import asyncio import contextlib import json # <--- 确保导入 json import random # <--- 添加导入 import time import traceback from collections import deque from typing import List, Optional, Dict, Any, Deque, Callable, Coroutine 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.common.logger_manager import get_logger from src.chat.models.utils_model import LLMRequest from src.config.config import global_config from src.chat.utils.timer_calculator import Timer from src.chat.heart_flow.observation.observation import Observation from src.chat.focus_chat.heartflow_prompt_builder import prompt_builder from src.chat.focus_chat.heartFC_Cycleinfo import CycleDetail from src.chat.heart_flow.observation.chatting_observation import ChattingObservation from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info from src.chat.focus_chat.info.info_base import InfoBase from src.chat.focus_chat.info.obs_info import ObsInfo from src.chat.focus_chat.info.cycle_info import CycleInfo from src.chat.focus_chat.info.mind_info import MindInfo from src.chat.focus_chat.info.structured_info import StructuredInfo 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.heart_flow.observation.memory_observation import MemoryObservation from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation from src.chat.heart_flow.observation.working_observation import WorkingObservation 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.hfc_utils import create_empty_anchor_message, parse_thinking_id_to_timestamp from src.chat.focus_chat.memory_activator import MemoryActivator install(extra_lines=3) WAITING_TIME_THRESHOLD = 300 # 等待新消息时间阈值，单位秒 EMOJI_SEND_PRO = 0.3 # 设置一个概率，比如 30% 才真的发 CONSECUTIVE_NO_REPLY_THRESHOLD = 3 # 连续不回复的阈值 logger = get_logger("hfc") # Logger Name Changed # 默认动作定义 DEFAULT_ACTIONS = {"no_reply": "不操作，继续浏览", "reply": "表达想法，可以只包含文本、表情或两者都有"} class ActionManager: """动作管理器：控制每次决策可以使用的动作""" def __init__(self): # 初始化为新的默认动作集 self._available_actions: Dict[str, str] = DEFAULT_ACTIONS.copy() self._original_actions_backup: Optional[Dict[str, str]] = None def get_available_actions(self) -> Dict[str, str]: """获取当前可用的动作集""" return self._available_actions.copy() # 返回副本以防外部修改 def add_action(self, action_name: str, description: str) -> bool: """ 添加新的动作 参数: action_name: 动作名称 description: 动作描述 返回: bool: 是否添加成功 """ if action_name in self._available_actions: return False self._available_actions[action_name] = description return True def remove_action(self, action_name: str) -> bool: """ 移除指定动作 参数: action_name: 动作名称 返回: bool: 是否移除成功 """ if action_name not in self._available_actions: return False del self._available_actions[action_name] return True def temporarily_remove_actions(self, actions_to_remove: List[str]): """ 临时移除指定的动作，备份原始动作集。 如果已经有备份，则不重复备份。 """ if self._original_actions_backup is None: self._original_actions_backup = self._available_actions.copy() actions_actually_removed = [] for action_name in actions_to_remove: if action_name in self._available_actions: del self._available_actions[action_name] actions_actually_removed.append(action_name) # logger.debug(f"临时移除了动作: {actions_actually_removed}") # 可选日志 def restore_actions(self): """ 恢复之前备份的原始动作集。 """ if self._original_actions_backup is not None: self._available_actions = self._original_actions_backup.copy() self._original_actions_backup = None # logger.debug("恢复了原始动作集") # 可选日志 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Plan-Replier-Sender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_consecutive_no_reply_callback: Callable[[], Coroutine[None, 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_consecutive_no_reply_callback: 连续不回复达到阈值时调用的异步回调函数 """ # 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.observations: List[Observation] = observations # 关联的观察列表，用于监控聊天流状态 self.on_consecutive_no_reply_callback = on_consecutive_no_reply_callback self.chatting_info_processor = ChattingInfoProcessor() self.mind_processor = MindProcessor(subheartflow_id=self.stream_id) self.memory_observation = MemoryObservation(observe_id=self.stream_id) self.hfcloop_observation = HFCloopObservation(observe_id=self.stream_id) self.tool_processor = ToolProcessor(subheartflow_id=self.stream_id) self.working_observation = WorkingObservation(observe_id=self.stream_id) self.memory_activator = MemoryActivator() # 日志前缀 self.log_prefix: str = str(chat_id) # Initial default, will be updated # --- Initialize attributes (defaults) --- self.is_group_chat: bool = False self.chat_target_info: Optional[dict] = None # --- End Initialization --- self.expressor = DefaultExpressor(chat_id=self.stream_id) # 动作管理器 self.action_manager = ActionManager() # 初始化状态控制 self._initialized = False self._processing_lock = asyncio.Lock() # LLM规划器配置 self.planner_llm = LLMRequest( model=global_config.llm_plan, max_tokens=1000, request_type="action_planning", # 用于动作规划 ) # 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: Optional[CycleDetail] = None self.total_no_reply_count: int = 0 # <--- 新增：连续不回复计数器 self._shutting_down: bool = False # <--- 新增：关闭标志位 self.total_waiting_time: float = 0.0 # <--- 新增：累计等待时间 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: self.is_group_chat, self.chat_target_info = await get_chat_type_and_target_info(self.stream_id) await self.expressor.initialize() self.chat_stream = await asyncio.to_thread(chat_manager.get_stream, self.stream_id) self.expressor.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 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} 开始认真水群(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 logger.debug(f"{self.log_prefix} 启动认真水群(HFC)主循环...") # 创建新的循环任务 self._loop_task = asyncio.create_task(self._hfc_loop()) # 添加完成回调 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: 麦麦脱离了聊天(异常): {exception}") logger.error(traceback.format_exc()) # Log full traceback for exceptions else: # Loop completing normally now means it was cancelled/shutdown externally logger.info(f"{self.log_prefix} HeartFChatting: 麦麦脱离了聊天 (外部停止)") except asyncio.CancelledError: logger.info(f"{self.log_prefix} HeartFChatting: 麦麦脱离了聊天(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 _hfc_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 True: # 主循环 logger.debug(f"{self.log_prefix} 开始第{self._cycle_counter}次循环") # --- 在循环开始处检查关闭标志 --- if self._shutting_down: logger.info(f"{self.log_prefix} 检测到关闭标志，退出 HFC 循环。") break # -------------------------------- # 创建新的循环信息 self._cycle_counter += 1 self._current_cycle = CycleDetail(self._cycle_counter) # 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() as acquired_lock: if not acquired_lock: # 如果未能获取锁（理论上不太可能，除非 shutdown 过程中释放了但又被抢了？） # 或者也可以在这里再次检查 self._shutting_down if self._shutting_down: break # 再次检查，确保退出 logger.warning(f"{self.log_prefix} 未能获取循环处理锁，跳过本次循环。") await asyncio.sleep(0.1) # 短暂等待避免空转 continue # thinking_id 是思考过程的ID，用于标记每一轮思考 thinking_id = "tid" + str(round(time.time(), 