import time import traceback from random import random from typing import List, Optional from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager from .reasoning_generator import ResponseGenerator from ...chat.chat_stream import chat_manager from ...chat.emoji_manager import emoji_manager from ...chat.message import MessageSending, MessageRecv, MessageThinking, MessageSet from ...chat.message_buffer import message_buffer from ...chat.messagesender import message_manager from ...chat.utils import is_mentioned_bot_in_message from ...chat.utils_image import image_path_to_base64 from ...memory_system.Hippocampus import HippocampusManager from ...message import UserInfo, Seg from ...moods.moods import MoodManager from ...person_info.relationship_manager import relationship_manager from ...storage.storage import MessageStorage from ...utils.timer_calculater import Timer from ...willing.willing_manager import willing_manager from ....config.config import global_config # 定义日志配置 chat_config = LogConfig( console_format=CHAT_STYLE_CONFIG["console_format"], file_format=CHAT_STYLE_CONFIG["file_format"], ) logger = get_module_logger("reasoning_chat", config=chat_config) class ReasoningChat: def __init__(self): self.storage = MessageStorage() self.gpt = ResponseGenerator() self.mood_manager = MoodManager.get_instance() @staticmethod async def _create_thinking_message(message, chat, userinfo, messageinfo): """创建思考消息""" bot_user_info = UserInfo( user_id=global_config.BOT_QQ, user_nickname=global_config.BOT_NICKNAME, platform=messageinfo.platform, ) thinking_time_point = round(time.time(), 2) thinking_id = "mt" + str(thinking_time_point) thinking_message = MessageThinking( message_id=thinking_id, chat_stream=chat, bot_user_info=bot_user_info, reply=message, thinking_start_time=thinking_time_point, ) message_manager.add_message(thinking_message) return thinking_id @staticmethod async def _send_response_messages(message, chat, response_set: List[str], thinking_id) -> Optional[MessageSending]: """发送回复消息""" container = message_manager.get_container(chat.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("未找到对应的思考消息，可能已超时被移除") return None thinking_start_time = thinking_message.thinking_start_time message_set = MessageSet(chat, thinking_id) mark_head = False first_bot_msg = None for msg in response_set: message_segment = Seg(type="text", data=msg) bot_message = MessageSending( message_id=thinking_id, chat_stream=chat, bot_user_info=UserInfo( user_id=global_config.BOT_QQ, 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, ) if not mark_head: mark_head = True first_bot_msg = bot_message message_set.add_message(bot_message) message_manager.add_message(message_set) return first_bot_msg @staticmethod async def _handle_emoji(message, chat, response): """处理表情包""" if random() < global_config.emoji_chance: emoji_raw = await emoji_manager.get_emoji_for_text(response) if emoji_raw: emoji_path, description = emoji_raw emoji_cq = image_path_to_base64(emoji_path) thinking_time_point = round(message.message_info.time, 2) message_segment = Seg(type="emoji", data=emoji_cq) bot_message = MessageSending( message_id="mt" + str(thinking_time_point), chat_stream=chat, bot_user_info=UserInfo( user_id=global_config.BOT_QQ, 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=False, is_emoji=True, ) message_manager.add_message(bot_message) async def _update_relationship(self, message: MessageRecv, response_set): """更新关系情绪""" ori_response = ",".join(response_set) stance, emotion = await self.gpt._get_emotion_tags(ori_response, message.processed_plain_text) await relationship_manager.calculate_update_relationship_value( chat_stream=message.chat_stream, label=emotion, stance=stance ) self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor) async def process_message(self, message_data: str) -> None: """处理消息并生成回复""" timing_results = {} response_set = None message = MessageRecv(message_data) groupinfo = message.message_info.group_info userinfo = message.message_info.user_info messageinfo = message.message_info # 消息加入缓冲池 await message_buffer.start_caching_messages(message) # 创建聊天流 chat = await chat_manager.get_or_create_stream( platform=messageinfo.platform, user_info=userinfo, group_info=groupinfo, ) message.update_chat_stream(chat) await