# -*- coding: utf-8 -*- import os import jieba import networkx as nx import matplotlib.pyplot as plt from collections import Counter import datetime import random import time from ..chat.config import global_config from ...common.database import Database # 使用正确的导入语法 from ..models.utils_model import LLM_request import math from ..chat.utils import calculate_information_content, get_cloest_chat_from_db ,find_similar_topics,text_to_vector,cosine_similarity class Memory_graph: def __init__(self): self.G = nx.Graph() # 使用 networkx 的图结构 self.db = Database.get_instance() def connect_dot(self, concept1, concept2): # 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 else: # 如果是新边，初始化 strength 为 1 self.G.add_edge(concept1, concept2, strength=1) def add_dot(self, concept, memory): 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) else: self.G.nodes[concept]['memory_items'] = [memory] else: # 如果是新节点，创建新的记忆列表 self.G.add_node(concept, memory_items=[memory]) def get_dot(self, concept): # 检查节点是否存在于图中 if concept in self.G: # 从图中获取节点数据 node_data = self.G.nodes[concept] return concept, node_data return None def get_related_item(self, topic, depth=1): if topic not in self.G: return [], [] first_layer_items = [] second_layer_items = [] # 获取相邻节点 neighbors = list(self.G.neighbors(topic)) # 获取当前节点的记忆项 node_data = self.get_dot(topic) if node_data: concept, data = node_data if 'memory_items' in data: memory_items = data['memory_items'] if isinstance(memory_items, list): first_layer_items.extend(memory_items) else: first_layer_items.append(memory_items) # 只在depth=2时获取第二层记忆 if depth >= 2: # 获取相邻节点的记忆项 for neighbor in neighbors: node_data = self.get_dot(neighbor) if node_data: concept, data = node_data if 'memory_items' in data: memory_items = data['memory_items'] if isinstance(memory_items, list): second_layer_items.extend(memory_items) else: second_layer_items.append(memory_items) return first_layer_items, second_layer_items @property def dots(self): # 返回所有节点对应的 Memory_dot 对象 return [self.get_dot(node) for node in self.G.nodes()] def forget_topic(self, topic): """随机删除指定话题中的一条记忆，如果话题没有记忆则移除该话题节点""" if topic not in self.G: return None # 获取话题节点数据 node_data = self.G.nodes[topic] # 如果节点存在memory_items if 'memory_items' in node_data: memory_items = node_data['memory_items'] # 确保memory_items是列表 if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] # 如果有记忆项可以删除 if memory_items: # 随机选择一个记忆项删除 removed_item = random.choice(memory_items) memory_items.remove(removed_item) # 更新节点的记忆项 if memory_items: self.G.nodes[topic]['memory_items'] = memory_items else: # 如果没有记忆项了，删除整个节点 self.G.remove_node(topic) return removed_item return None # 海马体 class Hippocampus: def __init__(self,memory_graph:Memory_graph): self.memory_graph = memory_graph self.llm_model_get_topic = LLM_request(model = global_config.llm_normal_minor,temperature=0.5) self.llm_model_summary = LLM_request(model = global_config.llm_normal,temperature=0.5) def get_all_node_names(self) -> list: """获取记忆图中所有节点的名字列表 Returns: list: 包含所有节点名字的列表 """ return list(self.memory_graph.G.nodes()) def calculate_node_hash(self, concept, memory_items): """计算节点的特征值""" if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] sorted_items = sorted(memory_items) content = f"{concept}:{'|'.join(sorted_items)}" return hash(content) def calculate_edge_hash(self, source, target): """计算边的特征值""" nodes = sorted([source, target]) return hash(f"{nodes[0]}:{nodes[1]}") def get_memory_sample(self,chat_size=20,time_frequency:dict={'near':2,'mid':4,'far':3}): current_timestamp = datetime.datetime.now().timestamp() chat_text = [] #短期：1h 中期：4h 长期：24h for _ in range(time_frequency.get('near')): # 循环10次 random_time = current_timestamp - random.randint(1, 3600) # 随机时间 chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) chat_text.append(chat_) for _ in range(time_frequency.get('mid')): # 循环10次 random_time = current_timestamp - random.randint(3600, 3600*4) # 随机时间 chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) chat_text.append(chat_) for _ in range(time_frequency.get('far')): # 循环10次 random_time = current_timestamp - random.randint(3600*4, 3600*24) # 随机时间 chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) chat_text.append(chat_) return [text for text in chat_text if text] async def