尝试彻底修复_execute_request炸飞
This commit is contained in:
UnCLAS-Prommer
@@ -506,319 +506,6 @@ class EntorhinalCortex:
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logger.success(f"[数据库] 同步了 {len(memory_nodes)} 个节点和 {len(memory_edges)} 条边")
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# 负责整合，遗忘，合并记忆
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class ParahippocampalGyrus:
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def __init__(self, hippocampus):
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self.hippocampus = hippocampus
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self.memory_graph = hippocampus.memory_graph
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self.config = hippocampus.config
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async def memory_compress(self, messages: list, compress_rate=0.1):
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"""压缩和总结消息内容，生成记忆主题和摘要。
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Args:
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messages (list): 消息列表，每个消息是一个字典，包含以下字段：
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- time: float, 消息的时间戳
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- detailed_plain_text: str, 消息的详细文本内容
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compress_rate (float, optional): 压缩率，用于控制生成的主题数量。默认为0.1。
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Returns:
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tuple: (compressed_memory, similar_topics_dict)
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- compressed_memory: set, 压缩后的记忆集合，每个元素是一个元组 (topic, summary)
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- topic: str, 记忆主题
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- summary: str, 主题的摘要描述
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- similar_topics_dict: dict, 相似主题字典，key为主题，value为相似主题列表
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每个相似主题是一个元组 (similar_topic, similarity)
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- similar_topic: str, 相似的主题
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- similarity: float, 相似度分数（0-1之间）
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Process:
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1. 合并消息文本并生成时间信息
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2. 使用LLM提取关键主题
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3. 过滤掉包含禁用关键词的主题
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4. 为每个主题生成摘要
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5. 查找与现有记忆中的相似主题
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"""
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if not messages:
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return set(), {}
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# 合并消息文本，同时保留时间信息
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input_text = ""
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time_info = ""
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# 计算最早和最晚时间
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earliest_time = min(msg["time"] for msg in messages)
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latest_time = max(msg["time"] for msg in messages)
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earliest_dt = datetime.datetime.fromtimestamp(earliest_time)
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latest_dt = datetime.datetime.fromtimestamp(latest_time)
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# 如果是同一年
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if earliest_dt.year == latest_dt.year:
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earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S")
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latest_str = latest_dt.strftime("%m-%d %H:%M:%S")
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time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n"
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else:
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earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S")
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latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S")
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time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n"
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for msg in messages:
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input_text += f"{msg['detailed_plain_text']}\n"
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logger.debug(input_text)
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topic_num = self.hippocampus.calculate_topic_num(input_text, compress_rate)
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topics_response = await self.hippocampus.llm_topic_judge.generate_response(
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self.hippocampus.find_topic_llm(input_text, topic_num)
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)
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# 使用正则表达式提取<>中的内容
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topics = re.findall(r"<([^>]+)>", topics_response[0])
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# 如果没有找到<>包裹的内容，返回['none']
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if not topics:
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topics = ["none"]
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else:
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# 处理提取出的话题
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topics = [
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topic.strip()
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for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
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if topic.strip()
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]
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# 过滤掉包含禁用关键词的topic
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filtered_topics = [
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topic for topic in topics if not any(keyword in topic for keyword in self.config.memory_ban_words)
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]
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logger.debug(f"过滤后话题: {filtered_topics}")
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# 创建所有话题的请求任务
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tasks = []
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for topic in filtered_topics:
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topic_what_prompt = self.hippocampus.topic_what(input_text, topic, time_info)
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task = self.hippocampus.llm_summary_by_topic.generate_response_async(topic_what_prompt)
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tasks.append((topic.strip(), task))
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# 等待所有任务完成
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compressed_memory = set()
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similar_topics_dict = {}
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for topic, task in tasks:
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response = await task
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if response:
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compressed_memory.add((topic, response[0]))
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existing_topics = list(self.memory_graph.G.nodes())
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similar_topics = []
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for existing_topic in existing_topics:
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topic_words = set(jieba.cut(topic))
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existing_words = set(jieba.cut(existing_topic))
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all_words = topic_words | existing_words
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v1 = [1 if word in topic_words else 0 for word in all_words]
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v2 = [1 if word in existing_words else 0 for word in all_words]
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similarity = cosine_similarity(v1, v2)
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if similarity >= 0.7:
