diff --git a/bot.py b/bot.py index 84ce5067b..c9e397724 100644 --- a/bot.py +++ b/bot.py @@ -149,6 +149,7 @@ if __name__ == "__main__": init_config() init_env() load_env() + load_logger() env_config = {key: os.getenv(key) for key in os.environ} scan_provider(env_config) diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index 49963ad3b..02fccc863 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -162,7 +162,7 @@ class BotConfig: personality_config = parent['personality'] personality = personality_config.get('prompt_personality') if len(personality) >= 2: - logger.info(f"载入自定义人格:{personality}") + logger.debug(f"载入自定义人格:{personality}") config.PROMPT_PERSONALITY = personality_config.get('prompt_personality', config.PROMPT_PERSONALITY) logger.info(f"载入自定义日程prompt:{personality_config.get('prompt_schedule', config.PROMPT_SCHEDULE_GEN)}") config.PROMPT_SCHEDULE_GEN = personality_config.get('prompt_schedule', config.PROMPT_SCHEDULE_GEN) diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py index 08fc6d30c..8e917caf4 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -7,6 +7,7 @@ import time import jieba import networkx as nx +from loguru import logger from ...common.database import Database # 使用正确的导入语法 from ..chat.config import global_config from ..chat.utils import ( @@ -22,7 +23,7 @@ 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): @@ -30,7 +31,7 @@ class Memory_graph: 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 @@ -44,7 +45,7 @@ class Memory_graph: 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: @@ -56,13 +57,13 @@ class Memory_graph: 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: @@ -73,7 +74,7 @@ class Memory_graph: 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 @@ -87,9 +88,9 @@ class Memory_graph: 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 @@ -99,43 +100,43 @@ class Memory_graph: """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): + def __init__(self, memory_graph: Memory_graph): self.memory_graph = memory_graph - self.llm_topic_judge = LLM_request(model = global_config.llm_topic_judge,temperature=0.5) - self.llm_summary_by_topic = LLM_request(model = global_config.llm_summary_by_topic,temperature=0.5) - + self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge, temperature=0.5) + self.llm_summary_by_topic = LLM_request(model=global_config.llm_summary_by_topic, 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 @@ -156,8 +157,8 @@ class Hippocampus: """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}): + + def get_memory_sample(self, chat_size=20, time_frequency: dict = {'near': 2, 'mid': 4, 'far': 3}): """获取记忆样本 Returns: @@ -165,26 +166,26 @@ class Hippocampus: """ current_timestamp = datetime.datetime.now().timestamp() chat_samples = [] - + # 短期：1h 中期：4h 长期：24h for _ in range(time_frequency.get('near')): random_time = current_timestamp - random.randint(1, 3600) messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) if messages: chat_samples.append(messages) - + for _ in range(time_frequency.get('mid')): - random_time = current_timestamp - random.randint(3600, 3600*4) + random_time = current_timestamp - random.randint(3600, 3600 * 4) messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) if messages: chat_samples.append(messages) - + for _ in range(time_frequency.get('far')): - random_time = current_timestamp - random.randint(3600*4, 3600*24) + random_time = current_timestamp - random.randint(3600 * 4, 3600 * 24) messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) if messages: chat_samples.append(messages) - + return chat_samples async def memory_compress(self, messages: list, compress_rate=0.1): @@ -199,17 +200,17 @@ class Hippocampus: """ if not messages: return set() - + # 合并消息文本，同时保留时间信息 input_text = "" time_info = "" # 计算最早和最晚时间 earliest_time = min(msg['time'] for msg in messages) latest_time = max(msg['time'] for msg in messages) - + earliest_dt = datetime.datetime.fromtimestamp(earliest_time) latest_dt = datetime.datetime.fromtimestamp(latest_time) - + # 如果是同一年 if earliest_dt.year == latest_dt.year: earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S") @@ -217,54 +218,56 @@ class Hippocampus: time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n" else: earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S") - latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S") + latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S") time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n" - + for msg in messages: input_text += f"{msg['text']}\n" - + print(input_text) - + topic_num = self.calculate_topic_num(input_text, compress_rate) topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(input_text, topic_num)) - + # 过滤topics filter_keywords = global_config.memory_ban_words - topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()] + 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"过滤后话题: {filtered_topics}") - + # 创建所有话题的请求任务 tasks = [] for topic in filtered_topics: topic_what_prompt = self.topic_what(input_text, topic, time_info) task = self.llm_summary_by_topic.