记忆系统优化

优化了记忆的连接建立
重启了遗忘功能

src/plugins/chat/__init__.py

@@ -121,9 +121,9 @@ async def build_memory_task():
 @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=0.1)
-    # print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成")
+    print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...")
+    await hippocampus.operation_forget_topic(percentage=0.1)
+    print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成")
 
 
 @scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval + 10, id="merge_memory")

src/plugins/chat/emoji_manager.py

@@ -203,7 +203,7 @@ class EmojiManager:
         try:
             prompt = f'这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，注意不要输出任何对消息内容的分析内容，只输出\"一种什么样的感觉\"中间的形容词部分。'
 
-            content, _ = await self.llm_emotion_judge.generate_response_async(prompt)
+            content, _ = await self.llm_emotion_judge.generate_response_async(prompt,temperature=1.5)
             logger.info(f"输出描述: {content}")
             return content
 

src/plugins/knowledege/knowledge_library.py

@@ -79,7 +79,7 @@ class KnowledgeLibrary:
                 content = f.read()
                 
             # 按1024字符分段
-            segments = [content[i:i+600] for i in range(0, len(content), 600)]
+            segments = [content[i:i+600] for i in range(0, len(content), 300)]
             
             # 处理每个分段
             for segment in segments:

src/plugins/memory_system/memory.py

@@ -25,26 +25,46 @@ class Memory_graph:
         self.db = Database.get_instance()
 
     def connect_dot(self, concept1, concept2):
-        # 如果边已存在，增加 strength
+        # 避免自连接
+        if concept1 == concept2:
+            return
+        
+        current_time = datetime.datetime.now().timestamp()
+        
+        # 如果边已存在,增加 strength
         if self.G.has_edge(concept1, concept2):
             self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1
+            # 更新最后修改时间
+            self.G[concept1][concept2]['last_modified'] = current_time
         else:
-            # 如果是新边，初始化 strength 为 1
-            self.G.add_edge(concept1, concept2, strength=1)
+            # 如果是新边,初始化 strength 为 1
+            self.G.add_edge(concept1, concept2, 
+                          strength=1,
+                          created_time=current_time,  # 添加创建时间
+                          last_modified=current_time) # 添加最后修改时间
 
     def add_dot(self, concept, memory):
+        current_time = datetime.datetime.now().timestamp()
+        
         if concept in self.G:
-            # 如果节点已存在，将新记忆添加到现有列表中
             if 'memory_items' in self.G.nodes[concept]:
                 if not isinstance(self.G.nodes[concept]['memory_items'], list):
-                    # 如果当前不是列表，将其转换为列表
                     self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']]
                 self.G.nodes[concept]['memory_items'].append(memory)
+                # 更新最后修改时间
+                self.G.nodes[concept]['last_modified'] = current_time
             else:
                 self.G.nodes[concept]['memory_items'] = [memory]
+                # 如果节点存在但没有memory_items,说明是第一次添加memory,设置created_time
+                if 'created_time' not in self.G.nodes[concept]:
+                    self.G.nodes[concept]['created_time'] = current_time
+                self.G.nodes[concept]['last_modified'] = current_time
         else:
-            # 如果是新节点，创建新的记忆列表
-            self.G.add_node(concept, memory_items=[memory])
+            # 如果是新节点,创建新的记忆列表
+            self.G.add_node(concept, 
+                          memory_items=[memory],
+                          created_time=current_time,  # 添加创建时间
+                          last_modified=current_time) # 添加最后修改时间
 
     def get_dot(self, concept):
         # 检查节点是否存在于图中
@@ -191,15 +211,11 @@ class Hippocampus:
     async def memory_compress(self, messages: list, compress_rate=0.1):
         """压缩消息记录为记忆
         
-        Args:
-            messages: 消息记录字典列表，每个字典包含text和time字段
-            compress_rate: 压缩率
-            
         Returns:
-            set: (话题, 记忆) 元组集合
+            tuple: (压缩记忆集合, 相似主题字典)
         """
         if not messages:
-            return set()
+            return set(), {}
 
