diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py index 0952e0024..47066ead5 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -27,6 +27,7 @@ logger = log_module.setup_logger(LogClassification.MEMORY) logger.info("初始化记忆系统") + class Memory_graph: def __init__(self): self.G = nx.Graph() # 使用 networkx 的图结构 @@ -35,9 +36,9 @@ class Memory_graph: # 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 @@ -45,14 +46,14 @@ class Memory_graph: 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, - created_time=current_time, # 添加创建时间 - last_modified=current_time) # 添加最后修改时间 + 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: if 'memory_items' in self.G.nodes[concept]: if not isinstance(self.G.nodes[concept]['memory_items'], list): @@ -68,10 +69,10 @@ class Memory_graph: 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], - created_time=current_time, # 添加创建时间 - last_modified=current_time) # 添加最后修改时间 + 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 @@ -210,12 +211,13 @@ class Hippocampus: # 成功抽取短期消息样本 # 数据写回：增加记忆次数 for message in messages: - db.messages.update_one({"_id": message["_id"]}, {"$set": {"memorized_times": message["memorized_times"] + 1}}) + db.messages.update_one({"_id": message["_id"]}, + {"$set": {"memorized_times": message["memorized_times"] + 1}}) return messages try_count += 1 # 三次尝试均失败 return None - + def get_memory_sample(self, chat_size=20, time_frequency: dict = {'near': 2, 'mid': 4, 'far': 3}): """获取记忆样本 @@ -225,7 +227,7 @@ class Hippocampus: # 硬编码：每条消息最大记忆次数 # 如有需求可写入global_config max_memorized_time_per_msg = 3 - + current_timestamp = datetime.datetime.now().timestamp() chat_samples = [] @@ -324,20 +326,20 @@ class Hippocampus: # 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 @@ -358,7 +360,7 @@ class Hippocampus: async def operation_build_memory(self, chat_size=20): time_frequency = {'near': 1, 'mid': 4, 'far': 4} memory_samples = self.get_memory_sample(chat_size, time_frequency) - + for i, messages in enumerate(memory_samples, 1): all_topics = [] # 加载进度可视化 @@ -371,14 +373,14 @@ class Hippocampus: compress_rate = global_config.memory_compress_rate 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)}") - + current_time = datetime.datetime.now().timestamp() - + 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) - + # 连接相似的已存在主题 if topic in similar_topics_dict: similar_topics = similar_topics_dict[topic] @@ -386,11 +388,11 @@ class Hippocampus: 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, - created_time=current_time, - last_modified=current_time) - + self.memory_graph.G.add_edge(topic, similar_topic, + strength=strength, + created_time=current_time, + last_modified=current_time) + # 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)): @@ -416,7 +418,7 @@ 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()) @@ -466,7 +468,7 @@ class Hippocampus: edge_hash = self.calculate_edge_hash(source, target) edge_key = (source, target) 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()) @@ -499,7 +501,7 @@ class Hippocampus: """从数据库同步数据到内存中的图结构""" current_time = datetime.datetime.now().timestamp() need_update = False - + # 清空当前图 self.memory_graph.G.clear() @@ -510,7 +512,7 @@ class Hippocampus: memory_items = node.get('memory_items', []) if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] - + # 检查时间字段是否存在 if 'created_time' not in node or 'last_modified' not in node: need_update = True @@ -520,22 +522,22 @@ class Hippocampus: update_data['created_time'] = current_time if 'last_modified' not in node: update_data['last_modified'] = current_time - + db.graph_data.nodes.update_one( {'concept': concept}, {'$set': update_data} ) logger.info(f"[时间更新] 节点 {concept} 添加缺失的时间字段") - + # 获取时间信息(如果不存在则使用当前时间) created_time = node.get('created_time', current_time) last_modified = node.get('last_modified', current_time) - + # 添加节点到图中 - self.memory_graph.G.add_node(concept, - memory_items=memory_items, - created_time=created_time, - last_modified=last_modified) + self.memory_graph.G.add_node(concept, + memory_items=memory_items, + created_time=created_time, + last_modified=last_modified) # 从数据库加载所有边 edges = list(db.graph_data.edges.find()) @@ -543,7 +545,7 @@ class Hippocampus: source = edge['source'] target = edge['target'] strength = edge.get('strength', 1) - + # 检查时间字段是否存在 if 'created_time' not in edge or 'last_modified' not in edge: need_update = True @@ -553,24 +555,24 @@ class Hippocampus: update_data['created_time'] = current_time if 'last_modified' not in edge: update_data['last_modified'] = current_time - + db.graph_data.edges.update_one( {'source': source, 'target': target}, {'$set': update_data} ) logger.info(f"[时间更新] 边 {source} - {target} 添加缺失的时间字段") - + # 获取时间信息(如果不存在则使用当前时间) created_time = edge.get('created_time', current_time) last_modified = edge.get('last_modified', current_time) - + # 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, - created_time=created_time, - last_modified=last_modified) - + self.memory_graph.G.add_edge(source, target, + strength=strength, + created_time=created_time, + last_modified=last_modified) + if need_update: logger.success("[数据库] 已为缺失的时间字段进行补充") @@ -578,44 +580,44 @@ class Hippocampus: """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""" # 检查数据库是否为空 # logger.remove() - + logger.info(f"[遗忘] 开始检查数据库... 当前Logger信息:") # logger.info(f"- Logger名称: {logger.name}") logger.info(f"- Logger等级: {logger.level}") # logger.info(f"- Logger处理器: {[handler.__class__.__name__ for handler in logger.handlers]}") - + # logger2 = setup_logger(LogModule.MEMORY) # logger2.info(f"[遗忘] 开始检查数据库... 当前Logger信息:") # logger.info(f"[遗忘] 开始检查数据库... 当前Logger信息:") - + all_nodes = list(self.memory_graph.G.nodes()) all_edges = list(self.memory_graph.G.edges()) - + if not all_nodes and not all_edges: logger.info("[遗忘] 记忆图为空,无需进行遗忘操作") return - + 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("[遗忘] 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') - - if current_time - last_modified > 3600*global_config.memory_forget_time: + + if current_time - last_modified > 3600 * global_config.memory_forget_time: 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 @@ -625,23 +627,23 @@ class Hippocampus: edge_data['last_modified'] = current_time edge_changes['weakened'] += 1 logger.info(f"[遗忘] 连接减弱: {source} -> {target} (强度: {current_strength} -> {new_strength})") - + # 检查并遗忘话题 logger.info("[遗忘] 开始检查节点...") for node in nodes_to_check: node_data = self.memory_graph.G.nodes[node] last_modified = node_data.get('last_modified', current_time) - - if current_time - last_modified > 3600*24: + + if current_time - last_modified > 3600 * 24: 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 @@ -651,7 +653,7 @@ class Hippocampus: self.memory_graph.G.remove_node(node) node_changes['removed'] += 1 logger.info(f"[遗忘] 节点移除: {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.info("[遗忘] 统计信息:") @@ -943,6 +945,7 @@ def segment_text(text): seg_text = list(jieba.cut(text)) return seg_text + driver = get_driver() config = driver.config