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): + 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}") + 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): + 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) - + 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 = [] @@ -279,7 +282,7 @@ class Hippocampus: 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}") @@ -289,7 +292,7 @@ class Hippocampus: 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): @@ -297,19 +300,19 @@ class Hippocampus: # 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 = { @@ -322,7 +325,7 @@ class Hippocampus: # 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( @@ -332,17 +335,17 @@ class Hippocampus: '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: @@ -351,13 +354,13 @@ class Hippocampus: '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 = { @@ -377,7 +380,7 @@ class Hippocampus: 'strength': strength }} ) - + # 删除多余的边 memory_edge_set = set(memory_edges) for edge_key in db_edge_dict: @@ -392,7 +395,7 @@ class Hippocampus: """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: @@ -403,7 +406,7 @@ class Hippocampus: 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: @@ -413,7 +416,7 @@ class Hippocampus: # 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 @@ -422,18 +425,18 @@ class Hippocampus: 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 @@ -442,14 +445,14 @@ class Hippocampus: 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() @@ -468,35 +471,35 @@ class Hippocampus: 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(selected_memories, 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 @@ -510,7 +513,7 @@ class Hippocampus: 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 @@ -518,13 +521,13 @@ class Hippocampus: 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() @@ -532,11 +535,11 @@ class Hippocampus: else: print("\n本次检查没有需要合并的节点") - def find_topic_llm(self,text, topic_num): + 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, time_info): + def topic_what(self, text, topic, time_info): prompt = f'这是一段文字，{time_info}：{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 @@ -551,11 +554,12 @@ class Hippocampus: """ topics_response = await self.llm_topic_judge.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()] + 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 @@ -569,16 +573,16 @@ class Hippocampus: """ 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}") pass - + 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 @@ -588,20 +592,20 @@ class Hippocampus: 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})") pass 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") pass - + 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 @@ -614,36 +618,36 @@ class Hippocampus: """ 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 识别主题: {await self._identify_topics(text)}") - + # 识别主题 identified_topics = await self._identify_topics(text) if not identified_topics: return 0 - + # 查找相似主题 all_similar_topics = self._find_similar_topics( - identified_topics, + identified_topics, similarity_threshold=similarity_threshold, debug_info="记忆激活" ) - + if not all_similar_topics: 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] @@ -653,15 +657,16 @@ class Hippocampus: memory_items = [memory_items] if memory_items else [] content_count = len(memory_items) penalty = 1.0 / (1 + math.log(content_count + 1)) - + activation = int(score * 50 * penalty) - print(f"\033[1;32m[记忆激活]\033[0m 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}") + print( + f"\033[1;32m[记忆激活]\033[0m 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}") return activation - + # 计算关键词匹配率，同时考虑内容数量 matched_topics = set() topic_similarities = {} - + for memory_topic, similarity in top_topics: # 计算内容数量惩罚 memory_items = self.memory_graph.G.nodes[memory_topic].get('memory_items', []) @@ -669,7 +674,7 @@ class Hippocampus: memory_items = [memory_items] if memory_items else [] content_count = len(memory_items) penalty = 1.0 / (1 + math.log(content_count + 1)) - + # 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) @@ -682,33 +687,36 @@ class Hippocampus: matched_topics.add(input_topic) adjusted_sim = sim * penalty topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim) - print(f"\033[1;32m[记忆激活]\033[0m 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})") - + print( + f"\033[1;32m[记忆激活]\033[0m 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})") + # 计算主题匹配率和平均相似度 topic_match = len(matched_topics) / len(identified_topics) average_similarities = sum(topic_similarities.values()) / len(topic_similarities) if topic_similarities else 0 - + # 计算最终激活值 activation = int((topic_match + average_similarities) / 2 * 100) - print(f"\033[1;32m[记忆激活]\033[0m 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}") - + print( + f"\033[1;32m[记忆激活]\033[0m 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}") + return activation - async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4, max_memory_num: int = 5) -> list: + 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, + 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: @@ -716,8 +724,8 @@ class Hippocampus: 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//2) + if len(first_layer) > max_memory_num / 2: + first_layer = random.sample(first_layer, max_memory_num // 2) # 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({ @@ -725,20 +733,20 @@ class Hippocampus: '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 + return seg_text from nonebot import get_driver @@ -749,19 +757,19 @@ 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, + 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 +# 创建记忆图 memory_graph = Memory_graph() -#创建海马体 +# 创建海马体 hippocampus = Hippocampus(memory_graph) -#从数据库加载记忆图 +# 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") \ No newline at end of file +logger.success(f"加载海马体耗时: {end_time - start_time:.2f} 秒")