fix:解耦海马体，莲藕促销

src/plugins/chat/__init__.py

@@ -14,7 +14,8 @@ 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 ..memory_system.memory import hippocampus
+# from ..memory_system.memory import hippocampus
+from src.plugins.memory_system.Hippocampus import HippocampusManager
 from .message_sender import message_manager, message_sender
 from .storage import MessageStorage
 from src.common.logger import get_module_logger
@@ -59,6 +60,22 @@ async def start_think_flow():
         logger.error(f"启动大脑和外部世界失败: {e}")
         raise
 
+async def start_memory():
+    """启动记忆系统"""
+    try:
+        start_time = time.time()
+        logger.info("开始初始化记忆系统...")
+        
+        # 使用HippocampusManager初始化海马体
+        hippocampus_manager = HippocampusManager.get_instance()
+        hippocampus_manager.initialize(global_config=global_config)
+        
+        end_time = time.time()
+        logger.success(f"记忆系统初始化完成，耗时: {end_time - start_time:.2f} 秒")
+    except Exception as e:
+        logger.error(f"记忆系统初始化失败: {e}")
+        raise
+
 
 @driver.on_startup
 async def start_background_tasks():
@@ -80,6 +97,8 @@ async def start_background_tasks():
     # 只启动表情包管理任务
     asyncio.create_task(emoji_manager.start_periodic_check())
     
+    asyncio.create_task(start_memory())
+
 
 @driver.on_startup
 async def init_schedule():
@@ -139,14 +158,14 @@ async def _(bot: Bot, event: NoticeEvent, state: T_State):
 @scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval, id="build_memory")
 async def build_memory_task():
     """每build_memory_interval秒执行一次记忆构建"""
-    await hippocampus.operation_build_memory()
+    await HippocampusManager.get_instance().build_memory()
 
 
 @scheduler.scheduled_job("interval", seconds=global_config.forget_memory_interval, id="forget_memory")
 async def forget_memory_task():
     """每30秒执行一次记忆构建"""
     print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...")
-    await hippocampus.operation_forget_topic(percentage=global_config.memory_forget_percentage)
+    await HippocampusManager.get_instance().forget_memory(percentage=global_config.memory_forget_percentage)
     print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成")
 
 

src/plugins/chat/bot.py

@@ -12,7 +12,7 @@ from nonebot.adapters.onebot.v11 import (
     FriendRecallNoticeEvent,
 )
 
-from ..memory_system.memory import hippocampus
+from ..memory_system.Hippocampus import HippocampusManager
 from ..moods.moods import MoodManager  # 导入情绪管理器
 from .config import global_config
 from .emoji_manager import emoji_manager  # 导入表情包管理器
@@ -129,7 +129,8 @@ class ChatBot:
 
         # 根据话题计算激活度
         topic = ""
-        interested_rate = await hippocampus.memory_activate_value(message.processed_plain_text) / 100
+        # interested_rate = await HippocampusManager.get_instance().memory_activate_value(message.processed_plain_text) / 100
+        interested_rate = 0.1
         logger.debug(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}")
 

src/plugins/chat/prompt_builder.py

@@ -3,7 +3,7 @@ import time
 from typing import Optional
 
 from ...common.database import db
-from ..memory_system.memory import hippocampus, memory_graph
+from ..memory_system.Hippocampus import HippocampusManager
 from ..moods.moods import MoodManager
 from ..schedule.schedule_generator import bot_schedule
 from .config import global_config
@@ -79,19 +79,20 @@ class PromptBuilder:
         start_time = time.time()
 
         # 调用 hippocampus 的 get_relevant_memories 方法
-        relevant_memories = await hippocampus.get_relevant_memories(
+        relevant_memories = await HippocampusManager.get_instance().get_memory_from_text(
-            text=message_txt, max_topics=3, similarity_threshold=0.5, max_memory_num=4
+            text=message_txt, num=3, max_depth=2, fast_retrieval=True
         )
+        memory_str = "\n".join(memory for topic, memories, _ in relevant_memories for memory in memories)
+        print(f"memory_str: {memory_str}")
 
         if relevant_memories:
             # 格式化记忆内容
-            memory_str = "\n".join(m["content"] for m in relevant_memories)
             memory_prompt = f"你回忆起：\n{memory_str}\n"
 