message.process() logger.trace(f"消息处理成功: {message.processed_plain_text}") # 过滤词/正则表达式过滤 if self._check_ban_words(message.processed_plain_text, chat, userinfo) or self._check_ban_regex( message.raw_message, chat, userinfo ): return # 查询缓冲器结果，会整合前面跳过的消息，改变processed_plain_text buffer_result = await message_buffer.query_buffer_result(message) # 处理缓冲器结果 if not buffer_result: # await willing_manager.bombing_buffer_message_handle(message.message_info.message_id) # willing_manager.delete(message.message_info.message_id) f_type = "seglist" if message.message_segment.type != "seglist": f_type = message.message_segment.type else: if ( isinstance(message.message_segment.data, list) and all(isinstance(x, Seg) for x in message.message_segment.data) and len(message.message_segment.data) == 1 ): f_type = message.message_segment.data[0].type if f_type == "text": logger.info(f"触发缓冲，已炸飞消息：{message.processed_plain_text}") elif f_type == "image": logger.info("触发缓冲，已炸飞表情包/图片") elif f_type == "seglist": logger.info("触发缓冲，已炸飞消息列") return try: await self.storage.store_message(message, chat) logger.trace(f"存储成功 (通过缓冲后): {message.processed_plain_text}") except Exception as e: logger.error(f"存储消息失败: {e}") logger.error(traceback.format_exc()) # 存储失败可能仍需考虑是否继续，暂时返回 return is_mentioned, reply_probability = is_mentioned_bot_in_message(message) # 记忆激活 with Timer("记忆激活", timing_results): interested_rate = await HippocampusManager.get_instance().get_activate_from_text( message.processed_plain_text, fast_retrieval=True ) # 处理提及 # 意愿管理器：设置当前message信息 willing_manager.setup(message, chat, is_mentioned, interested_rate) # 获取回复概率 is_willing = False if reply_probability != 1: 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"] # 打印消息信息 mes_name = chat.group_info.group_name if chat.group_info else "私聊" current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time)) willing_log = f"[回复意愿:{await willing_manager.get_willing(chat.stream_id):.2f}]" if is_willing else "" logger.info( f"[{current_time}][{mes_name}]" f"{message.message_info.user_info.user_nickname}:" f"{message.processed_plain_text}{willing_log}[概率:{reply_probability * 100:.1f}%]" ) do_reply = False if random() < reply_probability: do_reply = True # 回复前处理 await willing_manager.before_generate_reply_handle(message.message_info.message_id) # 创建思考消息 with Timer("创建思考消息", timing_results): thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo) logger.debug(f"创建捕捉器，thinking_id:{thinking_id}") info_catcher = info_catcher_manager.get_info_catcher(thinking_id) info_catcher.catch_decide_to_response(message) # 生成回复 try: with Timer("生成回复", timing_results): response_set = await self.gpt.generate_response(message, thinking_id) info_catcher.catch_after_generate_response(timing_results["生成回复"]) except Exception as e: logger.error(f"回复生成出现错误：{str(e)} {traceback.format_exc()}") response_set = None if not response_set: logger.info("为什么生成回复失败？") return # 发送消息 with Timer("发送消息", timing_results): first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id) info_catcher.catch_after_response(timing_results["发送消息"], response_set, first_bot_msg) info_catcher.done_catch() # 处理表情包 with Timer("处理表情包", timing_results): await self._handle_emoji(message, chat, response_set) # 更新关系情绪 with Timer("更新关系情绪", timing_results): await self._update_relationship(message, response_set) # 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: 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) if response_set else "无回复" logger.info(f"触发消息: {trigger_msg[:20]}... | 推理消息: {response_msg[:20]}... | 性能计时: {timing_str}") else: # 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 willing_manager.delete(message.message_info.message_id) @staticmethod def _check_ban_words(text: str, chat, userinfo) -> bool: """检查消息中是否包含过滤词""" for word in global_config.ban_words: if word in text: logger.info( f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}" ) logger.info(f"[过滤词识别]消息中含有{word}，filtered") return True return False @staticmethod def _check_ban_regex(text: str, chat, userinfo) -> bool: """检查消息是否匹配过滤正则表达式""" for pattern in global_config.ban_msgs_regex: if pattern.search(text): logger.info( f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}" ) logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered") return True return False