         # 合并消息文本，同时保留时间信息
         input_text = ""
@@ -246,12 +262,33 @@ class Hippocampus:
 
         # 等待所有任务完成
         compressed_memory = set()
+        similar_topics_dict = {}  # 存储每个话题的相似主题列表
         for topic, task in tasks:
             response = await task
             if response:
                 compressed_memory.add((topic, response[0]))
+                # 为每个话题查找相似的已存在主题
+                existing_topics = list(self.memory_graph.G.nodes())
+                similar_topics = []
+                
+                for existing_topic in existing_topics:
+                    topic_words = set(jieba.cut(topic))
+                    existing_words = set(jieba.cut(existing_topic))
+                    
+                    all_words = topic_words | existing_words
+                    v1 = [1 if word in topic_words else 0 for word in all_words]
+                    v2 = [1 if word in existing_words else 0 for word in all_words]
+                    
+                    similarity = cosine_similarity(v1, v2)
+                    
+                    if similarity >= 0.6:
+                        similar_topics.append((existing_topic, similarity))
+                
+                similar_topics.sort(key=lambda x: x[1], reverse=True)
+                similar_topics = similar_topics[:5]
+                similar_topics_dict[topic] = similar_topics
 
-        return compressed_memory
+        return compressed_memory, similar_topics_dict
 
     def calculate_topic_num(self, text, compress_rate):
         """计算文本的话题数量"""
@@ -265,33 +302,40 @@ class Hippocampus:
         return topic_num
 
     async def operation_build_memory(self, chat_size=20):
-        # 最近消息获取频率
-        time_frequency = {'near': 2, 'mid': 4, 'far': 2}
-        memory_sample = self.get_memory_sample(chat_size, time_frequency)
-
-        for i, input_text in enumerate(memory_sample, 1):
-            # 加载进度可视化
+        time_frequency = {'near': 3, 'mid': 8, 'far': 5}
+        memory_samples = self.get_memory_sample(chat_size, time_frequency)
+        
+        for i, messages in enumerate(memory_samples, 1):
             all_topics = []
-            progress = (i / len(memory_sample)) * 100
+            # 加载进度可视化
+            progress = (i / len(memory_samples)) * 100
             bar_length = 30
-            filled_length = int(bar_length * i // len(memory_sample))
+            filled_length = int(bar_length * i // len(memory_samples))
             bar = '█' * filled_length + '-' * (bar_length - filled_length)
-            logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_sample)})")
+            logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
 
-            # 生成压缩后记忆 ,表现为 (话题,记忆) 的元组
-            compressed_memory = set()
             compress_rate = 0.1
-            compressed_memory = await self.memory_compress(input_text, compress_rate)
-            logger.info(f"压缩后记忆数量: {len(compressed_memory)}")
-
-            # 将记忆加入到图谱中
+            compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate)
+            logger.info(f"压缩后记忆数量: {len(compressed_memory)}，似曾相识的话题: {len(similar_topics_dict)}")
+            
             for topic, memory in compressed_memory:
                 logger.info(f"添加节点: {topic}")
                 self.memory_graph.add_dot(topic, memory)
-                all_topics.append(topic)  # 收集所有话题
+                all_topics.append(topic)
+                
+                # 连接相似的已存在主题
+                if topic in similar_topics_dict:
+                    similar_topics = similar_topics_dict[topic]
+                    for similar_topic, similarity in similar_topics:
+                        if topic != similar_topic:
+                            strength = int(similarity * 10)
+                            logger.info(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})")
+                            self.memory_graph.G.add_edge(topic, similar_topic, strength=strength)
+            
+            # 连接同批次的相关话题
             for i in range(len(all_topics)):
                 for j in range(i + 1, len(all_topics)):
-                    logger.info(f"连接节点: {all_topics[i]} 和 {all_topics[j]}")
+                    logger.info(f"连接同批次节点: {all_topics[i]} 和 {all_topics[j]}")
                     self.memory_graph.connect_dot(all_topics[i], all_topics[j])
 
         self.sync_memory_to_db()
@@ -302,7 +346,7 @@ class Hippocampus:
         db_nodes = list(self.memory_graph.db.db.graph_data.nodes.find())
         memory_nodes = list(self.memory_graph.G.nodes(data=True))
 