             # 打印调试信息
             logger.debug("[记忆检索]找到以下相关记忆：")
-            for memory in relevant_memories:
+            # for topic, memory_items, similarity in relevant_memories:
-                logger.debug(f"- 主题「{memory['topic']}」[相似度: {memory['similarity']:.2f}]: {memory['content']}")
+            #     logger.debug(f"- 主题「{topic}」[相似度: {similarity:.2f}]: {memory_items}")
 
         end_time = time.time()
         logger.info(f"回忆耗时: {(end_time - start_time):.3f}秒")
@@ -192,7 +193,7 @@ class PromptBuilder:
         # print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
 
         # 获取主动发言的话题
-        all_nodes = memory_graph.dots
+        all_nodes = HippocampusManager.get_instance().memory_graph.dots
         all_nodes = filter(lambda dot: len(dot[1]["memory_items"]) > 3, all_nodes)
         nodes_for_select = random.sample(all_nodes, 5)
         topics = [info[0] for info in nodes_for_select]
@@ -245,7 +246,7 @@ class PromptBuilder:
         related_info = ""
         logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
         embedding = await get_embedding(message, request_type="prompt_build")
-        related_info += self.get_info_from_db(embedding, threshold=threshold)
+        related_info += self.get_info_from_db(embedding, limit=1, threshold=threshold)
 
         return related_info
 

src/plugins/memory_system/Hippocampus.py

@@ -4,7 +4,6 @@ import math
 import random
 import time
 import re
-
 import jieba
 import networkx as nx
 
@@ -15,7 +14,6 @@ from ..chat.utils import (
     calculate_information_content,
     cosine_similarity,
     get_closest_chat_from_db,
-    text_to_vector,
 )
 from ..models.utils_model import LLM_request
 from src.common.logger import get_module_logger, LogConfig, MEMORY_STYLE_CONFIG
@@ -180,7 +178,7 @@ class EntorhinalCortex:
         max_memorized_time_per_msg = 3
 
         # 创建双峰分布的记忆调度器
-        scheduler = MemoryBuildScheduler(
+        sample_scheduler = MemoryBuildScheduler(
             n_hours1=self.config.memory_build_distribution[0],
             std_hours1=self.config.memory_build_distribution[1],
             weight1=self.config.memory_build_distribution[2],
@@ -190,7 +188,7 @@ class EntorhinalCortex:
             total_samples=self.config.build_memory_sample_num
         )
 
-        timestamps = scheduler.get_timestamp_array()
+        timestamps = sample_scheduler.get_timestamp_array()
         logger.info(f"回忆往事: {[time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(ts)) for ts in timestamps]}")
         chat_samples = []
         for timestamp in timestamps:
@@ -674,8 +672,8 @@ 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 = self.config.llm_topic_judge
+        self.llm_topic_judge = LLM_request(self.config.llm_topic_judge)
-        self.llm_summary_by_topic = self.config.llm_summary_by_topic
+        self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic)
 
     def get_all_node_names(self) -> list:
         """获取记忆图中所有节点的名字列表"""
@@ -831,19 +829,79 @@ class Hippocampus:
         unique_memories.sort(key=lambda x: x[2], reverse=True)
         return unique_memories[:num]
 
-# driver = get_driver()
+class HippocampusManager:
-# config = driver.config
+    _instance = None
+    _hippocampus = None
+    _global_config = None
+    _initialized = False
 
-start_time = time.time()
+    @classmethod
+    def get_instance(cls):
+        if cls._instance is None:
+            cls._instance = cls()
+        return cls._instance
 
