability(interest_value) / global_config.chat.get_current_talk_frequency(self.stream_id) # 根据概率决定使用哪种模式 if random.random() < normal_mode_probability: mode = ChatMode.NORMAL logger.info(f"{self.log_prefix} 基于兴趣值 {interest_value:.2f}，概率 {normal_mode_probability:.2f}，选择Normal planner模式") else: mode = ChatMode.FOCUS logger.info(f"{self.log_prefix} 基于兴趣值 {interest_value:.2f}，概率 {normal_mode_probability:.2f}，选择Focus planner模式") # 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") if ENABLE_S4U: await send_typing() async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()): 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}") # 执行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): actions, _= await self.action_planner.plan( mode=mode, loop_start_time=self.last_read_time, available_actions=available_actions, ) # 3. 并行执行所有动作 async def execute_action(action_info): """执行单个动作的通用函数""" try: if action_info["action_type"] == "no_reply": # 直接处理no_reply逻辑，不再通过动作系统 reason = action_info.get("reasoning", "选择不回复") logger.info(f"{self.log_prefix} 选择不回复，原因: {reason}") # 存储no_reply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="no_reply", ) return { "action_type": "no_reply", "success": True, "reply_text": "", "command": "" } elif action_info["action_type"] != "reply": # 执行普通动作 with Timer("动作执行", cycle_timers): success, reply_text, command = await self._handle_action( action_info["action_type"], action_info["reasoning"], action_info["action_data"], cycle_timers, thinking_id, action_info["action_message"] ) return { "action_type": action_info["action_type"], "success": success, "reply_text": reply_text, "command": command } else: try: success, response_set, _ = await generator_api.generate_reply( chat_stream=self.chat_stream, reply_message = action_info["action_message"], available_actions=available_actions, reply_reason=action_info.get("reasoning", ""), enable_tool=global_config.tool.enable_tool, request_type="chat.replyer", from_plugin=False, ) if not success or not response_set: logger.info(f"对 {action_info['action_message'].get('processed_plain_text')} 的回复生成失败") 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 } loop_info, reply_text, cycle_timers_reply = await self._send_and_store_reply( response_set, action_info["action_message"], cycle_timers, thinking_id, actions, ) return { "action_type": "reply", "success": True, "reply_text": reply_text, "loop_info": loop_info } 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_info["action_type"], "success": False, "reply_text": "", "loop_info": None, "error": str(e) } action_tasks = [asyncio.create_task(execute_action(action)) for action in actions] # 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 = "" action_command = "" for i, result in enumerate(results): if isinstance(result, BaseException): logger.error(f"{self.log_prefix} 动作执行异常: {result}") continue action_info = actions[i] if result["action_type"] != "reply": action_success = result["success"] action_reply_text = result["reply_text"] action_command = result.get("command", "") 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") # 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": actions, }, "loop_action_info": { "action_taken": action_success, "reply_text": action_reply_text, "command": action_command, "taken_time": time.time(), }, } reply_text = action_reply_text 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) # await self.willing_manager.after_generate_reply_handle(message_data.get("message_id", "")) action_type = actions[0]["action_type"] if actions else "no_action" # 管理no_reply计数器：当执行了非no_reply动作时，重置计数器 if action_type != "no_reply": # no_reply逻辑已集成到heartFC_chat.py中，直接重置计数器 self.recent_interest_records.clear() self.no_reply_consecutive = 0 logger.debug(f"{self.log_prefix} 执行了{action_type}动作，重置no_reply计数器") return True if action_type == "no_reply": self.no_reply_consecutive += 1 self._determine_form_type() 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 _send_response(self, reply_set, message_data) -> 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_seg in reply_set: data = reply_seg[1] if not first_replied: await send_api.text_to_stream( text=data, stream_id=self.chat_stream.stream_id, reply_to_message = message_data, set_reply=need_reply, typing=False, ) first_replied = True else: await send_api.text_to_stream( text=data, stream_id=self.chat_stream.stream_id, reply_to_message = message_data, set_reply=False, typing=True, ) reply_text += data return reply_text