memory_compress(self, input_text, compress_rate=0.1): print(input_text) #获取topics topic_num = self.calculate_topic_num(input_text, compress_rate) topics_response = await self.llm_model_get_topic.generate_response(self.find_topic_llm(input_text, topic_num)) # 修改话题处理逻辑 # 定义需要过滤的关键词 filter_keywords = ['表情包', '图片', '回复', '聊天记录'] # 过滤topics topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()] filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)] # print(f"原始话题: {topics}") print(f"过滤后话题: {filtered_topics}") # 使用过滤后的话题继续处理 tasks = [] for topic in filtered_topics: topic_what_prompt = self.topic_what(input_text, topic) # 创建异步任务 task = self.llm_model_summary.generate_response_async(topic_what_prompt) tasks.append((topic.strip(), task)) # 等待所有任务完成 compressed_memory = set() for topic, task in tasks: response = await task if response: compressed_memory.add((topic, response[0])) return compressed_memory def calculate_topic_num(self,text, compress_rate): """计算文本的话题数量""" information_content = calculate_information_content(text) topic_by_length = text.count('\n')*compress_rate topic_by_information_content = max(1, min(5, int((information_content-3) * 2))) topic_num = int((topic_by_length + topic_by_information_content)/2) print(f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, topic_num: {topic_num}") 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 all_topics = [] progress = (i / len(memory_sample)) * 100 bar_length = 30 filled_length = int(bar_length * i // len(memory_sample)) bar = '█' * filled_length + '-' * (bar_length - filled_length) print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(memory_sample)})") # 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) print(f"\033[1;33m压缩后记忆数量\033[0m: {len(compressed_memory)}") # 将记忆加入到图谱中 for topic, memory in compressed_memory: print(f"\033[1;32m添加节点\033[0m: {topic}") self.memory_graph.add_dot(topic, memory) all_topics.append(topic) # 收集所有话题 for i in range(len(all_topics)): for j in range(i + 1, len(all_topics)): print(f"\033[1;32m连接节点\033[0m: {all_topics[i]} 和 {all_topics[j]}") self.memory_graph.connect_dot(all_topics[i], all_topics[j]) self.sync_memory_to_db() def sync_memory_to_db(self): """检查并同步内存中的图结构与数据库""" # 获取数据库中所有节点和内存中所有节点 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 db_nodes_dict = {node['concept']: node for node in db_nodes} # 检查并更新节点 for concept, data in memory_nodes: memory_items = data.get('memory_items', []) if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] # 计算内存中节点的特征值 memory_hash = self.calculate_node_hash(concept, memory_items) if concept not in db_nodes_dict: # 数据库中缺少的节点，添加 node_data = { 'concept': concept, 'memory_items': memory_items, 'hash': memory_hash } self.memory_graph.db.db.graph_data.nodes.insert_one(node_data) else: # 获取数据库中节点的特征值 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 if db_hash != memory_hash: self.memory_graph.db.db.graph_data.nodes.update_one( {'concept': concept}, {'$set': { 'memory_items': memory_items, 'hash': memory_hash }} ) # 检查并删除数据库中多余的节点 memory_concepts = set(node[0] for node in memory_nodes) for db_node in db_nodes: if db_node['concept'] not in memory_concepts: self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': db_node['concept']}) # 处理边的信息 db_edges = list(self.memory_graph.db.db.graph_data.edges.find()) memory_edges = list(self.memory_graph.G.edges()) # 创建边的哈希值字典 db_edge_dict = {} for edge in db_edges: edge_hash = self.calculate_edge_hash(edge['source'], edge['target']) db_edge_dict[(edge['source'], edge['target'])] = { 'hash': edge_hash, 'strength': edge.get('strength', 1) } # 检查并更新边 for source, target in memory_edges: edge_hash = self.calculate_edge_hash(source, target) edge_key = (source, target) strength = self.memory_graph.G[source][target].get('strength', 1) if edge_key not in db_edge_dict: # 添加新边 edge_data = { 'source': source, 'target': target, 'strength': strength, 'hash': edge_hash } self.memory_graph.db.db.graph_data.edges.insert_one(edge_data) else: # 检查边的特征值是否变化 if db_edge_dict[edge_key]['hash'] != edge_hash: self.memory_graph.db.db.graph_data.edges.update_one( {'source': source, 'target': target}, {'$set': { 'hash': edge_hash, 'strength': strength }} ) # 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 self.memory_graph.G.clear() # 