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similar_topics.append((existing_topic, similarity))
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similar_topics.sort(key=lambda x: x[1], reverse=True)
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similar_topics = similar_topics[:3]
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similar_topics_dict[topic] = similar_topics
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return compressed_memory, similar_topics_dict
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async def operation_build_memory(self):
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logger.debug("------------------------------------开始构建记忆--------------------------------------")
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start_time = time.time()
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memory_samples = self.hippocampus.entorhinal_cortex.get_memory_sample()
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all_added_nodes = []
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all_connected_nodes = []
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all_added_edges = []
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for i, messages in enumerate(memory_samples, 1):
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all_topics = []
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progress = (i / len(memory_samples)) * 100
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bar_length = 30
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filled_length = int(bar_length * i // len(memory_samples))
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bar = "█" * filled_length + "-" * (bar_length - filled_length)
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logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
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compress_rate = self.config.memory_compress_rate
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compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate)
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logger.debug(f"压缩后记忆数量: {compressed_memory}，似曾相识的话题: {similar_topics_dict}")
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current_time = datetime.datetime.now().timestamp()
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logger.debug(f"添加节点: {', '.join(topic for topic, _ in compressed_memory)}")
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all_added_nodes.extend(topic for topic, _ in compressed_memory)
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for topic, memory in compressed_memory:
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self.memory_graph.add_dot(topic, memory)
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all_topics.append(topic)
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if topic in similar_topics_dict:
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similar_topics = similar_topics_dict[topic]
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for similar_topic, similarity in similar_topics:
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if topic != similar_topic:
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strength = int(similarity * 10)
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logger.debug(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})")
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all_added_edges.append(f"{topic}-{similar_topic}")
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all_connected_nodes.append(topic)
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all_connected_nodes.append(similar_topic)
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self.memory_graph.G.add_edge(
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topic,
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similar_topic,
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strength=strength,
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created_time=current_time,
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last_modified=current_time,
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)
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for i in range(len(all_topics)):
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for j in range(i + 1, len(all_topics)):
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logger.debug(f"连接同批次节点: {all_topics[i]} 和 {all_topics[j]}")
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all_added_edges.append(f"{all_topics[i]}-{all_topics[j]}")
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self.memory_graph.connect_dot(all_topics[i], all_topics[j])
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logger.success(f"更新记忆: {', '.join(all_added_nodes)}")
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logger.debug(f"强化连接: {', '.join(all_added_edges)}")
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logger.info(f"强化连接节点: {', '.join(all_connected_nodes)}")
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await self.hippocampus.entorhinal_cortex.sync_memory_to_db()
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end_time = time.time()
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logger.success(f"---------------------记忆构建耗时: {end_time - start_time:.2f} 秒---------------------")
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async def operation_forget_topic(self, percentage=0.005):
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start_time = time.time()
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logger.info("[遗忘] 开始检查数据库...")
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# 验证百分比参数
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if not 0 <= percentage <= 1:
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logger.warning(f"[遗忘] 无效的遗忘百分比: {percentage}, 使用默认值 0.005")
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percentage = 0.005
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all_nodes = list(self.memory_graph.G.nodes())
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all_edges = list(self.memory_graph.G.edges())
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if not all_nodes and not all_edges:
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logger.info("[遗忘] 记忆图为空,无需进行遗忘操作")
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return
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# 确保至少检查1个节点和边，且不超过总数
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check_nodes_count = max(1, min(len(all_nodes), int(len(all_nodes) * percentage)))
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check_edges_count = max(1, min(len(all_edges), int(len(all_edges) * percentage)))
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# 只有在有足够的节点和边时才进行采样
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if len(all_nodes) >= check_nodes_count and len(all_edges) >= check_edges_count:
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try:
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nodes_to_check = random.sample(all_nodes, check_nodes_count)
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edges_to_check = random.sample(all_edges, check_edges_count)
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except ValueError as e:
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logger.error(f"[遗忘] 采样错误: {str(e)}")
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return
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else:
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logger.info("[遗忘] 没有足够的节点或边进行遗忘操作")
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return
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# 使用列表存储变化信息
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edge_changes = {
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"weakened": [], # 存储减弱的边
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"removed": [], # 存储移除的边
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}
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node_changes = {
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"reduced": [], # 存储减少记忆的节点
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"removed": [], # 存储移除的节点
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}
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current_time = datetime.datetime.now().timestamp()
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logger.info("[遗忘] 开始检查连接...")