-# 创建记忆图
+    @classmethod
-memory_graph = Memory_graph()
+    def get_hippocampus(cls):
-# 创建海马体
+        if not cls._initialized:
-hippocampus = Hippocampus()
+            raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
+        return cls._hippocampus
+
+    def initialize(self, global_config):
+        """初始化海马体实例"""
+        if self._initialized:
+            return self._hippocampus
+            
+        self._global_config = global_config
+        self._hippocampus = Hippocampus()
+        self._hippocampus.initialize(global_config)
+        self._initialized = True
+        
+        # 输出记忆系统参数信息
+        config = self._hippocampus.config
+        logger.success("--------------------------------")
+        logger.success("记忆系统参数配置:")
+        logger.success(f"记忆构建间隔: {global_config.build_memory_interval}秒")
+        logger.success(f"记忆遗忘间隔: {global_config.forget_memory_interval}秒")
+        logger.success(f"记忆遗忘比例: {global_config.memory_forget_percentage}")
+        logger.success(f"记忆压缩率: {config.memory_compress_rate}")
+        logger.success(f"记忆构建样本数: {config.build_memory_sample_num}")
+        logger.success(f"记忆构建样本长度: {config.build_memory_sample_length}")
+        logger.success(f"记忆遗忘时间: {config.memory_forget_time}小时")
+        logger.success(f"记忆构建分布: {config.memory_build_distribution}")
+        logger.success("--------------------------------")
+        
+        return self._hippocampus
+
+    async def build_memory(self):
+        """构建记忆的公共接口"""
+        if not self._initialized:
+            raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
+        return await self._hippocampus.parahippocampal_gyrus.operation_build_memory()
+
+    async def forget_memory(self, percentage: float = 0.1):
+        """遗忘记忆的公共接口"""
+        if not self._initialized:
+            raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
+        return await self._hippocampus.parahippocampal_gyrus.operation_forget_topic(percentage)
+
+    async def get_memory_from_text(self, text: str, num: int = 5, max_depth: int = 2, 
+                                 fast_retrieval: bool = False) -> list:
+        """从文本中获取相关记忆的公共接口"""
+        if not self._initialized:
+            raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
+        return await self._hippocampus.get_memory_from_text(text, num, max_depth, fast_retrieval)
+
+    def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
+        """从关键词获取相关记忆的公共接口"""
+        if not self._initialized:
+            raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
+        return self._hippocampus.get_memory_from_keyword(keyword, max_depth)
+
+    def get_all_node_names(self) -> list:
+        """获取所有节点名称的公共接口"""
+        if not self._initialized:
+            raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
+        return self._hippocampus.get_all_node_names()
 
-# 从全局配置初始化记忆系统
-from ..chat.config import global_config
-hippocampus.initialize(global_config=global_config)
 
-end_time = time.time()
-logger.success(f"加载海马体耗时: {end_time - start_time:.2f} 秒")

src/plugins/memory_system/config.py

@@ -29,6 +29,6 @@ class MemoryConfig:
             memory_compress_rate=global_config.memory_compress_rate,
             memory_forget_time=global_config.memory_forget_time,
             memory_ban_words=global_config.memory_ban_words,
-            llm_topic_judge=global_config.topic_judge_model,
+            llm_topic_judge=global_config.llm_topic_judge,
-            llm_summary_by_topic=global_config.summary_by_topic_model
+            llm_summary_by_topic=global_config.llm_summary_by_topic
         ) 

src/plugins/memory_system/memory_deprecated.py

@@ -227,7 +227,7 @@ class Hippocampus:
         max_memorized_time_per_msg = 3
 
         # 创建双峰分布的记忆调度器
-        scheduler = MemoryBuildScheduler(
+        sample_scheduler = MemoryBuildScheduler(
             n_hours1=global_config.memory_build_distribution[0],           # 第一个分布均值（4小时前）
             std_hours1=global_config.memory_build_distribution[1],         # 第一个分布标准差
             weight1=global_config.memory_build_distribution[2],          # 第一个分布权重 60%
@@ -238,7 +238,7 @@ class Hippocampus:
         )
 
         # 生成时间戳数组
-        timestamps = scheduler.get_timestamp_array()
+        timestamps = sample_scheduler.get_timestamp_array()
         # logger.debug(f"生成的时间戳数组: {timestamps}")
         # print(f"生成的时间戳数组: {timestamps}")
         # print(f"时间戳的实际时间: {[time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(ts)) for ts in timestamps]}")

src/plugins/schedule/schedule_generator.py

@@ -140,8 +140,8 @@ class ScheduleGenerator:
         if mind_thinking:
             prompt += f"你脑子里在想：{mind_thinking}\n"
         prompt += f"现在是{now_time}，结合你的个人特点和行为习惯,注意关注你今天的日程安排和想法，这很重要，"
-        prompt += "推测你现在和之后做什么，具体一些，详细一些\n"
+        prompt += "推测你现在在做什么，具体一些，详细一些\n"
-        prompt += "直接返回你在做的事情，不要输出其他内容："
+        prompt += "直接返回你在做的事情，注意是当前时间，不要输出其他内容："
         return prompt
     
     async def generate_daily_schedule(