从数据库加载所有节点 nodes = self.memory_graph.db.db.graph_data.nodes.find() for node in nodes: concept = node['concept'] memory_items = node.get('memory_items', []) # 确保memory_items是列表 if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] # 添加节点到图中 self.memory_graph.G.add_node(concept, memory_items=memory_items) # 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 # 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) 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 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 = [] 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)) print(f"遗忘节点 {node} 的记忆: {removed_item}") # 同步到数据库 if forgotten_nodes: self.sync_memory_to_db() print(f"完成遗忘操作，共遗忘 {len(forgotten_nodes)} 个节点的记忆") else: print("本次检查没有节点满足遗忘条件") async def merge_memory(self, topic): """ 对指定话题的记忆进行合并压缩 Args: topic: 要合并的话题节点 """ # 获取节点的记忆项 memory_items = self.memory_graph.G.nodes[topic].get('memory_items', []) if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] # 如果记忆项不足，直接返回 if len(memory_items) < 10: return # 随机选择10条记忆 selected_memories = random.sample(memory_items, 10) # 拼接成文本 merged_text = "\n".join(selected_memories) print(f"\n[合并记忆] 话题: {topic}") print(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(merged_text, 0.1) # 从原记忆列表中移除被选中的记忆 for memory in selected_memories: memory_items.remove(memory) # 添加新的压缩记忆 for _, compressed_memory in compressed_memories: memory_items.append(compressed_memory) print(f"添加压缩记忆: {compressed_memory}") # 更新节点的记忆项 self.memory_graph.G.nodes[topic]['memory_items'] = memory_items print(f"完成记忆合并，当前记忆数量: {len(memory_items)}") async def operation_merge_memory(self, percentage=0.1): """ 随机检查一定比例的节点，对内容数量超过100的节点进行记忆合并 Args: percentage: 要检查的节点比例，默认为0.1（10%） """ # 获取所有节点 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) merged_nodes = [] for node in nodes_to_check: # 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) # 如果内容数量超过100，进行合并 if content_count > 100: print(f"\n检查节点: {node}, 当前记忆数量: {content_count}") await self.merge_memory(node) merged_nodes.append(node) # 同步到数据库 if merged_nodes: self.sync_memory_to_db() print(f"\n完成记忆合并操作，共处理 {len(merged_nodes)} 个节点") else: print("\n本次检查没有需要合并的节点") def find_topic_llm(self,text, topic_num): prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。' return prompt def topic_what(self,text, topic): prompt = f'这是一段文字：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好' return prompt async def _identify_topics(self, text: str) -> list: """从文本中识别可能的主题 Args: text: 输入文本 Returns: list: 识别出的主题列表 """ topics_response = await self.llm_model_get_topic.generate_response(self.find_topic_llm(text, 5)) # print(f"话题: {topics_response[0]}") topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()] # print(f"话题: {topics}") return topics def _find_similar_topics(self, topics: list, similarity_threshold: float = 0.4, debug_info: str = "") -> list: """查找与给定主题相似的记忆主题 Args: topics: 主题列表 similarity_threshold: 相似度阈值 debug_info: 调试信息前缀 Returns: list: (主题, 相似度) 元组列表 """ all_memory_topics = self.get_all_node_names() all_similar_topics = [] # 计算每个识别出的主题与记忆主题的相似度 for topic in topics: if debug_info: print(f"\033[1;32m[{debug_info}]\033[0m 正在思考有没有见过: {topic}") topic_vector = text_to_vector(topic) has_similar_topic = False for memory_topic in all_memory_topics: memory_vector = text_to_vector(memory_topic) # 获取所有唯一词 all_words = set(topic_vector.keys()) | set(memory_vector.keys()) # 构建向量 v1 = [topic_vector.get(word, 0) for word in all_words] v2 = [memory_vector.get(word, 0) for word in all_words] # 计算相似度 similarity = cosine_similarity(v1, v2) if similarity >= similarity_threshold: has_similar_topic = True if debug_info: print(f"\033[1;32m[{debug_info}]\033[0m 找到相似主题: {topic} -> {memory_topic} (相似度: {similarity:.2f})") all_similar_topics.append((memory_topic, similarity)) if not has_similar_topic and debug_info: print(f"\033[1;31m[{debug_info}]\033[0m 没有见过: {topic} ，呃呃") return all_similar_topics def _get_top_topics(self, similar_topics: list, max_topics: int = 5) -> list: """获取相似度最高的主题 Args: similar_topics: (主题, 相似度) 元组列表 max_topics: 最大主题数量 Returns: list: (主题, 相似度) 元组列表 """ seen_topics = set() top_topics = [] for topic, score in sorted(similar_topics, key=lambda x: x[1], reverse=True): if topic not in seen_topics and