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edge_check_start = time.time()
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for source, target in edges_to_check:
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edge_data = self.memory_graph.G[source][target]
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last_modified = edge_data.get("last_modified")
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if current_time - last_modified > 3600 * self.config.memory_forget_time:
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current_strength = edge_data.get("strength", 1)
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new_strength = current_strength - 1
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if new_strength <= 0:
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self.memory_graph.G.remove_edge(source, target)
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edge_changes["removed"].append(f"{source} -> {target}")
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else:
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edge_data["strength"] = new_strength
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edge_data["last_modified"] = current_time
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edge_changes["weakened"].append(f"{source}-{target} (强度: {current_strength} -> {new_strength})")
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edge_check_end = time.time()
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logger.info(f"[遗忘] 连接检查耗时: {edge_check_end - edge_check_start:.2f}秒")
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logger.info("[遗忘] 开始检查节点...")
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node_check_start = time.time()
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for node in nodes_to_check:
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node_data = self.memory_graph.G.nodes[node]
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last_modified = node_data.get("last_modified", current_time)
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if current_time - last_modified > 3600 * 24:
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memory_items = node_data.get("memory_items", [])
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if not isinstance(memory_items, list):
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memory_items = [memory_items] if memory_items else []
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if memory_items:
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current_count = len(memory_items)
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removed_item = random.choice(memory_items)
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memory_items.remove(removed_item)
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if memory_items:
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self.memory_graph.G.nodes[node]["memory_items"] = memory_items
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self.memory_graph.G.nodes[node]["last_modified"] = current_time
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node_changes["reduced"].append(f"{node} (数量: {current_count} -> {len(memory_items)})")
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else:
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self.memory_graph.G.remove_node(node)
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node_changes["removed"].append(node)
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node_check_end = time.time()
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logger.info(f"[遗忘] 节点检查耗时: {node_check_end - node_check_start:.2f}秒")
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if any(edge_changes.values()) or any(node_changes.values()):
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sync_start = time.time()
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await self.hippocampus.entorhinal_cortex.resync_memory_to_db()
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sync_end = time.time()
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logger.info(f"[遗忘] 数据库同步耗时: {sync_end - sync_start:.2f}秒")
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# 汇总输出所有变化
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logger.info("[遗忘] 遗忘操作统计:")
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if edge_changes["weakened"]:
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logger.info(
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f"[遗忘] 减弱的连接 ({len(edge_changes['weakened'])}个): {', '.join(edge_changes['weakened'])}"
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)
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if edge_changes["removed"]:
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logger.info(
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f"[遗忘] 移除的连接 ({len(edge_changes['removed'])}个): {', '.join(edge_changes['removed'])}"
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)
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if node_changes["reduced"]:
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logger.info(
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f"[遗忘] 减少记忆的节点 ({len(node_changes['reduced'])}个): {', '.join(node_changes['reduced'])}"
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)
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if node_changes["removed"]:
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logger.info(
