Merge branch 'main-fix' of https://github.com/Dax233/MaiMBot into main-fix2

src/common/logger.py

@@ -81,7 +81,7 @@ MEMORY_STYLE_CONFIG = {
         "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 海马体 | {message}"),
     },
     "simple": {
-        "console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-yellow>海马体</light-yellow> | {message}"),
+        "console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-yellow>海马体</light-yellow> | <light-yellow>{message}</light-yellow>"),
         "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 海马体 | {message}"),
     },
 }
@@ -240,7 +240,7 @@ SUB_HEARTFLOW_STYLE_CONFIG = {
         "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦小脑袋 | {message}"),
     },
     "simple": {
-        "console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-blue>麦麦小脑袋</light-blue> | <green>{message}</green>"),  # noqa: E501
+        "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
         "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦小脑袋 | {message}"),
     },
 }

src/plugins/chat/__init__.py

@@ -14,6 +14,7 @@ from .emoji_manager import emoji_manager
 from .relationship_manager import relationship_manager
 from ..willing.willing_manager import willing_manager
 from .chat_stream import chat_manager
+from .auto_speak import auto_speak_manager  # 导入自动发言管理器
 # from ..memory_system.memory import hippocampus
 from src.plugins.memory_system.Hippocampus import HippocampusManager
 from .message_sender import message_manager, message_sender
@@ -94,6 +95,10 @@ async def start_background_tasks():
         logger.success("启动测试功能：心流系统")
         await start_think_flow()
 
+    # 启动自动发言检查任务
+    # await auto_speak_manager.start_auto_speak_check()
+    # logger.success("自动发言检查任务启动成功")
+
     # 只启动表情包管理任务
     asyncio.create_task(emoji_manager.start_periodic_check())
     