2)) # 主循环：思考->决策->执行 action_taken = await self._think_plan_execute_loop(cycle_timers, thinking_id) # 更新循环信息 self._current_cycle.set_thinking_id(thinking_id) self._current_cycle.timers = cycle_timers # 防止循环过快消耗资源 await _handle_cycle_delay(action_taken, loop_cycle_start_time, self.log_prefix) # 完成当前循环并保存历史 self._current_cycle.complete_cycle() self._cycle_history.append(self._current_cycle) # 保存CycleInfo到文件 try: filepath = CycleDetail.save_to_file(self._current_cycle, self.stream_id) logger.info(f"{self.log_prefix} 已保存循环信息到文件: {filepath}") except Exception as e: logger.error(f"{self.log_prefix} 保存循环信息到文件时出错: {e}") # 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.debug( f"{self.log_prefix} 第 #{self._current_cycle.cycle_id}次思考完成," f"耗时: {self._current_cycle.end_time - self._current_cycle.start_time:.2f}秒, " f"动作: {self._current_cycle.action_type}" + (f"\n计时器详情: {'; '.join(timer_strings)}" if timer_strings else "") ) 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} HeartFChatting: 麦麦的认真水群(HFC)循环意外被取消") else: logger.info(f"{self.log_prefix} HeartFChatting: 麦麦的认真水群(HFC)循环已取消 (正常关闭)") except Exception as e: logger.error(f"{self.log_prefix} HeartFChatting: 意外错误: {e}") logger.error(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 _think_plan_execute_loop(self, cycle_timers: dict, thinking_id: str) -> tuple[bool, str]: try: with Timer("观察", cycle_timers): await self.observations[0].observe() await self.memory_observation.observe() await self.working_observation.observe() await self.hfcloop_observation.observe() observations: List[Observation] = [] observations.append(self.observations[0]) observations.append(self.memory_observation) observations.append(self.working_observation) observations.append(self.hfcloop_observation) for observation in observations: logger.debug(f"{self.log_prefix} 观察信息: {observation}") with Timer("回忆", cycle_timers): running_memorys = await self.memory_activator.activate_memory(observations) # 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") # 并行执行两个任务：思考和工具执行 with Timer("执行 信息处理器", cycle_timers): # 1. 子思维思考 - 不执行工具调用 think_task = asyncio.create_task( self.mind_processor.process_info(observations=observations, running_memorys=running_memorys) ) logger.debug(f"{self.log_prefix} 启动子思维思考任务") # 2. 工具执行器 - 专门处理工具调用 tool_task = asyncio.create_task( self.tool_processor.process_info(observations=observations, running_memorys=running_memorys) ) logger.debug(f"{self.log_prefix} 启动工具执行任务") # 3. 聊天信息处理器 chatting_info_task = asyncio.create_task( self.chatting_info_processor.process_info( observations=observations, running_memorys=running_memorys ) ) logger.debug(f"{self.log_prefix} 启动聊天信息处理器任务") # 创建任务完成状态追踪 tasks = {"思考任务": think_task, "工具任务": tool_task, "聊天信息处理任务": chatting_info_task} pending = set(tasks.values()) # 等待所有任务完成，同时追踪每个任务的完成情况 results: dict[str, list[InfoBase]] = {} while pending: # 等待任务完成 done, pending = await asyncio.wait(pending, return_when=asyncio.FIRST_COMPLETED, timeout=1.0) # 记录完成的任务 for task in done: for name, t in tasks.items(): if task == t: task_end_time = time.time() task_duration = task_end_time - parallel_start_time logger.info(f"{self.log_prefix} {name}已完成，耗时: {task_duration:.2f}秒") results[name] = task.result() break # 如果仍有未完成任务，记录进行中状态 if pending: current_time = time.time() elapsed = current_time - parallel_start_time pending_names = [name for name, t in tasks.items() if t in pending] logger.info( f"{self.log_prefix} 