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f"[遗忘] 移除的节点 ({len(node_changes['removed'])}个): {', '.join(node_changes['removed'])}"
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)
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else:
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logger.info("[遗忘] 本次检查没有节点或连接满足遗忘条件")
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end_time = time.time()
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logger.info(f"[遗忘] 总耗时: {end_time - start_time:.2f}秒")
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# 海马体
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class Hippocampus:
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def __init__(self):
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@@ -1247,6 +934,327 @@ class Hippocampus:
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return activation_ratio
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# 负责整合，遗忘，合并记忆
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class ParahippocampalGyrus:
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def __init__(self, hippocampus: Hippocampus):
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self.hippocampus = hippocampus
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self.memory_graph = hippocampus.memory_graph
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self.config = hippocampus.config
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async def memory_compress(self, messages: list, compress_rate=0.1):
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"""压缩和总结消息内容，生成记忆主题和摘要。
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Args:
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messages (list): 消息列表，每个消息是一个字典，包含以下字段：
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- time: float, 消息的时间戳
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- detailed_plain_text: str, 消息的详细文本内容
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compress_rate (float, optional): 压缩率，用于控制生成的主题数量。默认为0.1。
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Returns:
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tuple: (compressed_memory, similar_topics_dict)
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- compressed_memory: set, 压缩后的记忆集合，每个元素是一个元组 (topic, summary)
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- topic: str, 记忆主题
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- summary: str, 主题的摘要描述
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- similar_topics_dict: dict, 相似主题字典，key为主题，value为相似主题列表
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每个相似主题是一个元组 (similar_topic, similarity)
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- similar_topic: str, 相似的主题
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- similarity: float, 相似度分数（0-1之间）
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Process:
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1. 合并消息文本并生成时间信息
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2. 使用LLM提取关键主题
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3. 过滤掉包含禁用关键词的主题
|
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4. 为每个主题生成摘要
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5. 查找与现有记忆中的相似主题
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"""
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if not messages:
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return set(), {}
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# 合并消息文本，同时保留时间信息
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input_text = ""
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time_info = ""
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# 计算最早和最晚时间
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earliest_time = min(msg["time"] for msg in messages)
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latest_time = max(msg["time"] for msg in messages)
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earliest_dt = datetime.datetime.fromtimestamp(earliest_time)
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latest_dt = datetime.datetime.fromtimestamp(latest_time)
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# 如果是同一年
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if earliest_dt.year == latest_dt.year:
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earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S")
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latest_str = latest_dt.strftime("%m-%d %H:%M:%S")
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time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n"
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else:
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earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S")
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latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S")
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time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n"
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for msg in messages:
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input_text += f"{msg['detailed_plain_text']}\n"
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logger.debug(input_text)
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topic_num = self.hippocampus.calculate_topic_num(input_text, compress_rate)