src/plugins/chat/auto_speak.py

@@ -0,0 +1,172 @@
+import time
+import asyncio
+import random
+from random import random as random_float
+from typing import Dict
+from ..config.config import global_config
+from .message import MessageSending, MessageThinking, MessageSet, MessageRecv
+from .message_base import UserInfo, Seg
+from .message_sender import message_manager
+from ..moods.moods import MoodManager
+from .llm_generator import ResponseGenerator
+from src.common.logger import get_module_logger
+from src.think_flow_demo.heartflow import subheartflow_manager
+from ...common.database import db
+logger = get_module_logger("auto_speak")
+
+class AutoSpeakManager:
+    def __init__(self):
+        self._last_auto_speak_time: Dict[str, float] = {}  # 记录每个聊天流上次自主发言的时间
+        self.mood_manager = MoodManager.get_instance()
+        self.gpt = ResponseGenerator()  # 添加gpt实例
+        self._started = False
+        self._check_task = None
+        self.db = db
+
+    async def get_chat_info(self, chat_id: str) -> dict:
+        """从数据库获取聊天流信息"""
+        chat_info = await self.db.chat_streams.find_one({"stream_id": chat_id})
+        return chat_info
+
+    async def start_auto_speak_check(self):
+        """启动自动发言检查任务"""
+        if not self._started:
+            self._check_task = asyncio.create_task(self._periodic_check())
+            self._started = True
+            logger.success("自动发言检查任务已启动")
+
+    async def _periodic_check(self):
+        """定期检查是否需要自主发言"""
+        while True and global_config.enable_think_flow:
+            
+            # 获取所有活跃的子心流
+            active_subheartflows = []
+            for chat_id, subheartflow in subheartflow_manager._subheartflows.items():
+                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
+                    active_subheartflows.append((chat_id, subheartflow))
+                    logger.debug(f"发现活跃子心流 - 聊天ID: {chat_id}, 意愿值: {subheartflow.current_state.willing:.2f}")
+
+            if not active_subheartflows:
+                logger.debug("当前没有活跃的子心流")
+                await asyncio.sleep(20)  # 添加异步等待
+                continue
+
+            # 随机选择一个活跃的子心流
+            chat_id, subheartflow = random.choice(active_subheartflows)
+            logger.info(f"随机选择子心流 - 聊天ID: {chat_id}, 意愿值: {subheartflow.current_state.willing:.2f}")
+            
+            # 检查是否应该自主发言
+            if await self.check_auto_speak(subheartflow):
+                logger.info(f"准备自主发言 - 聊天ID: {chat_id}")
+                # 生成自主发言
+                bot_user_info = UserInfo(
+                    user_id=global_config.BOT_QQ,
+                    user_nickname=global_config.BOT_NICKNAME,
+                    platform="qq",  # 默认使用qq平台
+                )
+                
+                # 创建一个空的MessageRecv对象作为上下文
+                message = MessageRecv({
+                    "message_info": {
+                        "user_info": {
+                            "user_id": chat_id,
+                            "user_nickname": "",
+                            "platform": "qq"
+                        },
+                        "group_info": None,
+                        "platform": "qq",
+                        "time": time.time()
+                    },
+                    "processed_plain_text": "",
+                    "raw_message": "",
+                    "is_emoji": False
+                })
+                
+                await self.generate_auto_speak(subheartflow, message, bot_user_info, message.message_info["user_info"], message.message_info)
+            else:
+                logger.debug(f"不满足自主发言条件 - 聊天ID: {chat_id}")
+            
+            # 每分钟检查一次
+            await asyncio.sleep(20)
+        
+            # await asyncio.sleep(5)  # 发生错误时等待5秒再继续
+
+    async def check_auto_speak(self, subheartflow) -> bool:
+        """检查是否应该自主发言"""
+        if not subheartflow:
+            return False
+            
+        current_time = time.time()
+        chat_id = subheartflow.observe_chat_id
+        
+        # 获取上次自主发言时间
+        if chat_id not in self._last_auto_speak_time:
+            self._last_auto_speak_time[chat_id] = 0 
+        last_speak_time = self._last_auto_speak_time.get(chat_id, 0)
+        
+        # 如果距离上次自主发言不到5分钟，不发言
+        if current_time - last_speak_time < 30:
+            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")
+            return False
+            
+        # 获取当前意愿值
+        current_willing = subheartflow.current_state.willing
+        
+        if current_willing > 0.1 and random_float() < 0.5:
+            self._last_auto_speak_time[chat_id] = current_time
+            logger.info(f"满足自主发言条件 - 聊天ID: {chat_id}, 意愿值: {current_willing:.2f}")
+            return True
+            
+        logger.debug(f"不满足自主发言条件 - 聊天ID: {chat_id}, 意愿值: {current_willing:.2f}")
+        return False
+
+    async def generate_auto_speak(self, subheartflow, message, bot_user_info: UserInfo, userinfo, messageinfo):
+        """生成自主发言内容"""
+        thinking_time_point = round(time.time(), 2)
+        think_id = "mt" + str(thinking_time_point)
+        thinking_message = MessageThinking(
+            message_id=think_id,
+            chat_stream=None,  # 不需要chat_stream
+            bot_user_info=bot_user_info,
+            reply=message,
+            thinking_start_time=thinking_time_point,
+        )
+        
+        message_manager.add_message(thinking_message)
+        
+        # 生成自主发言内容
+        response, raw_content = await self.gpt.generate_response(message)
+        
+        if response:
+            message_set = MessageSet(None, think_id)  # 不需要chat_stream
+            mark_head = False
+            
+            for msg in response:
+                message_segment = Seg(type="text", data=msg)
+                bot_message = MessageSending(
+                    message_id=think_id,
+                    chat_stream=None,  # 不需要chat_stream
+                    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_time_point,
+                )
+                if not mark_head:
+                    mark_head = True
+                message_set.add_message(bot_message)
+            
+            message_manager.add_message(message_set)
+            
+            # 更新情绪和关系
+            stance, emotion = await self.gpt._get_emotion_tags(raw_content, message.processed_plain_text)
+            self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
+            
+            return True
+            
+        return False
+
+# 创建全局AutoSpeakManager实例
+auto_speak_manager = AutoSpeakManager() 

src/plugins/chat/bot.py

@@ -129,14 +129,22 @@ class ChatBot:
 
