import re import time from random import random from ..memory_system.Hippocampus import HippocampusManager from ..moods.moods import MoodManager # 导入情绪管理器 from ..config.config import global_config from .emoji_manager import emoji_manager # 导入表情包管理器 from .llm_generator import ResponseGenerator from .message import MessageSending, MessageRecv, MessageThinking, MessageSet from .chat_stream import chat_manager from .message_sender import message_manager # 导入新的消息管理器 from .relationship_manager import relationship_manager from .storage import MessageStorage from .utils import is_mentioned_bot_in_message, get_recent_group_detailed_plain_text from .utils_image import image_path_to_base64 from ..willing.willing_manager import willing_manager # 导入意愿管理器 from ..message import UserInfo, Seg from src.heart_flow.heartflow import heartflow from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig # 定义日志配置 chat_config = LogConfig( # 使用消息发送专用样式 console_format=CHAT_STYLE_CONFIG["console_format"], file_format=CHAT_STYLE_CONFIG["file_format"], ) # 配置主程序日志格式 logger = get_module_logger("chat_bot", config=chat_config) class ChatBot: def __init__(self): self.storage = MessageStorage() self.gpt = ResponseGenerator() self.bot = None # bot 实例引用 self._started = False self.mood_manager = MoodManager.get_instance() # 获取情绪管理器单例 self.mood_manager.start_mood_update() # 启动情绪更新 async def _ensure_started(self): """确保所有任务已启动""" if not self._started: self._started = True async def message_process(self, message_data: str) -> None: """处理转化后的统一格式消息 1. 过滤消息 2. 记忆激活 3. 意愿激活 4. 生成回复并发送 5. 更新关系 6. 更新情绪 """ message = MessageRecv(message_data) groupinfo = message.message_info.group_info userinfo = message.message_info.user_info messageinfo = message.message_info # 消息过滤，涉及到config有待更新 # 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) # 创建 心流与chat的观察 heartflow.create_subheartflow(chat.stream_id) timer1 = time.time() await message.process() timer2 = time.time() logger.debug(f"2消息处理时间: {timer2 - timer1}秒") # 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 await self.storage.store_message(message, chat) timer1 = time.time() interested_rate = 0 interested_rate = await HippocampusManager.get_instance().get_activate_from_text( message.processed_plain_text, fast_retrieval=True ) timer2 = time.time() logger.debug(f"3记忆激活时间: {timer2 - timer1}秒") is_mentioned = is_mentioned_bot_in_message(message) if global_config.enable_think_flow: current_willing_old = willing_manager.get_willing(chat_stream=chat) current_willing_new = (heartflow.get_subheartflow(chat.stream_id).current_state.willing - 5) / 4 print(f"旧回复意愿：{current_willing_old}，新回复意愿：{current_willing_new}") current_willing = (current_willing_old + current_willing_new) / 2 else: current_willing = willing_manager.get_willing(chat_stream=chat) willing_manager.set_willing(chat.stream_id, current_willing) timer1 = time.time() reply_probability = await willing_manager.change_reply_willing_received( chat_stream=chat, is_mentioned_bot=is_mentioned, config=global_config, is_emoji=message.is_emoji, interested_rate=interested_rate, sender_id=str(message.message_info.user_info.user_id), ) timer2 = time.time() logger.debug(f"4计算意愿激活时间: {timer2 - timer1}秒") # 神秘的消息流数据结构处理 if chat.group_info: if chat.group_info.group_name: mes_name_dict = chat.group_info.group_name mes_name = mes_name_dict.get("group_name", "无名群聊") else: mes_name = "群聊" else: mes_name = "私聊" # 打印收到的信息的信息 current_time = time.strftime("%H:%M:%S", time.localtime(messageinfo.time)) logger.info( f"[{current_time}][{mes_name}]" f"{chat.user_info.user_nickname}:" f"{message.processed_plain_text}[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]" ) 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"] # 开始组织语言 if random() < reply_probability: timer1 = time.time() response_set, thinking_id = await self._generate_response_from_message(message, chat, userinfo, messageinfo) timer2 = time.time() logger.info(f"5生成回复时间: {timer2 - timer1}秒") if not response_set: logger.info("为什么生成回复失败？") return # 发送消息 timer1 = time.time() await self._send_response_messages(message, chat, response_set, thinking_id) timer2 = time.time() logger.info(f"7发送消息时间: {timer2 - timer1}秒") # 处理表情包 timer1 = time.time() await self._handle_emoji(message, chat, response_set) timer2 = time.time() logger.debug(f"8处理表情包时间: {timer2 - timer1}秒") timer1 = time.time() await self._update_using_response(message, chat, response_set) timer2 = time.time() logger.info(f"6更新htfl时间: {timer2 - timer1}秒") # 更新情绪和关系 # await self._update_emotion_and_relationship(message, chat, response_set) async def _generate_response_from_message(self, message, chat, userinfo, messageinfo): """生成回复内容 Args: message: 接收到的消息 chat: 聊天流对象 userinfo: 用户信息对象 messageinfo: 消息信息对象 Returns: tuple: (response, raw_content) 回复内容和原始内容 """ 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) willing_manager.change_reply_willing_sent(chat) response_set = await self.gpt.generate_response(message) return response_set, thinking_id async def _update_using_response(self, message, response_set): # 更新心流状态 stream_id = message.chat_stream.stream_id chat_talking_prompt = "" if stream_id: chat_talking_prompt = get_recent_group_detailed_plain_text( stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True ) heartflow.get_subheartflow(stream_id).do_after_reply(response_set, chat_talking_prompt) async def _send_response_messages(self, message, chat, response_set, thinking_id): container = message_manager.get_container(chat.stream_id) thinking_message = None # logger.info(f"开始发送消息准备") 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 # logger.info(f"开始发送消息") thinking_start_time = thinking_message.thinking_start_time message_set = MessageSet(chat, thinking_id) mark_head = False 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 message_set.add_message(bot_message) # logger.info(f"开始添加发送消息") message_manager.add_message(message_set) async def _handle_emoji(self, message, chat, response): """处理表情包 Args: message: 接收到的消息 chat: 聊天流对象 response: 生成的回复 """ 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_emotion_and_relationship(self, message, chat, response, raw_content): """更新情绪和关系 Args: message: 接收到的消息 chat: 聊天流对象 response: 生成的回复 raw_content: 原始内容 """ stance, emotion = await self.gpt._get_emotion_tags(raw_content, message.processed_plain_text) logger.debug(f"为 '{response}' 立场为：{stance} 获取到的情感标签为：{emotion}") await relationship_manager.calculate_update_relationship_value(chat_stream=chat, label=emotion, stance=stance) self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor) def _check_ban_words(self, text: str, chat, userinfo) -> bool: """检查消息中是否包含过滤词 Args: text: 要检查的文本 chat: 聊天流对象 userinfo: 用户信息对象 Returns: bool: 如果包含过滤词返回True，否则返回False """ 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 def _check_ban_regex(self, text: str, chat, userinfo) -> bool: """检查消息是否匹配过滤正则表达式 Args: text: 要检查的文本 chat: 聊天流对象 userinfo: 用户信息对象 Returns: bool: 如果匹配过滤正则返回True，否则返回False """ for pattern in global_config.ban_msgs_regex: if re.search(pattern, 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 # 创建全局ChatBot实例 chat_bot = ChatBot()