并行处理已进行{elapsed:.2f}秒，待完成任务: {', '.join(pending_names)}" ) # 所有任务完成，从结果中提取数据 mind_processed_infos = results.get("思考任务", []) tool_processed_infos = results.get("工具任务", []) chatting_info_processed_infos = results.get("聊天信息处理任务", []) # 记录总耗时 parallel_end_time = time.time() total_duration = parallel_end_time - parallel_start_time logger.info(f"{self.log_prefix} 思考和工具并行任务全部完成，总耗时: {total_duration:.2f}秒") all_plan_info = mind_processed_infos + tool_processed_infos + chatting_info_processed_infos logger.debug(f"{self.log_prefix} 所有信息处理器处理后的信息: {all_plan_info}") # 串行执行规划器 - 使用刚获取的思考结果 logger.debug(f"{self.log_prefix} 开始 规划器") with Timer("规划器", cycle_timers): planner_result = await self._planner(all_plan_info, cycle_timers) action = planner_result.get("action", "error") action_data = planner_result.get("action_data", {}) # 新增获取动作数据 reasoning = planner_result.get("reasoning", "未提供理由") logger.debug(f"{self.log_prefix} 动作和动作信息: {action}, {action_data}, {reasoning}") # 更新循环信息 self._current_cycle.set_action_info( action_type=action, action_data=action_data, reasoning=reasoning, action_taken=True, ) # 处理LLM错误 if planner_result.get("llm_error"): logger.error(f"{self.log_prefix} LLM失败: {reasoning}") return False, "" # 在此处添加日志记录 if action == "reply": action_str = "回复" elif action == "no_reply": action_str = "不回复" else: action_str = "位置动作" logger.info(f"{self.log_prefix} 麦麦决定'{action_str}', 原因'{reasoning}'") self.hfcloop_observation.add_loop_info(self._current_cycle) return await self._handle_action(action, reasoning, action_data, cycle_timers, thinking_id) except Exception as e: logger.error(f"{self.log_prefix} 并行+串行处理失败: {e}") logger.error(traceback.format_exc()) return False, "" async def _handle_action( self, action: str, reasoning: str, action_data: dict, cycle_timers: dict, thinking_id: str, ) -> tuple[bool, str]: """ 处理规划动作 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 planner_start_db_time: 规划开始时间 返回: tuple[bool, str]: (是否执行了动作, 思考消息ID) """ action_handlers = { "reply": self._handle_reply, "no_reply": self._handle_no_reply, } handler = action_handlers.get(action) if not handler: logger.warning(f"{self.log_prefix} 未知动作: {action}, 原因: {reasoning}") return False, "" try: if action == "reply": return await handler(reasoning, action_data, cycle_timers, thinking_id) else: # no_reply return await handler(reasoning, cycle_timers, thinking_id) except Exception as e: logger.error(f"{self.log_prefix} 处理{action}时出错: {e}") traceback.print_exc() return False, "" async def _handle_no_reply(self, reasoning: str, cycle_timers: dict, thinking_id: str) -> bool: """ 处理不回复的情况 工作流程： 1. 等待新消息、超时或关闭信号 2. 根据等待结果更新连续不回复计数 3. 如果达到阈值，触发回调 参数: reasoning: 不回复的原因 planner_start_db_time: 规划开始时间 cycle_timers: 计时器字典 返回: bool: 是否成功处理 """ logger.info(f"{self.log_prefix} 决定不回复: {reasoning}") observation = self.observations[0] if self.observations else None try: with Timer("等待新消息", cycle_timers): # 等待新消息、超时或关闭信号，并获取结果 await self._wait_for_new_message(observation, thinking_id, self.log_prefix) # 从计时器获取实际等待时间 current_waiting = cycle_timers.get("等待新消息", 0.0) if not self._shutting_down: self.total_no_reply_count += 1 self.total_waiting_time += current_waiting # 累加等待时间 logger.debug( f"{self.log_prefix} 连续不回复计数增加: {self.total_no_reply_count}/{CONSECUTIVE_NO_REPLY_THRESHOLD}, " f"本次等待: {current_waiting:.2f}秒, 累计等待: {self.total_waiting_time:.2f}秒" ) # 检查是否同时达到次数和时间阈值 time_threshold = 0.66 * WAITING_TIME_THRESHOLD * CONSECUTIVE_NO_REPLY_THRESHOLD if ( self.total_no_reply_count >= CONSECUTIVE_NO_REPLY_THRESHOLD and self.total_waiting_time >= time_threshold ): logger.info( f"{self.log_prefix} 连续不回复达到阈值 ({self.total_no_reply_count}次) " f"且累计等待时间达到 {self.total_waiting_time:.2f}秒 (阈值 {time_threshold}秒)，" f"调用回调请求状态转换" ) # 调用回调。注意：这里不重置计数器和时间，依赖回调函数成功改变状态来隐式重置上下文。 await self.on_consecutive_no_reply_callback() elif self.total_no_reply_count >= CONSECUTIVE_NO_REPLY_THRESHOLD: # 仅次数达到阈值，但时间未达到 logger.debug( f"{self.log_prefix} 连续不回复次数达到阈值 ({self.total_no_reply_count}次) " f"但累计等待时间 {self.total_waiting_time:.2f}秒 未达到时间阈值 ({time_threshold}秒)，暂不调用回调" ) # else: 次数和时间都未达到阈值，不做处理 return True, thinking_id except asyncio.CancelledError: logger.info(f"{self.log_prefix} 处理 'no_reply' 时等待被中断 (CancelledError)") raise except Exception as e: # 捕获调用管理器或其他地方可能发生的错误 logger.error(f"{self.log_prefix} 处理 'no_reply' 时发生错误: {e}") logger.error(traceback.format_exc()) return False, thinking_id async def _wait_for_new_message(self, observation: ChattingObservation, thinking_id: str, log_prefix: str) -> bool: """ 等待新消息 或 检测到关闭信号 参数: observation: 观察实例 planner_start_db_time: 开始等待的时间 log_prefix: 日志前缀 返回: bool: 是否检测到新消息 (如果因关闭信号退出则返回 False) """ wait_start_time = time.monotonic() while True: # --- 在每次循环开始时检查关闭标志 --- if self._shutting_down: logger.info(f"{log_prefix} 等待新消息时检测到关闭信号，中断等待。") return False # 表示因为关闭而退出 # ----------------------------------- thinking_id_timestamp = parse_thinking_id_to_timestamp(thinking_id) # 检查新消息 if await observation.has_new_messages_since(thinking_id_timestamp): logger.info(f"{log_prefix} 检测到新消息") return True # 检查超时 (放在检查新消息和关闭之后) if time.monotonic() - wait_start_time > WAITING_TIME_THRESHOLD: logger.warning(f"{log_prefix} 等待新消息超时({WAITING_TIME_THRESHOLD}秒)") return False try: # 短暂休眠，让其他任务有机会运行，并能更快响应取消或关闭 await asyncio.sleep(0.5) # 缩短休眠时间 except asyncio.CancelledError: # 如果在休眠时被取消，再次检查关闭标志 # 如果是正常关闭，则不需要警告 if not self._shutting_down: logger.warning(f"{log_prefix} _wait_for_new_message 的休眠被意外取消") # 无论如何，重新抛出异常，让上层处理 raise 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] async def _planner(self, all_plan_info: List[InfoBase], cycle_timers: dict) -> Dict[str, Any]: """ 规划器 (Planner): 使用LLM根据上下文决定是否和如何回复。 重构为：让LLM返回结构化JSON文本，然后在代码中解析。 参数: current_mind: 子思维的当前思考结果 cycle_timers: 计时器字典 is_re_planned: 是否为重新规划 (此重构中暂时简化，不处理 is_re_planned 的特殊逻辑) """ logger.info(f"{self.log_prefix}开始 规划") actions_to_remove_temporarily = [] # --- 检查历史动作并决定临时移除动作 (逻辑保持不变) --- lian_xu_wen_ben_hui_fu = 0 probability_roll = random.random() for cycle in reversed(self._cycle_history): if cycle.action_taken: if cycle.action_type == "text_reply": lian_xu_wen_ben_hui_fu += 1 else: break if len(self._cycle_history) > 0 and cycle.cycle_id <= self._cycle_history[0].cycle_id + ( len(self._cycle_history) - 4 ): break logger.debug(f"{self.log_prefix}[Planner] 检测到连续文本回复次数: {lian_xu_wen_ben_hui_fu}") if lian_xu_wen_ben_hui_fu >= 3: logger.info(f"{self.log_prefix}[Planner] 连续回复 >= 3 次，强制移除 text_reply 和 emoji_reply") actions_to_remove_temporarily.extend(["text_reply", "emoji_reply"]) elif lian_xu_wen_ben_hui_fu == 2: if probability_roll < 0.8: logger.info(f"{self.log_prefix}[Planner] 连续回复 2 次，80% 概率移除 text_reply 和 emoji_reply (触发)") actions_to_remove_temporarily.extend(["text_reply", "emoji_reply"]) else: logger.info( f"{self.log_prefix}[Planner] 连续回复 2 次，80% 概率移除 text_reply 和 emoji_reply (未触发)" ) elif lian_xu_wen_ben_hui_fu == 1: if probability_roll < 0.4: logger.info(f"{self.log_prefix}[Planner] 连续回复 1 次，40% 概率移除 text_reply (触发)") actions_to_remove_temporarily.append("text_reply") else: logger.info(f"{self.log_prefix}[Planner] 连续回复 1 次，40% 概率移除 text_reply (未触发)") # --- 结束检查历史动作 --- # 获取观察信息 for info in all_plan_info: if isinstance(info, ObsInfo): logger.debug(f"{self.log_prefix} 观察信息: {info}") observed_messages = info.get_talking_message() observed_messages_str = info.get_talking_message_str_truncate() chat_type = info.get_chat_type() if chat_type == "group": is_group_chat = True else: is_group_chat = False elif isinstance(info, MindInfo): logger.debug(f"{self.log_prefix} 思维信息: {info}") current_mind = info.get_current_mind() elif isinstance(info, CycleInfo): logger.debug(f"{self.log_prefix} 循环信息: {info}") cycle_info = info.get_observe_info() elif isinstance(info, StructuredInfo): logger.debug(f"{self.log_prefix} 结构化信息: {info}") structured_info = info.get_data() # --- 使用 LLM 进行决策 (JSON 输出模式) --- # action = "no_reply" # 默认动作 reasoning = "规划器初始化默认" llm_error = False # LLM 请求或解析错误标志 # 获取我们将传递给 prompt 构建器和用于验证的当前可用动作 current_available_actions = self.action_manager.get_available_actions() try: # --- 应用临时动作移除 --- if actions_to_remove_temporarily: self.action_manager.temporarily_remove_actions(actions_to_remove_temporarily) # 更新 current_available_actions 以反映移除后的状态 current_available_actions = self.action_manager.get_available_actions() logger.debug( f"{self.log_prefix}[Planner] 临时移除的动作: {actions_to_remove_temporarily}, 当前可用: {list(current_available_actions.keys())}" ) # --- 构建提示词 (调用修改后的 PromptBuilder 方法) --- prompt = await prompt_builder.build_planner_prompt( is_group_chat=is_group_chat, # <-- Pass HFC state chat_target_info=None, observed_messages_str=observed_messages_str, # <-- Pass local variable current_mind=current_mind, # <-- Pass argument structured_info=structured_info, # <-- Pass SubMind info current_available_actions=current_available_actions, # <-- Pass determined actions cycle_info=cycle_info, # <-- Pass cycle info ) # --- 调用 LLM (普通文本生成) --- llm_content = None try: llm_content, _, _ = await self.planner_llm.generate_response(prompt=prompt) logger.debug(f"{self.log_prefix}[Planner] LLM 原始 JSON 响应 (预期): {llm_content}") except Exception as req_e: logger.error(f"{self.log_prefix}[Planner] LLM 请求执行失败: {req_e}") reasoning = f"LLM 请求失败: {req_e}" llm_error = True # 直接使用默认动作返回错误结果 action = "no_reply" # 明确设置为默认值 # --- 解析 LLM 返回的 JSON (仅当 LLM 请求未出错时进行) --- if not llm_error and llm_content: try: # 尝试去除可能的 markdown 代码块标记 cleaned_content = ( llm_content.strip().removeprefix("```json").removeprefix("```").removesuffix("```").strip() ) if not cleaned_content: raise json.JSONDecodeError("Cleaned content is empty", cleaned_content, 0) parsed_json = json.loads(cleaned_content) # 提取决策，提供默认值 extracted_action = parsed_json.get("action", "no_reply") extracted_reasoning = parsed_json.get("reasoning", "LLM未提供理由") # extracted_emoji_query = parsed_json.get("emoji_query", "") # 新的reply格式 if extracted_action == "reply": action_data = { "text": parsed_json.get("text", []), "emojis": parsed_json.get("emojis", []), "target": parsed_json.get("target", ""), } else: action_data = {} # 其他动作可能不需要额外数据 # 验证动作是否在当前可用列表中 # !! 使用调用 prompt 时实际可用的动作列表进行验证 if extracted_action not in current_available_actions: logger.warning( f"{self.log_prefix}[Planner] LLM 返回了当前不可用或无效的动作: '{extracted_action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_reply'" ) action = "no_reply" reasoning = f"LLM 返回了当前不可用的动作 '{extracted_action}' (可用: {list(current_available_actions.keys())})。原始理由: {extracted_reasoning}" # 检查 no_reply 是否也恰好被移除了 (极端情况) if "no_reply" not in current_available_actions: logger.error( f"{self.log_prefix}[Planner] 严重错误：'no_reply' 动作也不可用！无法执行任何动作。" ) action = "error" # 回退到错误状态 reasoning = "无法执行任何有效动作，包括 no_reply" llm_error = True # 标记为严重错误 else: llm_error = False # 视为逻辑修正而非 LLM 错误 else: # 动作有效且可用 action = extracted_action reasoning = extracted_reasoning llm_error = False # 解析成功 logger.debug( f"{self.log_prefix}[要做什么]\nPrompt:\n{prompt}\n\n决策结果 (来自JSON): {action}, 理由: {reasoning}" ) logger.debug(f"{self.log_prefix}动作信息: '{action_data}'") except Exception as json_e: logger.warning( f"{self.log_prefix}[Planner] 解析LLM响应JSON失败: {json_e}. LLM原始输出: '{llm_content}'" ) reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_reply'." action = "no_reply" # 解析失败则默认不回复 llm_error = True # 标记解析错误 elif not llm_error and not llm_content: # LLM 请求成功但返回空内容 logger.warning(f"{self.log_prefix}[Planner] LLM 返回了空内容。") reasoning = "LLM 返回了空内容，使用默认动作 'no_reply'." action = "no_reply" llm_error = True # 标记为空响应错误 except Exception as outer_e: logger.error(f"{self.log_prefix}[Planner] Planner 处理过程中发生意外错误: {outer_e}") traceback.print_exc() action = "error" # 发生未知错误，标记为 error 动作 reasoning = f"Planner 内部处理错误: {outer_e}" llm_error = True finally: # --- 确保动作恢复 --- if self.action_manager._original_actions_backup is not None: self.action_manager.restore_actions() logger.debug( f"{self.log_prefix}[Planner] 恢复了原始动作集, 当前可用: {list(self.action_manager.get_available_actions().keys())}" ) # --- 概率性忽略文本回复附带的表情 (逻辑保持不变) --- emoji = action_data.get("emojis") if action == "reply" and emoji: logger.debug(f"{self.log_prefix}[Planner] 大模型建议文字回复带表情: '{emoji}'") if random.random() > EMOJI_SEND_PRO: logger.info(f"{self.log_prefix}但是麦麦这次不想加表情 ({1 - EMOJI_SEND_PRO:.0%})，忽略表情 '{emoji}'") action_data["emojis"] = "" # 清空表情请求 else: logger.info(f"{self.log_prefix}好吧，加上表情 '{emoji}'") # --- 结束概率性忽略 --- # 返回结果字典 return { "action": action, "action_data": action_data, "reasoning": reasoning, "current_mind": current_mind, "observed_messages": observed_messages, "llm_error": llm_error, # 返回错误状态 } async def _handle_reply( self, reasoning: str, reply_data: dict, cycle_timers: dict, thinking_id: str ) -> tuple[bool, str]: """ 处理统一的回复动作 - 可包含文本和表情，顺序任意 reply_data格式: { "text": "你好啊" # 文本内容列表（可选） "target": "锚定消息", # 锚定消息的文本内容 "emojis": "微笑" # 表情关键词列表（可选） } """ # 重置连续不回复计数器 self.total_no_reply_count = 0 self.total_waiting_time = 0.0 # 从聊天观察获取锚定消息 observations: ChattingObservation = self.observations[0] anchor_message = observations.serch_message_by_text(reply_data["target"]) # 如果没有找到锚点消息，创建一个占位符 if not anchor_message: logger.info(f"{self.log_prefix} 未找到锚点消息，创建占位符") anchor_message = await create_empty_anchor_message( self.chat_stream.platform, self.chat_stream.group_info, self.chat_stream ) else: anchor_message.update_chat_stream(self.chat_stream) success, reply_set = await self.expressor.deal_reply( cycle_timers=cycle_timers, action_data=reply_data, anchor_message=anchor_message, reasoning=reasoning, thinking_id=thinking_id, ) reply_text = "" for reply in reply_set: type = reply[0] data = reply[1] if type == "text": reply_text += data elif type == "emoji": reply_text += data self._current_cycle.set_response_info( response_text=reply_text, ) return success, reply_text