         # 根据话题计算激活度
         topic = ""
-        interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
-            message.processed_plain_text,fast_retrieval=True)
-        # interested_rate = 0.1
-        # logger.info(f"对{message.processed_plain_text}的激活度:{interested_rate}")
-        # logger.info(f"\033[1;32m[主题识别]\033[0m 使用{global_config.topic_extract}主题: {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)
+        
+        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)
+        
         # 处理提及
         if (f"[CQ:at,qq={global_config.BOT_QQ}" in message_cq.raw_message) and global_config.at_bot_inevitable_reply:
             reply_probability = 1
@@ -159,14 +167,6 @@ class ChatBot:
                     interested_rate=interested_rate,
                     sender_id=str(message.message_info.user_info.user_id),
                 )
-        
-        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)
 
         logger.info(
             f"[{current_time}][{chat.group_info.group_name if chat.group_info else '私聊'}]"
@@ -360,8 +360,9 @@ class ChatBot:
                 reply_message=None,
                 platform="qq",
             )
-
-            await self.message_process(message_cq)
+            
+            if random() < 0.1:
+                await self.message_process(message_cq)
 
         elif isinstance(event, GroupRecallNoticeEvent) or isinstance(event, FriendRecallNoticeEvent):
             user_info = UserInfo(

src/plugins/chat/prompt_builder.py

@@ -83,7 +83,7 @@ class PromptBuilder:
             text=message_txt,
             max_memory_num=3,
             max_memory_length=2,
-            max_depth=3,
+            max_depth=4,
             fast_retrieval=False
         )
         memory_str = ""

src/plugins/memory_system/Hippocampus.py

@@ -1046,14 +1046,14 @@ class Hippocampus:
             # 将选中的节点添加到remember_map
             for node, normalized_activation in sorted_nodes:
                 remember_map[node] = activate_map[node]  # 使用原始激活值
-                logger.info(
+                logger.debug(
                     f"节点 '{node}' (归一化激活值: {normalized_activation:.2f}, 激活值: {activate_map[node]:.2f})")
         else:
             logger.info("没有有效的激活值")
 
         # 从选中的节点中提取记忆
         all_memories = []
-        logger.info("开始从选中的节点中提取记忆:")
+        # logger.info("开始从选中的节点中提取记忆:")
         for node, activation in remember_map.items():
             logger.debug(f"处理节点 '{node}' (激活值: {activation:.2f}):")
             node_data = self.memory_graph.G.nodes[node]
@@ -1225,7 +1225,7 @@ class Hippocampus:
         total_nodes = len(self.memory_graph.G.nodes())
         # activated_nodes = len(activate_map)
         activation_ratio = total_activation / total_nodes if total_nodes > 0 else 0
-        activation_ratio = activation_ratio*40
+        activation_ratio = activation_ratio*60
         logger.info(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
         
         return activation_ratio

src/plugins/schedule/offline_llm.py

@@ -1,70 +0,0 @@
-import asyncio
-import os
-
-import aiohttp
-from src.common.logger import get_module_logger
-
-logger = get_module_logger("offline_llm")
-
-
-class LLMModel:
-    def __init__(self, model_name="deepseek-ai/DeepSeek-V3", **kwargs):
-        self.model_name = model_name
-        self.params = kwargs
-        self.api_key = os.getenv("SILICONFLOW_KEY")
-        self.base_url = os.getenv("SILICONFLOW_BASE_URL")
-
-        if not self.api_key or not self.base_url:
-            raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置")
-
-        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,
-            "messages": [{"role": "user", "content": prompt}],
-            "temperature": 0.7,
-            **self.params,
-        }
-
-        # 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", ""

src/plugins/schedule/schedule_generator.py

@@ -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()
                 

src/plugins/utils/statistic.py

@@ -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)

src/think_flow_demo/heartflow.py

@@ -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 = ""

src/think_flow_demo/sub_heartflow.py

@@ -48,6 +48,8 @@ class SubHeartflow:
             self.current_mind = "你什么也没想"
             
         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)
@@ -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}"