import re import time from random import random import json 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, GroupInfo, Seg from src.think_flow_demo.heartflow import subheartflow_manager from src.think_flow_demo.outer_world import outer_world 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_json = json.loads(message_data) # 哦我嘞个json # 进入maimbot 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, # 我嘞个gourp_info ) message.update_chat_stream(chat) # 创建 心流 观察 if global_config.enable_think_flow: await outer_world.check_and_add_new_observe() subheartflow_manager.create_subheartflow(chat.stream_id) await relationship_manager.update_relationship( chat_stream=chat, ) await relationship_manager.update_relationship_value(chat_stream=chat, relationship_value=0) await message.process() # 过滤词 for word in global_config.ban_words: if word in message.processed_plain_text: logger.info( f"[{chat.group_info.group_name if chat.group_info else '私聊'}]" f"{userinfo.user_nickname}:{message.processed_plain_text}" ) logger.info(f"[过滤词识别]消息中含有{word}，filtered") return # 正则表达式过滤 for pattern in global_config.ban_msgs_regex: if re.search(pattern, message.raw_message): logger.info( f"[{chat.group_info.group_name if chat.group_info else '私聊'}]" f"{userinfo.user_nickname}:{message.raw_message}" ) logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered") return current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(messageinfo.time)) # 根据话题计算激活度 topic = "" await self.storage.store_message(message, chat, topic[0] if topic else None) interested_rate = 0 interested_rate = await HippocampusManager.get_instance().get_activate_from_text( message.processed_plain_text, fast_retrieval=True ) 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 = (subheartflow_manager.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) 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), ) logger.info( f"[{current_time}][{chat.group_info.group_name if chat.group_info else '私聊'}]" f"{chat.user_info.user_nickname}:" f"{message.processed_plain_text}[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]" ) response = None 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: bot_user_info = UserInfo( user_id=global_config.BOT_QQ, user_nickname=global_config.BOT_NICKNAME, platform=messageinfo.platform, ) # 开始思考的时间点 thinking_time_point = round(time.time(), 2) # logger.debug(f"开始思考的时间点: {thinking_time_point}") think_id = "mt" + str(thinking_time_point) thinking_message = MessageThinking( message_id=think_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, raw_content = await self.gpt.generate_response(message) else: # 决定不回复时，也更新回复意愿 willing_manager.change_reply_willing_not_sent(chat) # print(f"response: {response}") if response: 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 ) if subheartflow_manager.get_subheartflow(stream_id): await subheartflow_manager.get_subheartflow(stream_id).do_after_reply(response, chat_talking_prompt) else: await subheartflow_manager.create_subheartflow(stream_id).do_after_reply(response, chat_talking_prompt) # print(f"有response: {response}") container = message_manager.get_container(chat.stream_id) thinking_message = None # 找到message,删除 # print(f"开始找思考消息") for msg in container.messages: if isinstance(msg, MessageThinking) and msg.message_info.message_id == think_id: # print(f"找到思考消息: {msg}") thinking_message = msg container.messages.remove(msg) break # 如果找不到思考消息，直接返回 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 # 记录开始思考的时间，避免从思考到回复的时间太久 thinking_start_time = thinking_message.thinking_start_time message_set = MessageSet(chat, think_id) # 计算打字时间，1是为了模拟打字，2是避免多条回复乱序 # accu_typing_time = 0 mark_head = False for msg in response: # print(f"\033[1;32m[回复内容]\033[0m {msg}") # 通过时间改变时间戳 # typing_time = calculate_typing_time(msg) # logger.debug(f"typing_time: {typing_time}") # accu_typing_time += typing_time # timepoint = thinking_time_point + accu_typing_time message_segment = Seg(type="text", data=msg) # logger.debug(f"message_segment: {message_segment}") bot_message = MessageSending( message_id=think_id, chat_stream=chat, bot_user_info=bot_user_info, sender_info=userinfo, 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) if len(str(bot_message)) < 1000: logger.debug(f"bot_message: {bot_message}") logger.debug(f"添加消息到message_set: {bot_message}") else: logger.debug(f"bot_message: {str(bot_message)[:1000]}...{str(bot_message)[-10:]}") logger.debug(f"添加消息到message_set: {str(bot_message)[:1000]}...{str(bot_message)[-10:]}") # message_set 可以直接加入 message_manager # print(f"\033[1;32m[回复]\033[0m 将回复载入发送容器") logger.debug("添加message_set到message_manager") message_manager.add_message(message_set) bot_response_time = thinking_time_point if random() < global_config.emoji_chance: emoji_raw = await emoji_manager.get_emoji_for_text(response) # 检查是否 <没有找到> emoji if emoji_raw != None: emoji_path, description = emoji_raw emoji_cq = image_path_to_base64(emoji_path) if random() < 0.5: bot_response_time = thinking_time_point - 1 else: bot_response_time = bot_response_time + 1 message_segment = Seg(type="emoji", data=emoji_cq) bot_message = MessageSending( message_id=think_id, chat_stream=chat, bot_user_info=bot_user_info, sender_info=userinfo, message_segment=message_segment, reply=message, is_head=False, is_emoji=True, ) message_manager.add_message(bot_message) # 获取立场和情感标签，更新关系值 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 ) # 使用情绪管理器更新情绪 self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor) # willing_manager.change_reply_willing_after_sent( # chat_stream=chat # ) # 创建全局ChatBot实例 chat_bot = ChatBot()