fix：优化激活值，优化logger显示

src/common/logger.py

@@ -228,6 +228,26 @@ CHAT_STYLE_CONFIG = {
     },
 }
 
+SUB_HEARTFLOW_STYLE_CONFIG = {
+    "advanced": {
+        "console_format": (
+            "<green>{time:YYYY-MM-DD HH:mm:ss}</green> | "
+            "<level>{level: <8}</level> | "
+            "<cyan>{extra[module]: <12}</cyan> | "
+            "<light-blue>麦麦小脑袋</light-blue> | "
+            "<level>{message}</level>"
+        ),
+        "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
+        "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦小脑袋 | {message}"),
+    },
+}
+
+
+
+
 # 根据SIMPLE_OUTPUT选择配置
 MEMORY_STYLE_CONFIG = MEMORY_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MEMORY_STYLE_CONFIG["advanced"]
 TOPIC_STYLE_CONFIG = TOPIC_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else TOPIC_STYLE_CONFIG["advanced"]  
@@ -238,6 +258,7 @@ MOOD_STYLE_CONFIG = MOOD_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MOOD_STYLE
 RELATION_STYLE_CONFIG = RELATION_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else RELATION_STYLE_CONFIG["advanced"]
 SCHEDULE_STYLE_CONFIG = SCHEDULE_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SCHEDULE_STYLE_CONFIG["advanced"]
 HEARTFLOW_STYLE_CONFIG = HEARTFLOW_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else HEARTFLOW_STYLE_CONFIG["advanced"]
+SUB_HEARTFLOW_STYLE_CONFIG = SUB_HEARTFLOW_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SUB_HEARTFLOW_STYLE_CONFIG["advanced"]  # noqa: E501
 
 def is_registered_module(record: dict) -> bool:
     """检查是否为已注册的模块"""

src/plugins/chat/bot.py

@@ -130,9 +130,9 @@ 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)*300
+            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"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)

src/plugins/chat/prompt_builder.py

@@ -81,15 +81,15 @@ class PromptBuilder:
         # 调用 hippocampus 的 get_relevant_memories 方法
         relevant_memories = await HippocampusManager.get_instance().get_memory_from_text(
             text=message_txt,
-            max_memory_num=4,
+            max_memory_num=3,
             max_memory_length=2,
             max_depth=3,
-            fast_retrieval=True
+            fast_retrieval=False
         )
         memory_str = ""
         for _topic, memories in relevant_memories:
             memory_str += f"{memories}\n"
-        print(f"memory_str: {memory_str}")
+        # print(f"memory_str: {memory_str}")
 
         if relevant_memories:
             # 格式化记忆内容

src/plugins/memory_system/Hippocampus.py

@@ -817,8 +817,8 @@ class Hippocampus:
         self.parahippocampal_gyrus = ParahippocampalGyrus(self)
         # 从数据库加载记忆图
         self.entorhinal_cortex.sync_memory_from_db()
-        self.llm_topic_judge = LLM_request(self.config.llm_topic_judge)
+        self.llm_topic_judge = LLM_request(self.config.llm_topic_judge,request_type="memory")
-        self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic)
+        self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic,request_type="memory")
 
     def get_all_node_names(self) -> list:
         """获取记忆图中所有节点的名字列表"""
@@ -950,7 +950,7 @@ class Hippocampus:
             # 提取关键词
             keywords = re.findall(r'<([^>]+)>', topics_response[0])
             if not keywords:
-                keywords = ['none']
+                keywords = []
             else:
                 keywords = [
                     keyword.strip()
@@ -1025,7 +1025,7 @@ class Hippocampus:
 
         # 基于激活值平方的独立概率选择
         remember_map = {}
-        logger.info("基于激活值平方的归一化选择:")
+        # logger.info("基于激活值平方的归一化选择:")
         
         # 计算所有激活值的平方和
         total_squared_activation = sum(activation ** 2 for activation in activate_map.values())
@@ -1080,11 +1080,10 @@ class Hippocampus:
                 # 获取最匹配的记忆
                 top_memories = memory_similarities[:max_memory_length]
                 
-                
                 # 添加到结果中
                 for memory, similarity in top_memories:
                     all_memories.append((node, [memory], similarity))
-                    logger.info(f"选中记忆: {memory} (相似度: {similarity:.2f})")
+                    # logger.info(f"选中记忆: {memory} (相似度: {similarity:.2f})")
             else:
                 logger.info("节点没有记忆")
 
@@ -1148,7 +1147,7 @@ class Hippocampus:
             # 提取关键词
             keywords = re.findall(r'<([^>]+)>', topics_response[0])
             if not keywords:
-                keywords = ['none']
+                keywords = []
             else:
                 keywords = [
                     keyword.strip()
@@ -1221,10 +1220,13 @@ class Hippocampus:
         #     logger.info(f"节点 '{node}': 累计激活值 = {total_activation:.2f}")
         
         # 计算激活节点数与总节点数的比值
+        total_activation = sum(activate_map.values())
+        logger.info(f"总激活值: {total_activation:.2f}")
         total_nodes = len(self.memory_graph.G.nodes())
-        activated_nodes = len(activate_map)
+        # activated_nodes = len(activate_map)
-        activation_ratio = activated_nodes / total_nodes if total_nodes > 0 else 0
+        activation_ratio = total_activation / total_nodes if total_nodes > 0 else 0
-        logger.info(f"激活节点数: {activated_nodes}, 总节点数: {total_nodes}, 激活比例: {activation_ratio}")
+        activation_ratio = activation_ratio*40
+        logger.info(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
         
         return activation_ratio
 

src/think_flow_demo/heartflow.py

@@ -1,4 +1,4 @@
-from .current_mind import SubHeartflow
+from .sub_heartflow import SubHeartflow
 from src.plugins.moods.moods import MoodManager
 from src.plugins.models.utils_model import LLM_request
 from src.plugins.config.config import global_config, BotConfig
@@ -46,7 +46,7 @@ class Heartflow:
         logger.info("麦麦大脑袋转起来了")
         self.current_state.update_current_state_info()
         
-        personality_info = " ".join(BotConfig.PROMPT_PERSONALITY)
+        personality_info = " ".join(global_config.PROMPT_PERSONALITY)
         current_thinking_info = self.current_mind
         mood_info = self.current_state.mood
         related_memory_info = 'memory'

src/think_flow_demo/outer_world.py

@@ -16,6 +16,10 @@ class Talking_info:
         self.observe_times = 0
         self.activate = 360
         
+        self.last_summary_time = int(datetime.now().timestamp())  # 上次更新summary的时间
+        self.summary_count = 0  # 30秒内的更新次数
+        self.max_update_in_30s = 2
+        
         self.oberve_interval = 3
         
         self.llm_summary = LLM_request(
@@ -60,16 +64,22 @@ class Talking_info:
         if len(self.talking_message) > 20:
             self.talking_message = self.talking_message[-20:]  # 只保留最新的20条
         self.translate_message_list_to_str()
-        # print(self.talking_message_str)
         self.observe_times += 1
         self.last_observe_time = new_messages[-1]["time"]
         
-        if self.observe_times > 3:
+        # 检查是否需要更新summary
+        current_time = int(datetime.now().timestamp())
+        if current_time - self.last_summary_time >= 30:  # 如果超过30秒，重置计数
+            self.summary_count = 0
+            self.last_summary_time = current_time
+            
+        if self.summary_count < self.max_update_in_30s:  # 如果30秒内更新次数小于2次
             await self.update_talking_summary()
-            # print(f"更新了聊天总结：{self.talking_summary}")
+            self.summary_count += 1
     
     async def update_talking_summary(self):
         #基于已经有的talking_summary，和新的talking_message，生成一个summary
+        # print(f"更新聊天总结：{self.talking_summary}")
         prompt = ""
         prompt = f"你正在参与一个qq群聊的讨论，这个群之前在聊的内容是：{self.talking_summary}\n"
         prompt += f"现在群里的群友们产生了新的讨论，有了新的发言，具体内容如下：{self.talking_message_str}\n"

src/think_flow_demo/sub_heartflow.py

@@ -6,6 +6,16 @@ from src.plugins.config.config import global_config, BotConfig
 import re
 import time
 from src.plugins.schedule.schedule_generator import bot_schedule
+from src.plugins.memory_system.Hippocampus import HippocampusManager
+from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402
+
+subheartflow_config = LogConfig(
+    # 使用海马体专用样式
+    console_format=SUB_HEARTFLOW_STYLE_CONFIG["console_format"],
+    file_format=SUB_HEARTFLOW_STYLE_CONFIG["file_format"],
+)   
+logger = get_module_logger("subheartflow", config=subheartflow_config)
+
 
 class CuttentState:
     def __init__(self):
@@ -37,7 +47,7 @@ class SubHeartflow:
         if not self.current_mind:
             self.current_mind = "你什么也没想"
             
-        self.personality_info = " ".join(BotConfig.PROMPT_PERSONALITY)
+        self.personality_info = " ".join(global_config.PROMPT_PERSONALITY)
     
     def assign_observe(self,stream_id):
         self.outer_world = outer_world.get_world_by_stream_id(stream_id)
@@ -55,23 +65,42 @@ class SubHeartflow:
                 await asyncio.sleep(60)
     
     async def do_a_thinking(self):
-        print("麦麦小脑袋转起来了")
         self.current_state.update_current_state_info()
         
         current_thinking_info = self.current_mind
         mood_info = self.current_state.mood
-        related_memory_info = ''
+        
         message_stream_info = self.outer_world.talking_summary
-        schedule_info = bot_schedule.get_current_num_task(num = 2,time_info = False)
+        print(f"message_stream_info：{message_stream_info}")
+        
+        related_memory = await HippocampusManager.get_instance().get_memory_from_text(
+            text=message_stream_info,
+            max_memory_num=3,
+            max_memory_length=2,
+            max_depth=3,
+            fast_retrieval=False
+        )
+        # print(f"相关记忆：{related_memory}")
+        if related_memory:
+            related_memory_info = ""
+            for memory in related_memory:
+                related_memory_info += memory[1]
+        else:
+            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)
         
         prompt = ""
         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"{self.personality_info}\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"你现在{mood_info}。"
+        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:"
         reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
@@ -79,7 +108,8 @@ class SubHeartflow:
         self.update_current_mind(reponse)
         
         self.current_mind = reponse
-        print(f"麦麦的脑内状态：{self.current_mind}")
+        print(prompt)
+        logger.info(f"麦麦的脑内状态：{self.current_mind}")
     
     async def do_after_reply(self,reply_content,chat_talking_prompt):
         # print("麦麦脑袋转起来了")
@@ -91,24 +121,29 @@ class SubHeartflow:
         message_stream_info = self.outer_world.talking_summary
         message_new_info = chat_talking_prompt
         reply_info = reply_content
+        schedule_info = bot_schedule.get_current_num_task(num = 1,time_info = False)
+       
         
         prompt = ""
-        prompt += f"{self.personality_info}\n"
+        prompt += f"你刚刚在做的事情是：{schedule_info}\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}"
         prompt += f"你现在{mood_info}。"
         prompt += "现在你接下去继续思考，产生新的想法，记得保留你刚刚的想法，不要分点输出，输出连贯的内心独白"
-        prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，以及你回复的内容，不要思考太多:"
+        prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:"
         
         reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
         
         self.update_current_mind(reponse)
         
         self.current_mind = reponse
-        print(f"{self.observe_chat_id}麦麦的脑内状态：{self.current_mind}")
+        logger.info(f"麦麦回复后的脑内状态：{self.current_mind}")
         
         self.last_reply_time = time.time()
         
@@ -133,7 +168,7 @@ class SubHeartflow:
         else:
             self.current_state.willing = 0
             
-        print(f"{self.observe_chat_id}麦麦的回复意愿：{self.current_state.willing}")
+        logger.info(f"{self.observe_chat_id}麦麦的回复意愿：{self.current_state.willing}")
             
         return self.current_state.willing
 

template/bot_config_template.toml

@@ -3,7 +3,7 @@ version = "0.0.11"
 
 [mai_version]
 version = "0.6.0"
-version-fix = "snapshot-1"
+version-fix = "snapshot-2"
 
 #以下是给开发人员阅读的，一般用户不需要阅读
 #如果你想要修改配置文件，请在修改后将version的值进行变更