-        # 转换数据库节点为字典格式，方便查找
+        # 转换数据库节点为字典格式,方便查找
         db_nodes_dict = {node['concept']: node for node in db_nodes}
 
         # 检查并更新节点
@@ -313,13 +357,19 @@ class Hippocampus:
 
             # 计算内存中节点的特征值
             memory_hash = self.calculate_node_hash(concept, memory_items)
+            
+            # 获取时间信息
+            created_time = data.get('created_time', datetime.datetime.now().timestamp())
+            last_modified = data.get('last_modified', datetime.datetime.now().timestamp())
 
             if concept not in db_nodes_dict:
-                # 数据库中缺少的节点，添加
+                # 数据库中缺少的节点,添加
                 node_data = {
                     'concept': concept,
                     'memory_items': memory_items,
-                    'hash': memory_hash
+                    'hash': memory_hash,
+                    'created_time': created_time,
+                    'last_modified': last_modified
                 }
                 self.memory_graph.db.db.graph_data.nodes.insert_one(node_data)
             else:
@@ -327,25 +377,21 @@ class Hippocampus:
                 db_node = db_nodes_dict[concept]
                 db_hash = db_node.get('hash', None)
 
-                # 如果特征值不同，则更新节点
+                # 如果特征值不同,则更新节点
                 if db_hash != memory_hash:
                     self.memory_graph.db.db.graph_data.nodes.update_one(
                         {'concept': concept},
                         {'$set': {
                             'memory_items': memory_items,
-                            'hash': memory_hash
+                            'hash': memory_hash,
+                            'created_time': created_time,
+                            'last_modified': last_modified
                         }}
                     )
 
-        # 检查并删除数据库中多余的节点
-        memory_concepts = set(node[0] for node in memory_nodes)
-        for db_node in db_nodes:
-            if db_node['concept'] not in memory_concepts:
-                self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': db_node['concept']})
-
         # 处理边的信息
         db_edges = list(self.memory_graph.db.db.graph_data.edges.find())
-        memory_edges = list(self.memory_graph.G.edges())
+        memory_edges = list(self.memory_graph.G.edges(data=True))
 
         # 创建边的哈希值字典
         db_edge_dict = {}
@@ -357,10 +403,14 @@ class Hippocampus:
             }
 
         # 检查并更新边
-        for source, target in memory_edges:
+        for source, target, data in memory_edges:
             edge_hash = self.calculate_edge_hash(source, target)
             edge_key = (source, target)
-            strength = self.memory_graph.G[source][target].get('strength', 1)
+            strength = data.get('strength', 1)
+            
+            # 获取边的时间信息
+            created_time = data.get('created_time', datetime.datetime.now().timestamp())
+            last_modified = data.get('last_modified', datetime.datetime.now().timestamp())
 
             if edge_key not in db_edge_dict:
                 # 添加新边
@@ -368,7 +418,9 @@ class Hippocampus:
                     'source': source,
                     'target': target,
                     'strength': strength,
-                    'hash': edge_hash
+                    'hash': edge_hash,
+                    'created_time': created_time,
+                    'last_modified': last_modified
                 }
                 self.memory_graph.db.db.graph_data.edges.insert_one(edge_data)
             else:
@@ -378,20 +430,12 @@ class Hippocampus:
                         {'source': source, 'target': target},
                         {'$set': {
                             'hash': edge_hash,
-                            'strength': strength
+                            'strength': strength,
+                            'created_time': created_time,
+                            'last_modified': last_modified
                         }}
                     )
 