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topics_response = await self.hippocampus.llm_topic_judge.generate_response(
|
||||
self.hippocampus.find_topic_llm(input_text, topic_num)
|
||||
)
|
||||
|
||||
# 使用正则表达式提取<>中的内容
|
||||
topics = re.findall(r"<([^>]+)>", topics_response[0])
|
||||
|
||||
# 如果没有找到<>包裹的内容，返回['none']
|
||||
if not topics:
|
||||
topics = ["none"]
|
||||
else:
|
||||
# 处理提取出的话题
|
||||
topics = [
|
||||
topic.strip()
|
||||
for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||||
if topic.strip()
|
||||
]
|
||||
|
||||
# 过滤掉包含禁用关键词的topic
|
||||
filtered_topics = [
|
||||
topic for topic in topics if not any(keyword in topic for keyword in self.config.memory_ban_words)
|
||||
]
|
||||
|
||||
logger.debug(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.hippocampus.topic_what(input_text, topic, time_info)
|
||||
try:
|
||||
task = self.hippocampus.llm_summary_by_topic.generate_response_async(topic_what_prompt)
|
||||
tasks.append((topic.strip(), task))
|
||||
except Exception as e:
|
||||
logger.error(f"生成话题 '{topic}' 的摘要时发生错误: {e}")
|
||||
continue
|
||||
|
||||
# 等待所有任务完成
|
||||
compressed_memory = set()
|
||||
similar_topics_dict = {}
|
||||
|
||||
for topic, task in tasks:
|
||||
response = await task
|
||||
if response:
|
||||
compressed_memory.add((topic, response[0]))
|
||||
|
||||
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 >= 0.7:
|
||||
similar_topics.append((existing_topic, similarity))
|
||||
|
||||
similar_topics.sort(key=lambda x: x[1], reverse=True)
|
||||
similar_topics = similar_topics[:3]
|
||||
similar_topics_dict[topic] = similar_topics
|
||||
|
||||
return compressed_memory, similar_topics_dict
|
||||
|
||||
async def operation_build_memory(self):
|
||||
logger.debug("------------------------------------开始构建记忆--------------------------------------")
|
||||
start_time = time.time()
|
||||
memory_samples = self.hippocampus.entorhinal_cortex.get_memory_sample()
|
||||
all_added_nodes = []
|
||||
all_connected_nodes = []
|
||||
all_added_edges = []
|
||||
for i, messages in enumerate(memory_samples, 1):
|
||||
all_topics = []
|
||||
progress = (i / len(memory_samples)) * 100
|
||||
bar_length = 30
|
||||
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_samples)})")
|
||||
|
||||
compress_rate = self.config.memory_compress_rate
|
||||
try:
|
||||
compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate)
|
||||
except Exception as e:
|
||||
logger.error(f"压缩记忆时发生错误: {e}")
|
||||
continue
|
||||
logger.debug(f"压缩后记忆数量: {compressed_memory}，似曾相识的话题: {similar_topics_dict}")
|
||||
|
||||
current_time = datetime.datetime.now().timestamp()
|
||||
logger.debug(f"添加节点: {', '.join(topic for topic, _ in compressed_memory)}")
|
||||
all_added_nodes.extend(topic for topic, _ in compressed_memory)
|
||||
|
||||
for topic, memory in compressed_memory:
|
||||
self.memory_graph.add_dot(topic, memory)
|
||||
all_topics.append(topic)
|
||||
|
||||
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.debug(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})")
|
||||
all_added_edges.append(f"{topic}-{similar_topic}")
|
||||
|
||||
all_connected_nodes.append(topic)
|
||||
all_connected_nodes.append(similar_topic)
|
||||
|
||||
self.memory_graph.G.add_edge(
|
||||
topic,
|
||||
similar_topic,
|
||||
strength=strength,
|
||||
created_time=current_time,
|
||||
last_modified=current_time,
|
||||
)
|
||||
|
||||
for i in range(len(all_topics)):
|
||||
for j in range(i + 1, len(all_topics)):
|
||||
logger.debug(f"连接同批次节点: {all_topics[i]} 和 {all_topics[j]}")
|
||||
all_added_edges.append(f"{all_topics[i]}-{all_topics[j]}")
|
||||
self.memory_graph.connect_dot(all_topics[i], all_topics[j])
|
||||
|
||||
logger.success(f"更新记忆: {', '.join(all_added_nodes)}")
|
||||
logger.debug(f"强化连接: {', '.join(all_added_edges)}")
|
||||
logger.info(f"强化连接节点: {', '.join(all_connected_nodes)}")
|
||||
|
||||
await self.hippocampus.entorhinal_cortex.sync_memory_to_db()
|
||||
|
||||
end_time = time.time()
|
||||
logger.success(f"---------------------记忆构建耗时: {end_time - start_time:.2f} 秒---------------------")
|
||||
|
||||
async def operation_forget_topic(self, percentage=0.005):
|
||||
start_time = time.time()
|
||||
logger.info("[遗忘] 开始检查数据库...")
|
||||
|
||||
# 验证百分比参数
|
||||
if not 0 <= percentage <= 1:
|
||||
logger.warning(f"[遗忘] 无效的遗忘百分比: {percentage}, 使用默认值 0.005")
|
||||
percentage = 0.005
|
||||
|
||||
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
|
||||
|
||||
# 确保至少检查1个节点和边，且不超过总数
|
||||
check_nodes_count = max(1, min(len(all_nodes), int(len(all_nodes) * percentage)))
|
||||
check_edges_count = max(1, min(len(all_edges), int(len(all_edges) * percentage)))
|
||||
|
||||
# 只有在有足够的节点和边时才进行采样
|
||||
if len(all_nodes) >= check_nodes_count and len(all_edges) >= check_edges_count:
|
||||
try:
|
||||
nodes_to_check = random.sample(all_nodes, check_nodes_count)
|
||||
edges_to_check = random.sample(all_edges, check_edges_count)
|
||||
except ValueError as e:
|
||||
logger.error(f"[遗忘] 采样错误: {str(e)}")
|
||||
return
|
||||
else:
|
||||
logger.info("[遗忘] 没有足够的节点或边进行遗忘操作")
|
||||
return
|
||||
|
||||
# 使用列表存储变化信息
|
||||
edge_changes = {
|
||||
"weakened": [], # 存储减弱的边
|
||||
"removed": [], # 存储移除的边
|
||||
}
|
||||
node_changes = {
|
||||
"reduced": [], # 存储减少记忆的节点
|
||||
"removed": [], # 存储移除的节点
|
||||
}
|
||||
|
||||
current_time = datetime.datetime.now().timestamp()
|
||||
|
||||
logger.info("[遗忘] 开始检查连接...")
