mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'main-fix' of https://github.com/Dax233/MaiMBot into main-fix2
Bakadax
commit
ae210d86ba
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@ -81,7 +81,7 @@ MEMORY_STYLE_CONFIG = {
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"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 海马体 | {message}"),
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},
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"simple": {
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"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-yellow>海马体</light-yellow> | {message}"),
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"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-yellow>海马体</light-yellow> | <light-yellow>{message}</light-yellow>"),
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"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 海马体 | {message}"),
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},
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}
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@ -240,7 +240,7 @@ SUB_HEARTFLOW_STYLE_CONFIG = {
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"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦小脑袋 | {message}"),
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},
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"simple": {
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"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-blue>麦麦小脑袋</light-blue> | <green>{message}</green>"), # noqa: E501
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"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-blue>麦麦小脑袋</light-blue> | <light-blue>{message}</light-blue>"), # noqa: E501
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"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦小脑袋 | {message}"),
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},
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}
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@ -14,6 +14,7 @@ from .emoji_manager import emoji_manager
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from .relationship_manager import relationship_manager
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from ..willing.willing_manager import willing_manager
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from .chat_stream import chat_manager
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from .auto_speak import auto_speak_manager # 导入自动发言管理器
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# from ..memory_system.memory import hippocampus
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from src.plugins.memory_system.Hippocampus import HippocampusManager
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from .message_sender import message_manager, message_sender
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@ -94,6 +95,10 @@ async def start_background_tasks():
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logger.success("启动测试功能：心流系统")
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await start_think_flow()
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# 启动自动发言检查任务
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# await auto_speak_manager.start_auto_speak_check()
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# logger.success("自动发言检查任务启动成功")
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# 只启动表情包管理任务
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asyncio.create_task(emoji_manager.start_periodic_check())
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@ -0,0 +1,172 @@
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import time
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import asyncio
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import random
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from random import random as random_float
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from typing import Dict
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from ..config.config import global_config
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from .message import MessageSending, MessageThinking, MessageSet, MessageRecv
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from .message_base import UserInfo, Seg
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from .message_sender import message_manager
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from ..moods.moods import MoodManager
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from .llm_generator import ResponseGenerator
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from src.common.logger import get_module_logger
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from src.think_flow_demo.heartflow import subheartflow_manager
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from ...common.database import db
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logger = get_module_logger("auto_speak")
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class AutoSpeakManager:
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def __init__(self):
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self._last_auto_speak_time: Dict[str, float] = {} # 记录每个聊天流上次自主发言的时间
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self.mood_manager = MoodManager.get_instance()
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self.gpt = ResponseGenerator() # 添加gpt实例
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self._started = False
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self._check_task = None
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self.db = db
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async def get_chat_info(self, chat_id: str) -> dict:
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"""从数据库获取聊天流信息"""
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chat_info = await self.db.chat_streams.find_one({"stream_id": chat_id})
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return chat_info
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async def start_auto_speak_check(self):
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"""启动自动发言检查任务"""
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if not self._started:
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self._check_task = asyncio.create_task(self._periodic_check())
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self._started = True
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logger.success("自动发言检查任务已启动")
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async def _periodic_check(self):
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"""定期检查是否需要自主发言"""
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while True and global_config.enable_think_flow:
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# 获取所有活跃的子心流
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active_subheartflows = []
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for chat_id, subheartflow in subheartflow_manager._subheartflows.items():
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if subheartflow.is_active and subheartflow.current_state.willing > 0: # 只考虑活跃且意愿值大于0.5的子心流
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active_subheartflows.append((chat_id, subheartflow))
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logger.debug(f"发现活跃子心流 - 聊天ID: {chat_id}, 意愿值: {subheartflow.current_state.willing:.2f}")
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if not active_subheartflows:
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logger.debug("当前没有活跃的子心流")
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await asyncio.sleep(20) # 添加异步等待
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continue
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# 随机选择一个活跃的子心流
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chat_id, subheartflow = random.choice(active_subheartflows)
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logger.info(f"随机选择子心流 - 聊天ID: {chat_id}, 意愿值: {subheartflow.current_state.willing:.2f}")
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# 检查是否应该自主发言
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if await self.check_auto_speak(subheartflow):
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logger.info(f"准备自主发言 - 聊天ID: {chat_id}")
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# 生成自主发言
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bot_user_info = UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform="qq", # 默认使用qq平台
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)
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# 创建一个空的MessageRecv对象作为上下文
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message = MessageRecv({
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"message_info": {
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"user_info": {
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"user_id": chat_id,
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"user_nickname": "",
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"platform": "qq"
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},
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"group_info": None,
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"platform": "qq",
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"time": time.time()
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},
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"processed_plain_text": "",
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"raw_message": "",
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"is_emoji": False
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})
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await self.generate_auto_speak(subheartflow, message, bot_user_info, message.message_info["user_info"], message.message_info)
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else:
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logger.debug(f"不满足自主发言条件 - 聊天ID: {chat_id}")
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# 每分钟检查一次
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await asyncio.sleep(20)
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# await asyncio.sleep(5) # 发生错误时等待5秒再继续
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async def check_auto_speak(self, subheartflow) -> bool:
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"""检查是否应该自主发言"""
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if not subheartflow:
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return False
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current_time = time.time()
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chat_id = subheartflow.observe_chat_id
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# 获取上次自主发言时间
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if chat_id not in self._last_auto_speak_time:
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self._last_auto_speak_time[chat_id] = 0
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last_speak_time = self._last_auto_speak_time.get(chat_id, 0)
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# 如果距离上次自主发言不到5分钟，不发言
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if current_time - last_speak_time < 30:
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logger.debug(f"距离上次发言时间太短 - 聊天ID: {chat_id}, 剩余时间: {30 - (current_time - last_speak_time):.1f}秒")
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return False
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# 获取当前意愿值
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current_willing = subheartflow.current_state.willing
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if current_willing > 0.1 and random_float() < 0.5:
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self._last_auto_speak_time[chat_id] = current_time
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logger.info(f"满足自主发言条件 - 聊天ID: {chat_id}, 意愿值: {current_willing:.2f}")
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return True
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logger.debug(f"不满足自主发言条件 - 聊天ID: {chat_id}, 意愿值: {current_willing:.2f}")
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return False
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async def generate_auto_speak(self, subheartflow, message, bot_user_info: UserInfo, userinfo, messageinfo):
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"""生成自主发言内容"""
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thinking_time_point = round(time.time(), 2)
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think_id = "mt" + str(thinking_time_point)
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thinking_message = MessageThinking(