-        # 删除多余的边
-        memory_edge_set = set(memory_edges)
-        for edge_key in db_edge_dict:
-            if edge_key not in memory_edge_set:
-                source, target = edge_key
-                self.memory_graph.db.db.graph_data.edges.delete_one({
-                    'source': source,
-                    'target': target
-                })
-
     def sync_memory_from_db(self):
         """从数据库同步数据到内存中的图结构"""
         # 清空当前图
@@ -405,61 +449,107 @@ class Hippocampus:
             # 确保memory_items是列表
             if not isinstance(memory_items, list):
                 memory_items = [memory_items] if memory_items else []
+            
+            # 获取时间信息
+            created_time = node.get('created_time', datetime.datetime.now().timestamp())
+            last_modified = node.get('last_modified', datetime.datetime.now().timestamp())
+            
             # 添加节点到图中
-            self.memory_graph.G.add_node(concept, memory_items=memory_items)
+            self.memory_graph.G.add_node(concept, 
+                                       memory_items=memory_items,
+                                       created_time=created_time,
+                                       last_modified=last_modified)
 
         # 从数据库加载所有边
         edges = self.memory_graph.db.db.graph_data.edges.find()
         for edge in edges:
             source = edge['source']
             target = edge['target']
-            strength = edge.get('strength', 1)  # 获取 strength，默认为 1
+            strength = edge.get('strength', 1)  # 获取 strength,默认为 1
+            
+            # 获取时间信息
+            created_time = edge.get('created_time', datetime.datetime.now().timestamp())
+            last_modified = edge.get('last_modified', datetime.datetime.now().timestamp())
+            
             # 只有当源节点和目标节点都存在时才添加边
             if source in self.memory_graph.G and target in self.memory_graph.G:
-                self.memory_graph.G.add_edge(source, target, strength=strength)
+                self.memory_graph.G.add_edge(source, target, 
+                                           strength=strength,
+                                           created_time=created_time,
+                                           last_modified=last_modified)
 