|
||||
edge_check_start = time.time()
|
||||
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 * self.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"].append(f"{source} -> {target}")
|
||||
else:
|
||||
edge_data["strength"] = new_strength
|
||||
edge_data["last_modified"] = current_time
|
||||
edge_changes["weakened"].append(f"{source}-{target} (强度: {current_strength} -> {new_strength})")
|
||||
edge_check_end = time.time()
|
||||
logger.info(f"[遗忘] 连接检查耗时: {edge_check_end - edge_check_start:.2f}秒")
|
||||
|
||||
logger.info("[遗忘] 开始检查节点...")
|
||||
node_check_start = time.time()
|
||||
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:
|
||||
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"].append(f"{node} (数量: {current_count} -> {len(memory_items)})")
|
||||
else:
|
||||
self.memory_graph.G.remove_node(node)
|
||||
node_changes["removed"].append(node)
|
||||
node_check_end = time.time()
|
||||
logger.info(f"[遗忘] 节点检查耗时: {node_check_end - node_check_start:.2f}秒")
|
||||
|
||||
if any(edge_changes.values()) or any(node_changes.values()):
|
||||
sync_start = time.time()
|
||||
|
||||
await self.hippocampus.entorhinal_cortex.resync_memory_to_db()
|
||||
|
||||
sync_end = time.time()
|
||||
logger.info(f"[遗忘] 数据库同步耗时: {sync_end - sync_start:.2f}秒")
|
||||
|
||||
# 汇总输出所有变化
|
||||
logger.info("[遗忘] 遗忘操作统计:")
|
||||
if edge_changes["weakened"]:
|
||||
logger.info(
|
||||
f"[遗忘] 减弱的连接 ({len(edge_changes['weakened'])}个): {', '.join(edge_changes['weakened'])}"
|
||||
)
|
||||
|
||||
if edge_changes["removed"]:
|
||||
logger.info(
|
||||
f"[遗忘] 移除的连接 ({len(edge_changes['removed'])}个): {', '.join(edge_changes['removed'])}"
|
||||
)
|
||||
|
||||
if node_changes["reduced"]:
|
||||
logger.info(
|
||||
f"[遗忘] 减少记忆的节点 ({len(node_changes['reduced'])}个): {', '.join(node_changes['reduced'])}"
|
||||
)
|
||||
|
||||
if node_changes["removed"]:
|
||||
logger.info(
|
||||
f"[遗忘] 移除的节点 ({len(node_changes['removed'])}个): {', '.join(node_changes['removed'])}"
|
||||
)
|
||||
else:
|
||||
logger.info("[遗忘] 本次检查没有节点或连接满足遗忘条件")
|
||||
|
||||
end_time = time.time()
|
||||
logger.info(f"[遗忘] 总耗时: {end_time - start_time:.2f}秒")
|
||||
|
||||
|
||||
class HippocampusManager:
|
||||
_instance = None
|
||||
_hippocampus = None
|
||||
@@ -1317,12 +1325,13 @@ class HippocampusManager:
|
||||
if not self._initialized:
|
||||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||||
try:
|
||||
response = await self._hippocampus.get_memory_from_text(text, max_memory_num, max_memory_length, max_depth, fast_retrieval)
|
||||
response = await self._hippocampus.get_memory_from_text(
|
||||
text, max_memory_num, max_memory_length, max_depth, fast_retrieval
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"文本激活记忆失败: {e}")
|
||||
response = []
|
||||
return response
|
||||
|
||||
|
||||
async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> float:
|
||||
"""从文本中获取激活值的公共接口"""
|
||||
|
||||
Reference in New Issue
Block a user