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message_id=think_id,
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chat_stream=None, # 不需要chat_stream
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bot_user_info=bot_user_info,
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reply=message,
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thinking_start_time=thinking_time_point,
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)
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message_manager.add_message(thinking_message)
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# 生成自主发言内容
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response, raw_content = await self.gpt.generate_response(message)
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if response:
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message_set = MessageSet(None, think_id) # 不需要chat_stream
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mark_head = False
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for msg in response:
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message_segment = Seg(type="text", data=msg)
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bot_message = MessageSending(
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message_id=think_id,
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chat_stream=None, # 不需要chat_stream
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bot_user_info=bot_user_info,
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sender_info=userinfo,
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message_segment=message_segment,
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reply=message,
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is_head=not mark_head,
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is_emoji=False,
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thinking_start_time=thinking_time_point,
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)
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if not mark_head:
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mark_head = True
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message_set.add_message(bot_message)
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message_manager.add_message(message_set)
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# 更新情绪和关系
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stance, emotion = await self.gpt._get_emotion_tags(raw_content, message.processed_plain_text)
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self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
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return True
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return False
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# 创建全局AutoSpeakManager实例
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auto_speak_manager = AutoSpeakManager()
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@ -129,13 +129,21 @@ class ChatBot:
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# 根据话题计算激活度
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topic = ""
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await self.storage.store_message(message, chat, topic[0] if topic else None)
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interested_rate = 0
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interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
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message.processed_plain_text,fast_retrieval=True)
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# interested_rate = 0.1
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# logger.info(f"对{message.processed_plain_text}的激活度:{interested_rate}")
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# logger.info(f"\033[1;32m[主题识别]\033[0m 使用{global_config.topic_extract}主题: {topic}")
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await self.storage.store_message(message, chat, topic[0] if topic else None)
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if global_config.enable_think_flow:
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current_willing_old = willing_manager.get_willing(chat_stream=chat)
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current_willing_new = (subheartflow_manager.get_subheartflow(chat.stream_id).current_state.willing-5)/4
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print(f"旧回复意愿：{current_willing_old}，新回复意愿：{current_willing_new}")
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current_willing = (current_willing_old + current_willing_new) / 2
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else:
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current_willing = willing_manager.get_willing(chat_stream=chat)
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willing_manager.set_willing(chat.stream_id,current_willing)
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# 处理提及
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if (f"[CQ:at,qq={global_config.BOT_QQ}" in message_cq.raw_message) and global_config.at_bot_inevitable_reply:
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@ -160,14 +168,6 @@ class ChatBot:
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sender_id=str(message.message_info.user_info.user_id),
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)
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if global_config.enable_think_flow:
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current_willing_old = willing_manager.get_willing(chat_stream=chat)
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current_willing_new = (subheartflow_manager.get_subheartflow(chat.stream_id).current_state.willing-5)/4
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print(f"旧回复意愿：{current_willing_old}，新回复意愿：{current_willing_new}")
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current_willing = (current_willing_old + current_willing_new) / 2
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else:
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current_willing = willing_manager.get_willing(chat_stream=chat)
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logger.info(
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f"[{current_time}][{chat.group_info.group_name if chat.group_info else '私聊'}]"
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f"{chat.user_info.user_nickname}:"
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@ -361,7 +361,8 @@ class ChatBot:
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platform="qq",
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)
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await self.message_process(message_cq)
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if random() < 0.1:
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await self.message_process(message_cq)
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elif isinstance(event, GroupRecallNoticeEvent) or isinstance(event, FriendRecallNoticeEvent):
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user_info = UserInfo(
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|
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@ -83,7 +83,7 @@ class PromptBuilder:
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text=message_txt,
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max_memory_num=3,
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max_memory_length=2,
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max_depth=3,
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max_depth=4,
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fast_retrieval=False
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)
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memory_str = ""
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|
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@ -1046,14 +1046,14 @@ class Hippocampus:
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# 将选中的节点添加到remember_map
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for node, normalized_activation in sorted_nodes:
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remember_map[node] = activate_map[node] # 使用原始激活值
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logger.info(
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logger.debug(
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f"节点 '{node}' (归一化激活值: {normalized_activation:.2f}, 激活值: {activate_map[node]:.2f})")
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else:
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logger.info("没有有效的激活值")
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# 从选中的节点中提取记忆
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all_memories = []
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logger.info("开始从选中的节点中提取记忆:")
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# logger.info("开始从选中的节点中提取记忆:")
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for node, activation in remember_map.items():
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logger.debug(f"处理节点 '{node}' (激活值: {activation:.2f}):")
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node_data = self.memory_graph.G.nodes[node]
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@ -1225,7 +1225,7 @@ class Hippocampus:
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total_nodes = len(self.memory_graph.G.nodes())
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# activated_nodes = len(activate_map)
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activation_ratio = total_activation / total_nodes if total_nodes > 0 else 0
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activation_ratio = activation_ratio*40
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activation_ratio = activation_ratio*60
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logger.info(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
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return activation_ratio
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|
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@ -1,70 +0,0 @@
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import asyncio
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import os
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import aiohttp
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from src.common.logger import get_module_logger
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logger = get_module_logger("offline_llm")
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class LLMModel:
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def __init__(self, model_name="deepseek-ai/DeepSeek-V3", **kwargs):
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self.model_name = model_name
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self.params = kwargs
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self.api_key = os.getenv("SILICONFLOW_KEY")
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self.base_url = os.getenv("SILICONFLOW_BASE_URL")
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if not self.api_key or not self.base_url:
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raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置")
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logger.info(f"API URL: {self.base_url}") # 使用 logger 记录 base_url
|
||||
|
||||
async def generate_response_async(self, prompt: str) -> str:
|
||||
"""异步方式根据输入的提示生成模型的响应"""
|
||||
headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
|
||||
|
||||
# 构建请求体
|
||||
data = {
|
||||
"model": self.model_name,
|
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"messages": [{"role": "user", "content": prompt}],
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||||
"temperature": 0.7,
|
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**self.params,
|
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}
|
||||
|
||||
# 发送请求到完整的 chat/completions 端点
|
||||
api_url = f"{self.base_url.rstrip('/')}/chat/completions"
|
||||
logger.info(f"Request URL: {api_url}") # 记录请求的 URL
|
||||
|
||||
max_retries = 3
|
||||
base_wait_time = 15
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
for retry in range(max_retries):
|
||||
try:
|
||||
async with session.post(api_url, headers=headers, json=data) as response:
|
||||
if response.status == 429:
|
||||
wait_time = base_wait_time * (2**retry) # 指数退避
|
||||
logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
|
||||
await asyncio.sleep(wait_time)
|
||||
continue
|
||||
|
||||
response.raise_for_status() # 检查其他响应状态
|
||||
|
||||
result = await response.json()
|
||||
if "choices" in result and len(result["choices"]) > 0:
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
|
||||
return content, reasoning_content
|
||||
return "没有返回结果", ""
|
||||
|
||||
except Exception as e:
|
||||
if retry < max_retries - 1: # 如果还有重试机会
|
||||
wait_time = base_wait_time * (2**retry)
|
||||
logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
|
||||
await asyncio.sleep(wait_time)
|
||||
else:
|
||||
logger.error(f"请求失败: {str(e)}")
|
||||
return f"请求失败: {str(e)}", ""
|
||||
|
||||
logger.error("达到最大重试次数，请求仍然失败")
|
||||
return "达到最大重试次数，请求仍然失败", ""
|
||||
|
|
@ -68,7 +68,7 @@ class ScheduleGenerator:
|
|||
self.print_schedule()
|
||||
|
||||
while True:
|
||||
print(self.get_current_num_task(1, True))
|
||||
# print(self.get_current_num_task(1, True))
|
||||
|
||||
current_time = datetime.datetime.now()
|
||||
|
||||
|
|
|
|||
|
|
@ -44,13 +44,19 @@ class LLMStatistics:
|
|||
|
||||
def _record_online_time(self):
|
||||
"""记录在线时间"""
|
||||
try:
|
||||
current_time = datetime.now()
|
||||
# 检查5分钟内是否已有记录
|
||||
recent_record = db.online_time.find_one({
|
||||
"timestamp": {
|
||||
"$gte": current_time - timedelta(minutes=5)
|
||||
}
|
||||
})
|
||||
|
||||
if not recent_record:
|
||||
db.online_time.insert_one({
|
||||
"timestamp": datetime.now(),
|
||||
"timestamp": current_time,
|
||||
"duration": 5 # 5分钟
|
||||
})
|
||||
except Exception:
|
||||
logger.exception("记录在线时间失败")
|
||||
|
||||
def _collect_statistics_for_period(self, start_time: datetime) -> Dict[str, Any]:
|
||||
"""收集指定时间段的LLM请求统计数据
|
||||
|
|
@ -217,7 +223,7 @@ class LLMStatistics:
|
|||
logger.exception("统计数据处理失败")
|
||||
|
||||
# 等待5分钟
|
||||
for _ in range(300): # 5分钟 = 300秒
|
||||
for _ in range(30): # 5分钟 = 300秒
|
||||
if not self.running:
|
||||
break
|
||||
time.sleep(1)
|
||||
|
|
|
|||
|
|
@ -35,6 +35,7 @@ class Heartflow:
|
|||
self._subheartflows = {}
|
||||
self.active_subheartflows_nums = 0
|
||||
|
||||
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||
|
||||
|
||||
async def heartflow_start_working(self):
|
||||
|
|
@ -46,13 +47,13 @@ class Heartflow:
|
|||
logger.info("麦麦大脑袋转起来了")
|
||||
self.current_state.update_current_state_info()
|
||||
|
||||
personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||
personality_info = self.personality_info
|
||||
current_thinking_info = self.current_mind
|
||||
mood_info = self.current_state.mood
|
||||
related_memory_info = 'memory'
|
||||
sub_flows_info = await self.get_all_subheartflows_minds()
|
||||
|
||||
schedule_info = bot_schedule.get_current_num_task(num = 5,time_info = True)
|
||||
schedule_info = bot_schedule.get_current_num_task(num = 4,time_info = True)
|
||||
|
||||
prompt = ""
|
||||
prompt += f"你刚刚在做的事情是：{schedule_info}\n"
|
||||
|
|
@ -91,7 +92,7 @@ class Heartflow:
|
|||
return await self.minds_summary(sub_minds)
|
||||
|
||||
async def minds_summary(self,minds_str):
|
||||
personality_info = " ".join(BotConfig.PROMPT_PERSONALITY)
|
||||
personality_info = self.personality_info
|
||||
mood_info = self.current_state.mood
|
||||
|
||||
prompt = ""
|
||||
|
|
|
|||
|
|
@ -49,6 +49,8 @@ class SubHeartflow:
|
|||
|
||||
self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
|
||||
|
||||
self.is_active = False
|
||||
|
||||
def assign_observe(self,stream_id):
|
||||
self.outer_world = outer_world.get_world_by_stream_id(stream_id)
|
||||
self.observe_chat_id = stream_id
|
||||
|
|
@ -58,8 +60,10 @@ class SubHeartflow:
|
|||
current_time = time.time()
|
||||
if current_time - self.last_reply_time > 180: # 3分钟 = 180秒
|
||||
# print(f"{self.observe_chat_id}麦麦已经3分钟没有回复了，暂时停止思考")
|
||||
self.is_active = False
|
||||
await asyncio.sleep(60) # 每30秒检查一次
|
||||
else:
|
||||
self.is_active = True
|
||||
await self.do_a_thinking()
|
||||
await self.judge_willing()
|
||||
await asyncio.sleep(60)
|
||||
|
|
@ -75,7 +79,7 @@ class SubHeartflow:
|
|||
|
||||
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||
text=message_stream_info,
|
||||
max_memory_num=3,
|
||||
max_memory_num=2,
|
||||
max_memory_length=2,
|
||||
max_depth=3,
|
||||
fast_retrieval=False
|
||||
|
|
@ -88,7 +92,7 @@ class SubHeartflow:
|
|||
else:
|
||||
related_memory_info = ''
|
||||
|
||||
print(f"相关记忆：{related_memory_info}")
|
||||
# print(f"相关记忆：{related_memory_info}")
|
||||
|
||||
schedule_info = bot_schedule.get_current_num_task(num = 1,time_info = False)
|
||||
|
||||
|
|
@ -96,10 +100,12 @@ class SubHeartflow:
|
|||
prompt += f"你刚刚在做的事情是：{schedule_info}\n"
|
||||
# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
|
||||
prompt += f"你{self.personality_info}\n"
|
||||
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{message_stream_info}\n"
|
||||
if related_memory_info:
|
||||
prompt += f"你想起来{related_memory_info}。"
|
||||
prompt += f"刚刚你的想法是{current_thinking_info}。"
|
||||
prompt += f"你想起来你之前见过的回忆：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
|
||||
prompt += f"刚刚你的想法是{current_thinking_info}。\n"
|
||||
prompt += "-----------------------------------\n"
|
||||
if message_stream_info:
|
||||
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{message_stream_info}\n"
|
||||
prompt += f"你现在{mood_info}。\n"
|
||||
prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
|
||||
prompt += "但是记得结合上述的消息，要记得维持住你的人设，关注聊天和新内容，不要思考太多:"
|
||||
|
|
@ -108,7 +114,7 @@ class SubHeartflow:
|
|||
self.update_current_mind(reponse)
|
||||
|
||||
self.current_mind = reponse
|
||||
print(prompt)
|
||||
logger.info(f"prompt:\n{prompt}\n")
|
||||
logger.info(f"麦麦的脑内状态：{self.current_mind}")
|
||||
|
||||
async def do_after_reply(self,reply_content,chat_talking_prompt):
|
||||
|
|
@ -117,7 +123,7 @@ class SubHeartflow:
|
|||
|
||||
current_thinking_info = self.current_mind
|
||||
mood_info = self.current_state.mood
|
||||
related_memory_info = 'memory'
|
||||
# related_memory_info = 'memory'
|
||||
message_stream_info = self.outer_world.talking_summary
|
||||
message_new_info = chat_talking_prompt
|
||||
reply_info = reply_content
|
||||
|
|
@ -129,8 +135,8 @@ class SubHeartflow:
|
|||
prompt += f"你{self.personality_info}\n"
|
||||
|
||||
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{message_stream_info}\n"
|
||||
if related_memory_info:
|
||||
prompt += f"你想起来{related_memory_info}。"
|
||||
# if related_memory_info:
|
||||
# prompt += f"你想起来{related_memory_info}。"
|
||||
prompt += f"刚刚你的想法是{current_thinking_info}。"
|
||||
prompt += f"你现在看到了网友们发的新消息:{message_new_info}\n"
|
||||
prompt += f"你刚刚回复了群友们:{reply_info}"
|
||||
|
|
|
|||
Loading…
Reference in New Issue