len(top_topics) < max_topics: seen_topics.add(topic) top_topics.append((topic, score)) return top_topics async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int: """计算输入文本对记忆的激活程度""" print(f"\033[1;32m[记忆激活]\033[0m 开始计算文本的记忆激活度: {text}") # 识别主题 identified_topics = await self._identify_topics(text) print(f"\033[1;32m[记忆激活]\033[0m 识别出的主题: {identified_topics}") if not identified_topics: # print(f"\033[1;32m[记忆激活]\033[0m 未识别出主题,返回0") return 0 # 查找相似主题 all_similar_topics = self._find_similar_topics( identified_topics, similarity_threshold=similarity_threshold, debug_info="记忆激活" ) if not all_similar_topics: print(f"\033[1;32m[记忆激活]\033[0m 未找到相似主题,返回0") return 0 # 获取最相关的主题 top_topics = self._get_top_topics(all_similar_topics, max_topics) # 如果只找到一个主题，进行惩罚 if len(top_topics) == 1: topic, score = top_topics[0] activation = int(score * 50) # 单主题情况下，直接用相似度*50作为激活值 print(f"\033[1;32m[记忆激活]\033[0m 只找到一个主题，进行惩罚:") print(f"\033[1;32m[记忆激活]\033[0m - 主题: {topic}") print(f"\033[1;32m[记忆激活]\033[0m - 相似度: {score:.3f}") print(f"\033[1;32m[记忆激活]\033[0m - 最终激活值: {activation}") return activation # 计算关键词匹配率 matched_topics = set() topic_similarities = {} print(f"\033[1;32m[记忆激活]\033[0m 计算关键词匹配情况:") for memory_topic, similarity in top_topics: # 对每个记忆主题，检查它与哪些输入主题相似 for input_topic in identified_topics: topic_vector = text_to_vector(input_topic) memory_vector = text_to_vector(memory_topic) all_words = set(topic_vector.keys()) | set(memory_vector.keys()) v1 = [topic_vector.get(word, 0) for word in all_words] v2 = [memory_vector.get(word, 0) for word in all_words] sim = cosine_similarity(v1, v2) if sim >= similarity_threshold: matched_topics.add(input_topic) topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), sim) print(f"\033[1;32m[记忆激活]\033[0m - 输入主题「{input_topic}」匹配到记忆「{memory_topic}」, 相似度: {sim:.3f}") # 计算主题匹配率 topic_match = len(matched_topics) / len(identified_topics) print(f"\033[1;32m[记忆激活]\033[0m 主题匹配率:") print(f"\033[1;32m[记忆激活]\033[0m - 匹配主题数: {len(matched_topics)}") print(f"\033[1;32m[记忆激活]\033[0m - 总主题数: {len(identified_topics)}") print(f"\033[1;32m[记忆激活]\033[0m - 匹配率: {topic_match:.3f}") # 计算匹配主题的平均相似度 average_similarities = sum(topic_similarities.values()) / len(topic_similarities) if topic_similarities else 0 print(f"\033[1;32m[记忆激活]\033[0m 平均相似度:") print(f"\033[1;32m[记忆激活]\033[0m - 各主题相似度: {[f'{k}:{v:.3f}' for k,v in topic_similarities.items()]}") print(f"\033[1;32m[记忆激活]\033[0m - 平均相似度: {average_similarities:.3f}") # 计算最终激活值 activation = (topic_match + average_similarities) / 2 * 100 print(f"\033[1;32m[记忆激活]\033[0m 最终激活值: {int(activation)}") return int(activation) async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4, max_memory_num: int = 5) -> list: """根据输入文本获取相关的记忆内容""" # 识别主题 identified_topics = await self._identify_topics(text) # 查找相似主题 all_similar_topics = self._find_similar_topics( identified_topics, similarity_threshold=similarity_threshold, debug_info="记忆检索" ) # 获取最相关的主题 relevant_topics = self._get_top_topics(all_similar_topics, max_topics) # 获取相关记忆内容 relevant_memories = [] for topic, score in relevant_topics: # 获取该主题的记忆内容 first_layer, _ = self.memory_graph.get_related_item(topic, depth=1) if first_layer: # 如果记忆条数超过限制，随机选择指定数量的记忆 if len(first_layer) > max_memory_num/2: first_layer = random.sample(first_layer, max_memory_num) # 为每条记忆添加来源主题和相似度信息 for memory in first_layer: relevant_memories.append({ 'topic': topic, 'similarity': score, 'content': memory }) # 如果记忆数量超过5个,随机选择5个 # 按相似度排序 relevant_memories.sort(key=lambda x: x['similarity'], reverse=True) if len(relevant_memories) > max_memory_num: relevant_memories = random.sample(relevant_memories, max_memory_num) return relevant_memories def segment_text(text): seg_text = list(jieba.cut(text)) return seg_text from nonebot import get_driver driver = get_driver() config = driver.config start_time = time.time() Database.initialize( host= config.MONGODB_HOST, port= config.MONGODB_PORT, db_name= config.DATABASE_NAME, username= config.MONGODB_USERNAME, password= config.MONGODB_PASSWORD, auth_source=config.MONGODB_AUTH_SOURCE ) #创建记忆图 memory_graph = Memory_graph() #创建海马体 hippocampus = Hippocampus(memory_graph) #从数据库加载记忆图 hippocampus.sync_memory_from_db() end_time = time.time() print(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m")