import asyncio import time import traceback from random import random from typing import List, Optional, Dict, Any # 导入类型提示 from maim_message import UserInfo, Seg from src.common.logger import get_logger from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info from src.manager.mood_manager import mood_manager from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager from src.chat.utils.timer_calculator import Timer from src.chat.utils.prompt_builder import global_prompt_manager from .normal_chat_generator import NormalChatGenerator from ..message_receive.message import MessageSending, MessageRecv, MessageThinking, MessageSet from src.chat.message_receive.message_sender import message_manager from src.chat.normal_chat.willing.willing_manager import get_willing_manager from src.chat.normal_chat.normal_chat_utils import get_recent_message_stats from src.config.config import global_config from src.chat.focus_chat.planners.action_manager import ActionManager from src.chat.normal_chat.normal_chat_planner import NormalChatPlanner from src.chat.normal_chat.normal_chat_action_modifier import NormalChatActionModifier from src.chat.normal_chat.normal_chat_expressor import NormalChatExpressor from src.chat.focus_chat.replyer.default_replyer import DefaultReplyer from src.person_info.person_info import PersonInfoManager from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_with_chat from src.person_info.relationship_manager import get_relationship_manager willing_manager = get_willing_manager() logger = get_logger("normal_chat") class NormalChat: def __init__(self, chat_stream: ChatStream, interest_dict: dict = None, on_switch_to_focus_callback=None): """初始化 NormalChat 实例。只进行同步操作。""" self.chat_stream = chat_stream self.stream_id = chat_stream.stream_id self.stream_name = get_chat_manager().get_stream_name(self.stream_id) or self.stream_id # 初始化Normal Chat专用表达器 self.expressor = NormalChatExpressor(self.chat_stream) self.replyer = DefaultReplyer(self.chat_stream) # Interest dict self.interest_dict = interest_dict self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.stream_id) self.willing_amplifier = 1 self.start_time = time.time() # Other sync initializations self.gpt = NormalChatGenerator() self.mood_manager = mood_manager self.start_time = time.time() self._chat_task: Optional[asyncio.Task] = None self._initialized = False # Track initialization status # Planner相关初始化 self.action_manager = ActionManager() self.planner = NormalChatPlanner(self.stream_name, self.action_manager) self.action_modifier = NormalChatActionModifier(self.action_manager, self.stream_id, self.stream_name) self.enable_planner = global_config.normal_chat.enable_planner # 从配置中读取是否启用planner # 记录最近的回复内容，每项包含: {time, user_message, response, is_mentioned, is_reference_reply} self.recent_replies = [] self.max_replies_history = 20 # 最多保存最近20条回复记录 # 添加engaging_person统计 self.engaging_persons = {} # person_id -> {first_time, last_time, receive_count, reply_count, relation_built} # 添加回调函数，用于在满足条件时通知切换到focus_chat模式 self.on_switch_to_focus_callback = on_switch_to_focus_callback self._disabled = False # 增加停用标志 logger.debug(f"[{self.stream_name}] NormalChat 初始化完成 (异步部分)。") # 改为实例方法 async def _create_thinking_message(self, message: MessageRecv, timestamp: Optional[float] = None) -> str: """创建思考消息""" messageinfo = message.message_info bot_user_info = UserInfo( user_id=global_config.bot.qq_account, user_nickname=global_config.bot.nickname, platform=messageinfo.platform, ) thinking_time_point = round(time.time(), 2) thinking_id = "tid" + str(thinking_time_point) thinking_message = MessageThinking( message_id=thinking_id, chat_stream=self.chat_stream, bot_user_info=bot_user_info, reply=message, thinking_start_time=thinking_time_point, timestamp=timestamp if timestamp is not None else None, ) await message_manager.add_message(thinking_message) return thinking_id # 改为实例方法 async def _add_messages_to_manager( self, message: MessageRecv, response_set: List[str], thinking_id ) -> Optional[MessageSending]: """发送回复消息""" container = await message_manager.get_container(self.stream_id) # 使用 self.stream_id thinking_message = None for msg in container.messages[:]: if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id: thinking_message = msg container.messages.remove(msg) break if not thinking_message: logger.warning(f"[{self.stream_name}] 未找到对应的思考消息 {thinking_id}，可能已超时被移除") return None thinking_start_time = thinking_message.thinking_start_time message_set = MessageSet(self.chat_stream, thinking_id) # 使用 self.chat_stream mark_head = False first_bot_msg = None for msg in response_set: if global_config.experimental.debug_show_chat_mode: msg += "ⁿ" message_segment = Seg(type="text", data=msg) bot_message = MessageSending( message_id=thinking_id, chat_stream=self.chat_stream, # 使用 self.chat_stream bot_user_info=UserInfo( user_id=global_config.bot.qq_account, user_nickname=global_config.bot.nickname, platform=message.message_info.platform, ), sender_info=message.message_info.user_info, message_segment=message_segment, reply=message, is_head=not mark_head, is_emoji=False, thinking_start_time=thinking_start_time, apply_set_reply_logic=True, ) if not mark_head: mark_head = True first_bot_msg = bot_message message_set.add_message(bot_message) await message_manager.add_message(message_set) return first_bot_msg async def _reply_interested_message(self) -> None: """ 后台任务方法，轮询当前实例关联chat的兴趣消息 通常由start_monitoring_interest()启动 """ logger.debug(f"[{self.stream_name}] 兴趣监控任务开始") try: while True: # 第一层检查：立即检查取消和停用状态 if self._disabled: logger.info(f"[{self.stream_name}] 检测到停用标志，退出兴趣监控") break # 检查当前任务是否已被取消 current_task = asyncio.current_task() if current_task and current_task.cancelled(): logger.info(f"[{self.stream_name}] 当前任务已被取消，退出") break try: # 短暂等待，让出控制权 await asyncio.sleep(0.1) # 第二层检查：睡眠后再次检查状态 if self._disabled: logger.info(f"[{self.stream_name}] 睡眠后检测到停用标志，退出") break # 获取待处理消息 items_to_process = list(self.interest_dict.items()) if not items_to_process: # 没有消息时继续下一轮循环 continue # 第三层检查：在处理消息前最后检查一次 if self._disabled: logger.info(f"[{self.stream_name}] 处理消息前检测到停用标志，退出") break # 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() ): # 在上下文内部再次检查取消状态 if self._disabled: logger.info(f"[{self.stream_name}] 在处理上下文中检测到停止信号，退出") break # 并行处理兴趣消息 async def process_single_message(msg_id, message, interest_value, is_mentioned): """处理单个兴趣消息""" try: # 在处理每个消息前检查停止状态 if self._disabled: logger.debug(f"[{self.stream_name}] 处理消息时检测到停用，跳过消息 {msg_id}") return # 处理消息 if time.time() - self.start_time > 300: self.adjust_reply_frequency(duration=300 / 60) else: self.adjust_reply_frequency(duration=(time.time() - self.start_time) / 60) await self.normal_response( message=message, is_mentioned=is_mentioned, interested_rate=interest_value * self.willing_amplifier, ) except asyncio.CancelledError: logger.debug(f"[{self.stream_name}] 处理消息 {msg_id} 时被取消") raise # 重新抛出取消异常 except Exception as e: logger.error(f"[{self.stream_name}] 处理兴趣消息{msg_id}时出错: {e}") # 不打印完整traceback，避免日志污染 finally: # 无论如何都要清理消息 self.interest_dict.pop(msg_id, None) # 创建并行任务列表 tasks = [] for msg_id, (message, interest_value, is_mentioned) in items_to_process: task = process_single_message(msg_id, message, interest_value, is_mentioned) tasks.append(task) # 并行执行所有任务，限制并发数量避免资源过度消耗 if tasks: # 使用信号量控制并发数，最多同时处理5个消息 semaphore = asyncio.Semaphore(5) async def limited_process(task, sem): async with sem: await task limited_tasks = [limited_process(task, semaphore) for task in tasks] await asyncio.gather(*limited_tasks, return_exceptions=True) except asyncio.CancelledError: logger.info(f"[{self.stream_name}] 处理上下文时任务被取消") break except Exception as e: logger.error(f"[{self.stream_name}] 处理上下文时出错: {e}") # 出错后短暂等待，避免快速重试 await asyncio.sleep(0.5) except asyncio.CancelledError: logger.info(f"[{self.stream_name}] 主循环中任务被取消") break except Exception as e: logger.error(f"[{self.stream_name}] 主循环出错: {e}") # 出错后等待一秒再继续 await asyncio.sleep(1.0) except asyncio.CancelledError: logger.info(f"[{self.stream_name}] 兴趣监控任务被取消") except Exception as e: logger.error(f"[{self.stream_name}] 兴趣监控任务严重错误: {e}") finally: logger.debug(f"[{self.stream_name}] 兴趣监控任务结束") # 改为实例方法, 移除 chat 参数 async def normal_response(self, message: MessageRecv, is_mentioned: bool, interested_rate: float) -> None: # 新增：如果已停用，直接返回 if self._disabled: logger.info(f"[{self.stream_name}] 已停用，忽略 normal_response。") return # 更新engaging_persons统计信息 self._update_engaging_person_stats(message, is_reply=False) # 检查是否有用户满足关系构建条件 asyncio.create_task(self._check_relation_building_conditions()) timing_results = {} reply_probability = ( 1.0 if is_mentioned and global_config.normal_chat.mentioned_bot_inevitable_reply else 0.0 ) # 如果被提及，且开启了提及必回复，则基础概率为1，否则需要意愿判断 # 意愿管理器：设置当前message信息 willing_manager.setup(message, self.chat_stream, is_mentioned, interested_rate) # 获取回复概率 # is_willing = False # 仅在未被提及或基础概率不为1时查询意愿概率 if reply_probability < 1: # 简化逻辑，如果未提及 (reply_probability 为 0)，则获取意愿概率 # is_willing = True reply_probability = await willing_manager.get_reply_probability(message.message_info.message_id) if message.message_info.additional_config: if "maimcore_reply_probability_gain" in message.message_info.additional_config.keys(): reply_probability += message.message_info.additional_config["maimcore_reply_probability_gain"] reply_probability = min(max(reply_probability, 0), 1) # 确保概率在 0-1 之间 # 打印消息信息 mes_name = self.chat_stream.group_info.group_name if self.chat_stream.group_info else "私聊" # current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time)) # 使用 self.stream_id # willing_log = f"[激活值:{await willing_manager.get_willing(self.stream_id):.2f}]" if is_willing else "" logger.info( f"[{mes_name}]" f"{message.message_info.user_info.user_nickname}:" # 使用 self.chat_stream f"{message.processed_plain_text}[兴趣:{interested_rate:.2f}][回复概率:{reply_probability * 100:.1f}%]" ) do_reply = False response_set = None # 初始化 response_set if random() < reply_probability: do_reply = True # 回复前处理 await willing_manager.before_generate_reply_handle(message.message_info.message_id) thinking_id = await self._create_thinking_message(message) # 如果启用planner，预先修改可用actions（避免在并行任务中重复调用） available_actions = None if self.enable_planner: try: await self.action_modifier.modify_actions_for_normal_chat( self.chat_stream, self.recent_replies, message.processed_plain_text ) available_actions = self.action_manager.get_using_actions_for_mode("normal") except Exception as e: logger.warning(f"[{self.stream_name}] 获取available_actions失败: {e}") available_actions = None # 定义并行执行的任务 async def generate_normal_response(): """生成普通回复""" try: return await self.gpt.generate_response( message=message, thinking_id=thinking_id, enable_planner=self.enable_planner, available_actions=available_actions, ) except Exception as e: logger.error(f"[{self.stream_name}] 回复生成出现错误：{str(e)} {traceback.format_exc()}") return None async def plan_and_execute_actions(): """规划和执行额外动作""" if not self.enable_planner: logger.debug(f"[{self.stream_name}] Planner未启用，跳过动作规划") return None try: # 获取发送者名称（动作修改已在并行执行前完成） sender_name = self._get_sender_name(message) 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": "", } # 检查是否应该跳过规划 if self.action_modifier.should_skip_planning(): logger.debug(f"[{self.stream_name}] 没有可用动作，跳过规划") self.action_type = "no_action" return no_action # 执行规划 plan_result = await self.planner.plan(message, sender_name) action_type = plan_result["action_result"]["action_type"] action_data = plan_result["action_result"]["action_data"] reasoning = plan_result["action_result"]["reasoning"] is_parallel = plan_result["action_result"].get("is_parallel", False) logger.info( f"[{self.stream_name}] Planner决策: {action_type}, 理由: {reasoning}, 并行执行: {is_parallel}" ) self.action_type = action_type # 更新实例属性 self.is_parallel_action = is_parallel # 新增：保存并行执行标志 # 如果规划器决定不执行任何动作 if action_type == "no_action": logger.debug(f"[{self.stream_name}] Planner决定不执行任何额外动作") return no_action elif action_type == "change_to_focus_chat": logger.info(f"[{self.stream_name}] Planner决定切换到focus聊天模式") return None # 执行额外的动作（不影响回复生成） action_result = await self._execute_action(action_type, action_data, message, thinking_id) if action_result is not None: logger.info(f"[{self.stream_name}] 额外动作 {action_type} 执行完成") else: logger.warning(f"[{self.stream_name}] 额外动作 {action_type} 执行失败") return { "action_type": action_type, "action_data": action_data, "reasoning": reasoning, "is_parallel": is_parallel, } except Exception as e: logger.error(f"[{self.stream_name}] Planner执行失败: {e}") return no_action # 并行执行回复生成和动作规划 self.action_type = None # 初始化动作类型 self.is_parallel_action = False # 初始化并行动作标志 with Timer("并行生成回复和规划", timing_results): response_set, plan_result = await asyncio.gather( generate_normal_response(), plan_and_execute_actions(), return_exceptions=True ) # 处理生成回复的结果 if isinstance(response_set, Exception): logger.error(f"[{self.stream_name}] 回复生成异常: {response_set}") response_set = None # 处理规划结果（可选，不影响回复） if isinstance(plan_result, Exception): logger.error(f"[{self.stream_name}] 动作规划异常: {plan_result}") elif plan_result: logger.debug(f"[{self.stream_name}] 额外动作处理完成: {self.action_type}") if not response_set or ( self.enable_planner and self.action_type not in ["no_action", "change_to_focus_chat"] and not self.is_parallel_action ): if not response_set: logger.info(f"[{self.stream_name}] 模型未生成回复内容") elif ( self.enable_planner and self.action_type not in ["no_action", "change_to_focus_chat"] and not self.is_parallel_action ): logger.info(f"[{self.stream_name}] 模型选择其他动作（非并行动作）") # 如果模型未生成回复，移除思考消息 container = await message_manager.get_container(self.stream_id) # 使用 self.stream_id for msg in container.messages[:]: if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id: container.messages.remove(msg) logger.debug(f"[{self.stream_name}] 已移除未产生回复的思考消息 {thinking_id}") break # 需要在此处也调用 not_reply_handle 和 delete 吗？ # 如果是因为模型没回复，也算是一种 "未回复" await willing_manager.not_reply_handle(message.message_info.message_id) willing_manager.delete(message.message_info.message_id) return # 不执行后续步骤 # logger.info(f"[{self.stream_name}] 回复内容: {response_set}") if self._disabled: logger.info(f"[{self.stream_name}] 已停用，忽略 normal_response。") return # 发送回复 (不再需要传入 chat) with Timer("消息发送", timing_results): first_bot_msg = await self._add_messages_to_manager(message, response_set, thinking_id) # 检查 first_bot_msg 是否为 None (例如思考消息已被移除的情况) if first_bot_msg: # 更新engaging_persons统计信息 - 标记为回复 self._update_engaging_person_stats(message, is_reply=True) # 记录回复信息到最近回复列表中 reply_info = { "time": time.time(), "user_message": message.processed_plain_text, "user_info": { "user_id": message.message_info.user_info.user_id, "user_nickname": message.message_info.user_info.user_nickname, }, "response": response_set, "is_mentioned": is_mentioned, "is_reference_reply": message.reply is not None, # 判断是否为引用回复 "timing": {k: round(v, 2) for k, v in timing_results.items()}, } self.recent_replies.append(reply_info) # 保持最近回复历史在限定数量内 if len(self.recent_replies) > self.max_replies_history: self.recent_replies = self.recent_replies[-self.max_replies_history :] # 检查是否需要切换到focus模式 if global_config.chat.chat_mode == "auto": if self.action_type == "change_to_focus_chat": logger.info(f"[{self.stream_name}] 检测到切换到focus聊天模式的请求") if self.on_switch_to_focus_callback: await self.on_switch_to_focus_callback() else: logger.warning(f"[{self.stream_name}] 没有设置切换到focus聊天模式的回调函数，无法执行切换") return # 回复后处理 await willing_manager.after_generate_reply_handle(message.message_info.message_id) # 输出性能计时结果 if do_reply and response_set: # 确保 response_set 不是 None timing_str = " | ".join([f"{step}: {duration:.2f}秒" for step, duration in timing_results.items()]) trigger_msg = message.processed_plain_text response_msg = " ".join(response_set) logger.info( f"[{self.stream_name}]回复消息: {trigger_msg[:30]}... | 回复内容: {response_msg[:30]}... | 计时: {timing_str}" ) elif not do_reply: # 不回复处理 await willing_manager.not_reply_handle(message.message_info.message_id) # 意愿管理器：注销当前message信息 (无论是否回复，只要处理过就删除) willing_manager.delete(message.message_info.message_id) # 改为实例方法, 移除 chat 参数 async def start_chat(self): """启动聊天任务。""" logger.debug(f"[{self.stream_name}] 开始启动聊天任务") # 重置停用标志 self._disabled = False # 检查是否已有运行中的任务 if self._chat_task and not self._chat_task.done(): logger.info(f"[{self.stream_name}] 聊天轮询任务已在运行中。") return # 清理可能存在的已完成任务引用 if self._chat_task and self._chat_task.done(): self._chat_task = None try: logger.debug(f"[{self.stream_name}] 创建新的聊天轮询任务") polling_task = asyncio.create_task(self._reply_interested_message()) # 设置回调 polling_task.add_done_callback(lambda t: self._handle_task_completion(t)) # 保存任务引用 self._chat_task = polling_task logger.debug(f"[{self.stream_name}] 聊天任务启动完成") except Exception as e: logger.error(f"[{self.stream_name}] 启动聊天任务失败: {e}") self._chat_task = None raise def _handle_task_completion(self, task: asyncio.Task): """任务完成回调处理""" try: # 简化回调逻辑，避免复杂的异常处理 logger.debug(f"[{self.stream_name}] 任务完成回调被调用") # 检查是否是我们管理的任务 if task is not self._chat_task: # 如果已经不是当前任务（可能在stop_chat中已被清空），直接返回 logger.debug(f"[{self.stream_name}] 回调的任务不是当前管理的任务") return # 清理任务引用 self._chat_task = None logger.debug(f"[{self.stream_name}] 任务引用已清理") # 简单记录任务状态，不进行复杂处理 if task.cancelled(): logger.debug(f"[{self.stream_name}] 任务已取消") elif task.done(): try: # 尝试获取异常，但不抛出 exc = task.exception() if exc: logger.error(f"[{self.stream_name}] 任务异常: {type(exc).__name__}: {exc}") else: logger.debug(f"[{self.stream_name}] 任务正常完成") except Exception as e: # 获取异常时也可能出错，静默处理 logger.debug(f"[{self.stream_name}] 获取任务异常时出错: {e}") except Exception as e: # 回调函数中的任何异常都要捕获，避免影响系统 logger.error(f"[{self.stream_name}] 任务完成回调处理出错: {e}") # 确保任务引用被清理 self._chat_task = None # 改为实例方法, 移除 stream_id 参数 async def stop_chat(self): """停止当前实例的兴趣监控任务。""" logger.debug(f"[{self.stream_name}] 开始停止聊天任务") # 立即设置停用标志，防止新任务启动 self._disabled = True # 如果没有运行中的任务，直接返回 if not self._chat_task or self._chat_task.done(): logger.debug(f"[{self.stream_name}] 没有运行中的任务，直接完成停止") self._chat_task = None return # 保存任务引用并立即清空，避免回调中的循环引用 task_to_cancel = self._chat_task self._chat_task = None logger.debug(f"[{self.stream_name}] 取消聊天任务") # 尝试优雅取消任务 task_to_cancel.cancel() # 不等待任务完成，让它自然结束 # 这样可以避免等待过程中的潜在递归问题 # 异步清理思考消息，不阻塞当前流程 asyncio.create_task(self._cleanup_thinking_messages_async()) logger.debug(f"[{self.stream_name}] 聊天任务停止完成") async def _cleanup_thinking_messages_async(self): """异步清理思考消息，避免阻塞主流程""" try: # 添加短暂延迟，让任务有时间响应取消 await asyncio.sleep(0.1) container = await message_manager.get_container(self.stream_id) if container: # 查找并移除所有 MessageThinking 类型的消息 thinking_messages = [msg for msg in container.messages[:] if isinstance(msg, MessageThinking)] if thinking_messages: for msg in thinking_messages: container.messages.remove(msg) logger.info(f"[{self.stream_name}] 清理了 {len(thinking_messages)} 条未处理的思考消息。") except Exception as e: logger.error(f"[{self.stream_name}] 异步清理思考消息时出错: {e}") # 不打印完整栈跟踪，避免日志污染 # 获取最近回复记录的方法 def get_recent_replies(self, limit: int = 10) -> List[dict]: """获取最近的回复记录 Args: limit: 最大返回数量，默认10条 Returns: List[dict]: 最近的回复记录列表，每项包含： time: 回复时间戳 user_message: 用户消息内容 user_info: 用户信息(user_id, user_nickname) response: 回复内容 is_mentioned: 是否被提及(@) is_reference_reply: 是否为引用回复 timing: 各阶段耗时 """ # 返回最近的limit条记录，按时间倒序排列 return sorted(self.recent_replies[-limit:], key=lambda x: x["time"], reverse=True) def adjust_reply_frequency(self, duration: int = 10): """ 调整回复频率 """ # 获取最近30分钟内的消息统计 stats = get_recent_message_stats(minutes=duration, chat_id=self.stream_id) bot_reply_count = stats["bot_reply_count"] total_message_count = stats["total_message_count"] if total_message_count == 0: return logger.debug( f"[{self.stream_name}]({self.willing_amplifier}) 最近{duration}分钟 回复数量: {bot_reply_count}，消息总数: {total_message_count}" ) # 计算回复频率 _reply_frequency = bot_reply_count / total_message_count differ = global_config.normal_chat.talk_frequency - (bot_reply_count / duration) # 如果回复频率低于0.5，增加回复概率 if differ > 0.1: mapped = 1 + (differ - 0.1) * 4 / 0.9 mapped = max(1, min(5, mapped)) logger.debug( f"[{self.stream_name}] 回复频率低于{global_config.normal_chat.talk_frequency}，增加回复概率，differ={differ:.3f}，映射值={mapped:.2f}" ) self.willing_amplifier += mapped * 0.1 # 你可以根据实际需要调整系数 elif differ < -0.1: mapped = 1 - (differ + 0.1) * 4 / 0.9 mapped = max(1, min(5, mapped)) logger.debug( f"[{self.stream_name}] 回复频率高于{global_config.normal_chat.talk_frequency}，减少回复概率，differ={differ:.3f}，映射值={mapped:.2f}" ) self.willing_amplifier -= mapped * 0.1 if self.willing_amplifier > 5: self.willing_amplifier = 5 elif self.willing_amplifier < 0.1: self.willing_amplifier = 0.1 def _get_sender_name(self, message: MessageRecv) -> str: """获取发送者名称，用于planner""" if message.chat_stream.user_info: user_info = message.chat_stream.user_info if user_info.user_cardname and user_info.user_nickname: return f"[{user_info.user_nickname}][群昵称：{user_info.user_cardname}]" elif user_info.user_nickname: return f"[{user_info.user_nickname}]" else: return f"用户({user_info.user_id})" return "某人" async def _execute_action( self, action_type: str, action_data: dict, message: MessageRecv, thinking_id: str ) -> Optional[bool]: """执行具体的动作，只返回执行成功与否""" try: # 创建动作处理器实例 action_handler = self.action_manager.create_action( action_name=action_type, action_data=action_data, reasoning=action_data.get("reasoning", ""), cycle_timers={}, # normal_chat使用空的cycle_timers thinking_id=thinking_id, observations=[], # normal_chat不使用observations expressor=self.expressor, # 使用normal_chat专用的expressor replyer=self.replyer, chat_stream=self.chat_stream, log_prefix=self.stream_name, shutting_down=self._disabled, ) if action_handler: # 执行动作 result = await action_handler.handle_action() success = False if result and isinstance(result, tuple) and len(result) >= 2: # handle_action返回 (success: bool, message: str) success = result[0] elif result: # 如果返回了其他结果，假设成功 success = True return success except Exception as e: logger.error(f"[{self.stream_name}] 执行动作 {action_type} 失败: {e}") return False def set_planner_enabled(self, enabled: bool): """设置是否启用planner""" self.enable_planner = enabled logger.info(f"[{self.stream_name}] Planner {'启用' if enabled else '禁用'}") def get_action_manager(self) -> ActionManager: """获取动作管理器实例""" return self.action_manager def _update_engaging_person_stats(self, message: MessageRecv, is_reply: bool): """更新engaging_persons统计信息""" # 通过platform和user_id计算person_id platform = message.message_info.platform user_id = message.message_info.user_info.user_id person_id = PersonInfoManager.get_person_id(platform, user_id) current_time = time.time() if person_id not in self.engaging_persons: self.engaging_persons[person_id] = { "first_time": current_time, "last_time": current_time, "receive_count": 0, "reply_count": 0, "relation_built": False, } if is_reply: self.engaging_persons[person_id]["reply_count"] += 1 logger.debug( f"[{self.stream_name}] 用户 {person_id} 回复次数更新: {self.engaging_persons[person_id]['reply_count']}" ) else: self.engaging_persons[person_id]["receive_count"] += 1 self.engaging_persons[person_id]["last_time"] = current_time logger.debug( f"[{self.stream_name}] 用户 {person_id} 消息次数更新: {self.engaging_persons[person_id]['receive_count']}" ) async def _check_relation_building_conditions(self): """检查engaging_persons中是否有满足关系构建条件的用户""" current_time = time.time() for person_id, stats in list(self.engaging_persons.items()): # 计算时间差和消息数量 time_elapsed = current_time - stats["first_time"] total_messages = self._get_total_messages_in_timerange(stats["first_time"], stats["last_time"]) # print(f"person_id: {person_id}, total_messages: {total_messages}, time_elapsed: {time_elapsed}") # 检查是否满足关系构建条件 should_build_relation = ( total_messages >= 30 # 30条消息必定满足 or (total_messages >= 15 and time_elapsed >= 600) # 15条且10分钟 or (total_messages >= 10 and time_elapsed >= 900) # 10条且15分钟 or (total_messages >= 5 and time_elapsed >= 1800) # 5条且30 ) if should_build_relation: logger.info( f"[{self.stream_name}] 用户 {person_id} 满足关系构建条件。" f"消息数：{total_messages}，时长：{time_elapsed:.0f}秒，" f"收到消息：{stats['receive_count']}，回复次数：{stats['reply_count']}" ) # 计算构建概率并决定是否构建 await self._evaluate_and_build_relation(person_id, stats, total_messages) # 评估完成后移除该用户，重新开始统计 del self.engaging_persons[person_id] logger.info(f"[{self.stream_name}] 用户 {person_id} 评估完成，已移除记录，将重新开始统计") def _get_total_messages_in_timerange(self, start_time: float, end_time: float) -> int: """获取指定时间范围内的总消息数量""" try: messages = get_raw_msg_by_timestamp_with_chat(self.stream_id, start_time, end_time) return len(messages) if messages else 0 except Exception as e: logger.error(f"[{self.stream_name}] 获取时间范围内消息数量失败: {e}") return 0 async def _evaluate_and_build_relation(self, person_id: str, stats: dict, total_messages: int): """评估并执行关系构建""" import math receive_count = stats["receive_count"] reply_count = stats["reply_count"] # 计算回复概率（reply_count在总消息中的比值） reply_ratio = reply_count / total_messages if total_messages > 0 else 0 # 使用对数函数让低比率时概率上升更快：log(1 + ratio * k) / log(1 + k) + base # k=7时，0.05比率对应约0.4概率，0.1比率对应约0.6概率，0.2比率对应约0.8概率 k_reply = 10 * global_config.relationship.relation_frequency base_reply_prob = 0.1 # 基础概率10% reply_build_probability = ( (math.log(1 + reply_ratio * k_reply) / math.log(1 + k_reply)) * 0.9 + base_reply_prob if reply_ratio > 0 else base_reply_prob ) # 计算接收概率（receive_count的影响） receive_ratio = receive_count / total_messages if total_messages > 0 else 0 # 接收概率使用更温和的对数曲线，最大0.5，基础0.08 k_receive = 10 * global_config.relationship.relation_frequency base_receive_prob = 0.08 # 基础概率8% receive_build_probability = ( (math.log(1 + receive_ratio * k_receive) / math.log(1 + k_receive)) * 0.42 + base_receive_prob if receive_ratio > 0 else base_receive_prob ) # 取最高概率 final_probability = max(reply_build_probability, receive_build_probability) logger.info( f"[{self.stream_name}] 用户 {person_id} 关系构建概率评估：" f"回复比例：{reply_ratio:.2f}(对数概率:{reply_build_probability:.2f})" f"，接收比例：{receive_ratio:.2f}(对数概率:{receive_build_probability:.2f})" f"，最终概率：{final_probability:.2f}" ) # 使用随机数决定是否构建关系 if random() < final_probability: logger.info(f"[{self.stream_name}] 决定为用户 {person_id} 构建关系") await self._build_relation_for_person(person_id, stats) else: logger.info(f"[{self.stream_name}] 用户 {person_id} 未通过关系构建概率判定") async def _build_relation_for_person(self, person_id: str, stats: dict): """为特定用户构建关系""" try: start_time = stats["first_time"] end_time = stats["last_time"] # 获取该时间段的所有消息用于关系构建 main_messages = get_raw_msg_by_timestamp_with_chat(self.stream_id, start_time, end_time) if not main_messages: logger.warning(f"[{self.stream_name}] 未找到用户 {person_id} 的消息，关系构建跳过") return # 获取第一条消息的时间戳，然后获取之前的5条消息 first_message_time = main_messages[0]["time"] before_messages = self._get_messages_before_timestamp(first_message_time, 5) # 获取最后一条消息的时间戳，然后获取之后的5条消息 last_message_time = main_messages[-1]["time"] after_messages = self._get_messages_after_timestamp(last_message_time, 5) # 合并所有消息并去重 all_messages = before_messages + main_messages + after_messages # 根据消息ID去重并按时间排序 seen_ids = set() unique_messages = [] for msg in all_messages: msg_id = msg["message_id"] if msg_id not in seen_ids: seen_ids.add(msg_id) unique_messages.append(msg) # 按时间排序 unique_messages.sort(key=lambda x: x["time"]) logger.info( f"[{self.stream_name}] 为用户 {person_id} 获取到消息用于关系构建: " f"原时间段内 {len(main_messages)} 条，之前 {len(before_messages)} 条，" f"之后 {len(after_messages)} 条，去重后总计 {len(unique_messages)} 条" ) # 调用关系管理器更新印象 relationship_manager = get_relationship_manager() await relationship_manager.update_person_impression( person_id=person_id, timestamp=end_time, bot_engaged_messages=unique_messages ) logger.info(f"[{self.stream_name}] 用户 {person_id} 关系构建完成") except Exception as e: logger.error(f"[{self.stream_name}] 为用户 {person_id} 构建关系时出错: {e}") traceback.print_exc() def _get_messages_before_timestamp(self, timestamp: float, limit: int = 5) -> List[Dict[str, Any]]: """获取指定时间戳之前的指定数量消息""" try: from src.common.message_repository import find_messages filter_query = {"chat_id": self.stream_id, "time": {"$lt": timestamp}} sort_order = [("time", -1)] # 倒序排列，取最近的几条 messages = find_messages(message_filter=filter_query, sort=sort_order, limit=limit) # 返回时保持正序 return sorted(messages, key=lambda x: x["time"]) except Exception as e: logger.error(f"[{self.stream_name}] 获取时间戳之前的消息失败: {e}") return [] def _get_messages_after_timestamp(self, timestamp: float, limit: int = 5) -> List[Dict[str, Any]]: """获取指定时间戳之后的指定数量消息""" try: from src.common.message_repository import find_messages filter_query = {"chat_id": self.stream_id, "time": {"$gt": timestamp}} sort_order = [("time", 1)] # 正序排列，取最早的几条 return find_messages(message_filter=filter_query, sort=sort_order, limit=limit) except Exception as e: logger.error(f"[{self.stream_name}] 获取时间戳之后的消息失败: {e}") return []