     async def operation_forget_topic(self, percentage=0.1):
-        """随机选择图中一定比例的节点进行检查，根据条件决定是否遗忘"""
-        # 获取所有节点
+        """随机选择图中一定比例的节点和边进行检查,根据时间条件决定是否遗忘"""
         all_nodes = list(self.memory_graph.G.nodes())
-        # 计算要检查的节点数量
-        check_count = max(1, int(len(all_nodes) * percentage))
-        # 随机选择节点
-        nodes_to_check = random.sample(all_nodes, check_count)
-
-        forgotten_nodes = []
+        all_edges = list(self.memory_graph.G.edges())
+        
+        check_nodes_count = max(1, int(len(all_nodes) * percentage))
+        check_edges_count = max(1, int(len(all_edges) * percentage))
+        
+        nodes_to_check = random.sample(all_nodes, check_nodes_count)
+        edges_to_check = random.sample(all_edges, check_edges_count)
+        
+        edge_changes = {'weakened': 0, 'removed': 0}
+        node_changes = {'reduced': 0, 'removed': 0}
+        
+        current_time = datetime.datetime.now().timestamp()
+        
+        # 检查并遗忘连接
+        logger.info("开始检查连接...")
+        for source, target in edges_to_check:
+            edge_data = self.memory_graph.G[source][target]
+            last_modified = edge_data.get('last_modified')
+            # print(source,target)
+            # print(f"float(last_modified):{float(last_modified)}"    )
+            # print(f"current_time:{current_time}")
+            # print(f"current_time - last_modified:{current_time - last_modified}")
+            if current_time - last_modified > 3600*24:  # test
+                current_strength = edge_data.get('strength', 1)
+                new_strength = current_strength - 1
+                
+                if new_strength <= 0:
+                    self.memory_graph.G.remove_edge(source, target)
+                    edge_changes['removed'] += 1
+                    logger.info(f"\033[1;31m[连接移除]\033[0m {source} - {target}")
+                else:
+                    edge_data['strength'] = new_strength
+                    edge_data['last_modified'] = current_time
+                    edge_changes['weakened'] += 1
+                    logger.info(f"\033[1;34m[连接减弱]\033[0m {source} - {target} (强度: {current_strength} -> {new_strength})")
+        
+        # 检查并遗忘话题
+        logger.info("开始检查节点...")
         for node in nodes_to_check:
-            # 获取节点的连接数
-            connections = self.memory_graph.G.degree(node)
-
-            # 获取节点的内容条数
-            memory_items = self.memory_graph.G.nodes[node].get('memory_items', [])
-            if not isinstance(memory_items, list):
-                memory_items = [memory_items] if memory_items else []
-            content_count = len(memory_items)
-
-            # 检查连接强度
-            weak_connections = True
-            if connections > 1:  # 只有当连接数大于1时才检查强度
-                for neighbor in self.memory_graph.G.neighbors(node):
-                    strength = self.memory_graph.G[node][neighbor].get('strength', 1)
-                    if strength > 2:
-                        weak_connections = False
-                        break
-
-            # 如果满足遗忘条件
-            if (connections <= 1 and weak_connections) or content_count <= 2:
-                removed_item = self.memory_graph.forget_topic(node)
-                if removed_item:
-                    forgotten_nodes.append((node, removed_item))
-                    logger.debug(f"遗忘节点 {node} 的记忆: {removed_item}")
-
-        # 同步到数据库
-        if forgotten_nodes:
+            node_data = self.memory_graph.G.nodes[node]
+            last_modified = node_data.get('last_modified', current_time)
+            
+            if current_time - last_modified > 3600*24:  # test
+                memory_items = node_data.get('memory_items', [])
+                if not isinstance(memory_items, list):
+                    memory_items = [memory_items] if memory_items else []
+                
+                if memory_items:
+                    current_count = len(memory_items)
+                    removed_item = random.choice(memory_items)
+                    memory_items.remove(removed_item)
+                    
+                    if memory_items:
+                        self.memory_graph.G.nodes[node]['memory_items'] = memory_items
+                        self.memory_graph.G.nodes[node]['last_modified'] = current_time
+                        node_changes['reduced'] += 1
+                        logger.info(f"\033[1;33m[记忆减少]\033[0m {node} (记忆数量: {current_count} -> {len(memory_items)})")
+                    else:
+                        self.memory_graph.G.remove_node(node)
+                        node_changes['removed'] += 1
+                        logger.info(f"\033[1;31m[节点移除]\033[0m {node}")
+        
+        if any(count > 0 for count in edge_changes.values()) or any(count > 0 for count in node_changes.values()):
             self.sync_memory_to_db()
-            logger.debug(f"完成遗忘操作，共遗忘 {len(forgotten_nodes)} 个节点的记忆")
+            logger.info("\n遗忘操作统计:")
+            logger.info(f"连接变化: {edge_changes['weakened']} 个减弱, {edge_changes['removed']} 个移除")
+            logger.info(f"节点变化: {node_changes['reduced']} 个减少记忆, {node_changes['removed']} 个移除")
         else:
-            logger.debug("本次检查没有节点满足遗忘条件")
+            logger.info("\n本次检查没有节点或连接满足遗忘条件")
 
     async def merge_memory(self, topic):
         """
@@ -486,7 +576,7 @@ class Hippocampus:
         logger.debug(f"选择的记忆:\n{merged_text}")
 
         # 使用memory_compress生成新的压缩记忆
-        compressed_memories = await self.memory_compress(selected_memories, 0.1)
+        compressed_memories, _ = await self.memory_compress(selected_memories, 0.1)
 
         # 从原记忆列表中移除被选中的记忆
         for memory in selected_memories: