better：优化心流
This commit is contained in:
SengokuCola
@@ -225,10 +225,438 @@ class Memory_graph:
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return None
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# 海马体
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class Hippocampus:
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def __init__(self):
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self.memory_graph = Memory_graph()
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self.llm_topic_judge = None
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self.llm_summary_by_topic = None
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self.entorhinal_cortex = None
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self.parahippocampal_gyrus = None
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self.config = None
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def initialize(self, global_config):
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self.config = MemoryConfig.from_global_config(global_config)
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# 初始化子组件
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self.entorhinal_cortex = EntorhinalCortex(self)
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self.parahippocampal_gyrus = ParahippocampalGyrus(self)
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# 从数据库加载记忆图
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self.entorhinal_cortex.sync_memory_from_db()
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self.llm_topic_judge = LLM_request(self.config.llm_topic_judge, request_type="memory")
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self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic, request_type="memory")
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def get_all_node_names(self) -> list:
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"""获取记忆图中所有节点的名字列表"""
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return list(self.memory_graph.G.nodes())
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def calculate_node_hash(self, concept, memory_items) -> int:
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"""计算节点的特征值"""
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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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sorted_items = sorted(memory_items)
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content = f"{concept}:{'|'.join(sorted_items)}"
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return hash(content)
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def calculate_edge_hash(self, source, target) -> int:
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"""计算边的特征值"""
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nodes = sorted([source, target])
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return hash(f"{nodes[0]}:{nodes[1]}")
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def find_topic_llm(self, text, topic_num):
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prompt = (
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f"这是一段文字：{text}。请你从这段话中总结出最多{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，"
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f"将主题用逗号隔开，并加上<>,例如<主题1>,<主题2>......尽可能精简。只需要列举最多{topic_num}个话题就好，不要有序号，不要告诉我其他内容。"
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f"如果确定找不出主题或者没有明显主题，返回<none>。"
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)
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return prompt
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def topic_what(self, text, topic, time_info):
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prompt = (
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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，'
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f"可以包含时间和人物，以及具体的观点。只输出这句话就好"
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)
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return prompt
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def calculate_topic_num(self, text, compress_rate):
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"""计算文本的话题数量"""
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information_content = calculate_information_content(text)
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topic_by_length = text.count("\n") * compress_rate
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topic_by_information_content = max(1, min(5, int((information_content - 3) * 2)))
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topic_num = int((topic_by_length + topic_by_information_content) / 2)
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logger.debug(
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f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, "
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f"topic_num: {topic_num}"
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)
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return topic_num
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def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
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"""从关键词获取相关记忆。
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Args:
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keyword (str): 关键词
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max_depth (int, optional): 记忆检索深度，默认为2。1表示只获取直接相关的记忆，2表示获取间接相关的记忆。
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Returns:
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list: 记忆列表，每个元素是一个元组 (topic, memory_items, similarity)
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- topic: str, 记忆主题
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- memory_items: list, 该主题下的记忆项列表
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- similarity: float, 与关键词的相似度
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"""
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if not keyword:
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return []
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# 获取所有节点
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all_nodes = list(self.memory_graph.G.nodes())
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memories = []
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# 计算关键词的词集合
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keyword_words = set(jieba.cut(keyword))
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# 遍历所有节点，计算相似度
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for node in all_nodes:
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node_words = set(jieba.cut(node))
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all_words = keyword_words | node_words
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v1 = [1 if word in keyword_words else 0 for word in all_words]
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v2 = [1 if word in node_words else 0 for word in all_words]
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similarity = cosine_similarity(v1, v2)
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# 如果相似度超过阈值，获取该节点的记忆
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if similarity >= 0.3: # 可以调整这个阈值
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node_data = self.memory_graph.G.nodes[node]
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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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memories.append((node, memory_items, similarity))
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# 按相似度降序排序
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memories.sort(key=lambda x: x[2], reverse=True)
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return memories
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async def get_memory_from_text(
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self,
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text: str,
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max_memory_num: int = 3,
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max_memory_length: int = 2,
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max_depth: int = 3,
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fast_retrieval: bool = False,
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) -> list:
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"""从文本中提取关键词并获取相关记忆。
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Args:
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text (str): 输入文本
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num (int, optional): 需要返回的记忆数量。默认为5。
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max_depth (int, optional): 记忆检索深度。默认为2。
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fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
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如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。
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如果为False，使用LLM提取关键词，速度较慢但更准确。
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Returns:
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list: 记忆列表，每个元素是一个元组 (topic, memory_items, similarity)
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- topic: str, 记忆主题
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- memory_items: list, 该主题下的记忆项列表
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- similarity: float, 与文本的相似度
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"""
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if not text:
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return []
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if fast_retrieval:
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# 使用jieba分词提取关键词
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words = jieba.cut(text)
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# 过滤掉停用词和单字词
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keywords = [word for word in words if len(word) > 1]
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# 去重
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keywords = list(set(keywords))
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# 限制关键词数量
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keywords = keywords[:5]
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else:
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# 使用LLM提取关键词
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topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
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# logger.info(f"提取关键词数量: {topic_num}")
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topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
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# 提取关键词
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keywords = re.findall(r"<([^>]+)>", topics_response[0])
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if not keywords:
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keywords = []
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else:
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keywords = [
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keyword.strip()
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for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
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if keyword.strip()
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]
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# logger.info(f"提取的关键词: {', '.join(keywords)}")
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# 过滤掉不存在于记忆图中的关键词
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valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
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if not valid_keywords:
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logger.info("没有找到有效的关键词节点")
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return []
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logger.info(f"有效的关键词: {', '.join(valid_keywords)}")
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# 从每个关键词获取记忆
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all_memories = []
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activate_map = {} # 存储每个词的累计激活值
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# 对每个关键词进行扩散式检索
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for keyword in valid_keywords:
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logger.debug(f"开始以关键词 '{keyword}' 为中心进行扩散检索 (最大深度: {max_depth}):")
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# 初始化激活值
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activation_values = {keyword: 1.0}
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# 记录已访问的节点
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visited_nodes = {keyword}
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# 待处理的节点队列，每个元素是(节点, 激活值, 当前深度)
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nodes_to_process = [(keyword, 1.0, 0)]
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while nodes_to_process:
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current_node, current_activation, current_depth = nodes_to_process.pop(0)
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# 如果激活值小于0或超过最大深度，停止扩散
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if current_activation <= 0 or current_depth >= max_depth:
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continue
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# 获取当前节点的所有邻居
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neighbors = list(self.memory_graph.G.neighbors(current_node))
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for neighbor in neighbors:
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if neighbor in visited_nodes:
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continue
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# 获取连接强度
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edge_data = self.memory_graph.G[current_node][neighbor]
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strength = edge_data.get("strength", 1)
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# 计算新的激活值
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new_activation = current_activation - (1 / strength)
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if new_activation > 0:
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activation_values[neighbor] = new_activation
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visited_nodes.add(neighbor)
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nodes_to_process.append((neighbor, new_activation, current_depth + 1))
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logger.debug(
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f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})"
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) # noqa: E501
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# 更新激活映射
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for node, activation_value in activation_values.items():
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if activation_value > 0:
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if node in activate_map:
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activate_map[node] += activation_value
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else:
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activate_map[node] = activation_value
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# 输出激活映射
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# logger.info("激活映射统计:")
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# for node, total_activation in sorted(activate_map.items(), key=lambda x: x[1], reverse=True):
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# logger.info(f"节点 '{node}': 累计激活值 = {total_activation:.2f}")
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# 基于激活值平方的独立概率选择
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remember_map = {}
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# logger.info("基于激活值平方的归一化选择:")
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# 计算所有激活值的平方和
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total_squared_activation = sum(activation**2 for activation in activate_map.values())
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if total_squared_activation > 0:
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# 计算归一化的激活值
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normalized_activations = {
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node: (activation**2) / total_squared_activation for node, activation in activate_map.items()
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}
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# 按归一化激活值排序并选择前max_memory_num个
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sorted_nodes = sorted(normalized_activations.items(), key=lambda x: x[1], reverse=True)[:max_memory_num]
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# 将选中的节点添加到remember_map
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for node, normalized_activation in sorted_nodes:
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remember_map[node] = activate_map[node] # 使用原始激活值
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logger.debug(
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f"节点 '{node}' (归一化激活值: {normalized_activation:.2f}, 激活值: {activate_map[node]:.2f})"
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)
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else:
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logger.info("没有有效的激活值")
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# 从选中的节点中提取记忆
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all_memories = []
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# logger.info("开始从选中的节点中提取记忆:")
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for node, activation in remember_map.items():
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logger.debug(f"处理节点 '{node}' (激活值: {activation:.2f}):")
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node_data = self.memory_graph.G.nodes[node]
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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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logger.debug(f"节点包含 {len(memory_items)} 条记忆")
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# 计算每条记忆与输入文本的相似度
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memory_similarities = []
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for memory in memory_items:
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# 计算与输入文本的相似度
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memory_words = set(jieba.cut(memory))
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text_words = set(jieba.cut(text))
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all_words = memory_words | text_words
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v1 = [1 if word in memory_words else 0 for word in all_words]
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v2 = [1 if word in text_words else 0 for word in all_words]
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similarity = cosine_similarity(v1, v2)
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memory_similarities.append((memory, similarity))
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# 按相似度排序
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memory_similarities.sort(key=lambda x: x[1], reverse=True)
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# 获取最匹配的记忆
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top_memories = memory_similarities[:max_memory_length]
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# 添加到结果中
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for memory, similarity in top_memories:
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all_memories.append((node, [memory], similarity))
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# logger.info(f"选中记忆: {memory} (相似度: {similarity:.2f})")
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else:
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logger.info("节点没有记忆")
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# 去重（基于记忆内容）
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logger.debug("开始记忆去重:")
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seen_memories = set()
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unique_memories = []
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for topic, memory_items, activation_value in all_memories:
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memory = memory_items[0] # 因为每个topic只有一条记忆
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if memory not in seen_memories:
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seen_memories.add(memory)
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unique_memories.append((topic, memory_items, activation_value))
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logger.debug(f"保留记忆: {memory} (来自节点: {topic}, 激活值: {activation_value:.2f})")
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else:
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logger.debug(f"跳过重复记忆: {memory} (来自节点: {topic})")
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# 转换为(关键词, 记忆)格式
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result = []
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for topic, memory_items, _ in unique_memories:
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memory = memory_items[0] # 因为每个topic只有一条记忆
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result.append((topic, memory))
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logger.info(f"选中记忆: {memory} (来自节点: {topic})")
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return result
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async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> float:
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"""从文本中提取关键词并获取相关记忆。
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Args:
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text (str): 输入文本
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num (int, optional): 需要返回的记忆数量。默认为5。
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max_depth (int, optional): 记忆检索深度。默认为2。
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fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
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如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。
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如果为False，使用LLM提取关键词，速度较慢但更准确。
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Returns:
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float: 激活节点数与总节点数的比值
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"""
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if not text:
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return 0
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if fast_retrieval:
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# 使用jieba分词提取关键词
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words = jieba.cut(text)
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# 过滤掉停用词和单字词
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keywords = [word for word in words if len(word) > 1]
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# 去重
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keywords = list(set(keywords))
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# 限制关键词数量
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keywords = keywords[:5]
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else:
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# 使用LLM提取关键词
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topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
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# logger.info(f"提取关键词数量: {topic_num}")
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topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
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# 提取关键词
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keywords = re.findall(r"<([^>]+)>", topics_response[0])
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if not keywords:
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keywords = []
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else:
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keywords = [
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keyword.strip()
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for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
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if keyword.strip()
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]
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# logger.info(f"提取的关键词: {', '.join(keywords)}")
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# 过滤掉不存在于记忆图中的关键词
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valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
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if not valid_keywords:
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logger.info("没有找到有效的关键词节点")
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return 0
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logger.info(f"有效的关键词: {', '.join(valid_keywords)}")
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# 从每个关键词获取记忆
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activate_map = {} # 存储每个词的累计激活值
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# 对每个关键词进行扩散式检索
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for keyword in valid_keywords:
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logger.debug(f"开始以关键词 '{keyword}' 为中心进行扩散检索 (最大深度: {max_depth}):")
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# 初始化激活值
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activation_values = {keyword: 1.0}
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# 记录已访问的节点
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visited_nodes = {keyword}
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# 待处理的节点队列，每个元素是(节点, 激活值, 当前深度)
|
||||
nodes_to_process = [(keyword, 1.0, 0)]
|
||||
|
||||
while nodes_to_process:
|
||||
current_node, current_activation, current_depth = nodes_to_process.pop(0)
|
||||
|
||||
# 如果激活值小于0或超过最大深度，停止扩散
|
||||
if current_activation <= 0 or current_depth >= max_depth:
|
||||
continue
|
||||
|
||||
# 获取当前节点的所有邻居
|
||||
neighbors = list(self.memory_graph.G.neighbors(current_node))
|
||||
|
||||
for neighbor in neighbors:
|
||||
if neighbor in visited_nodes:
|
||||
continue
|
||||
|
||||
# 获取连接强度
|
||||
edge_data = self.memory_graph.G[current_node][neighbor]
|
||||
strength = edge_data.get("strength", 1)
|
||||
|
||||
# 计算新的激活值
|
||||
new_activation = current_activation - (1 / strength)
|
||||
|
||||
if new_activation > 0:
|
||||
activation_values[neighbor] = new_activation
|
||||
visited_nodes.add(neighbor)
|
||||
nodes_to_process.append((neighbor, new_activation, current_depth + 1))
|
||||
# logger.debug(
|
||||
# f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})") # noqa: E501
|
||||
|
||||
# 更新激活映射
|
||||
for node, activation_value in activation_values.items():
|
||||
if activation_value > 0:
|
||||
if node in activate_map:
|
||||
activate_map[node] += activation_value
|
||||
else:
|
||||
activate_map[node] = activation_value
|
||||
|
||||
# 输出激活映射
|
||||
# logger.info("激活映射统计:")
|
||||
# for node, total_activation in sorted(activate_map.items(), key=lambda x: x[1], reverse=True):
|
||||
# logger.info(f"节点 '{node}': 累计激活值 = {total_activation:.2f}")
|
||||
|
||||
# 计算激活节点数与总节点数的比值
|
||||
total_activation = sum(activate_map.values())
|
||||
logger.info(f"总激活值: {total_activation:.2f}")
|
||||
total_nodes = len(self.memory_graph.G.nodes())
|
||||
# activated_nodes = len(activate_map)
|
||||
activation_ratio = total_activation / total_nodes if total_nodes > 0 else 0
|
||||
activation_ratio = activation_ratio * 60
|
||||
logger.info(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
|
||||
|
||||
return activation_ratio
|
||||
|
||||
|
||||
|
||||
# 负责海马体与其他部分的交互
|
||||
class EntorhinalCortex:
|
||||
def __init__(self, hippocampus):
|
||||
def __init__(self, hippocampus: Hippocampus):
|
||||
self.hippocampus = hippocampus
|
||||
self.memory_graph = hippocampus.memory_graph
|
||||
self.config = hippocampus.config
|
||||
@@ -819,433 +1247,6 @@ class ParahippocampalGyrus:
|
||||
logger.info(f"[遗忘] 总耗时: {end_time - start_time:.2f}秒")
|
||||
|
||||
|
||||
# 海马体
|
||||
class Hippocampus:
|
||||
def __init__(self):
|
||||
self.memory_graph = Memory_graph()
|
||||
self.llm_topic_judge = None
|
||||
self.llm_summary_by_topic = None
|
||||
self.entorhinal_cortex = None
|
||||
self.parahippocampal_gyrus = None
|
||||
self.config = None
|
||||
|
||||
def initialize(self, global_config):
|
||||
self.config = MemoryConfig.from_global_config(global_config)
|
||||
# 初始化子组件
|
||||
self.entorhinal_cortex = EntorhinalCortex(self)
|
||||
self.parahippocampal_gyrus = ParahippocampalGyrus(self)
|
||||
# 从数据库加载记忆图
|
||||
self.entorhinal_cortex.sync_memory_from_db()
|
||||
self.llm_topic_judge = LLM_request(self.config.llm_topic_judge, request_type="memory")
|
||||
self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic, request_type="memory")
|
||||
|
||||
def get_all_node_names(self) -> list:
|
||||
"""获取记忆图中所有节点的名字列表"""
|
||||
return list(self.memory_graph.G.nodes())
|
||||
|
||||
def calculate_node_hash(self, concept, memory_items) -> int:
|
||||
"""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) -> int:
|
||||
"""计算边的特征值"""
|
||||
nodes = sorted([source, target])
|
||||
return hash(f"{nodes[0]}:{nodes[1]}")
|
||||
|
||||
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最多{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，"
|
||||
f"将主题用逗号隔开，并加上<>,例如<主题1>,<主题2>......尽可能精简。只需要列举最多{topic_num}个话题就好，不要有序号，不要告诉我其他内容。"
|
||||
f"如果确定找不出主题或者没有明显主题，返回<none>。"
|
||||
)
|
||||
return prompt
|
||||
|
||||
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，'
|
||||
f"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
|
||||
|
||||
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)
|
||||
logger.debug(
|
||||
f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, "
|
||||
f"topic_num: {topic_num}"
|
||||
)
|
||||
return topic_num
|
||||
|
||||
def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
|
||||
"""从关键词获取相关记忆。
|
||||
|
||||
Args:
|
||||
keyword (str): 关键词
|
||||
max_depth (int, optional): 记忆检索深度，默认为2。1表示只获取直接相关的记忆，2表示获取间接相关的记忆。
|
||||
|
||||
Returns:
|
||||
list: 记忆列表，每个元素是一个元组 (topic, memory_items, similarity)
|
||||
- topic: str, 记忆主题
|
||||
- memory_items: list, 该主题下的记忆项列表
|
||||
- similarity: float, 与关键词的相似度
|
||||
"""
|
||||
if not keyword:
|
||||
return []
|
||||
|
||||
# 获取所有节点
|
||||
all_nodes = list(self.memory_graph.G.nodes())
|
||||
memories = []
|
||||
|
||||
# 计算关键词的词集合
|
||||
keyword_words = set(jieba.cut(keyword))
|
||||
|
||||
# 遍历所有节点，计算相似度
|
||||
for node in all_nodes:
|
||||
node_words = set(jieba.cut(node))
|
||||
all_words = keyword_words | node_words
|
||||
v1 = [1 if word in keyword_words else 0 for word in all_words]
|
||||
v2 = [1 if word in node_words else 0 for word in all_words]
|
||||
similarity = cosine_similarity(v1, v2)
|
||||
|
||||
# 如果相似度超过阈值，获取该节点的记忆
|
||||
if similarity >= 0.3: # 可以调整这个阈值
|
||||
node_data = self.memory_graph.G.nodes[node]
|
||||
memory_items = node_data.get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
|
||||
memories.append((node, memory_items, similarity))
|
||||
|
||||
# 按相似度降序排序
|
||||
memories.sort(key=lambda x: x[2], reverse=True)
|
||||
return memories
|
||||
|
||||
async def get_memory_from_text(
|
||||
self,
|
||||
text: str,
|
||||
max_memory_num: int = 3,
|
||||
max_memory_length: int = 2,
|
||||
max_depth: int = 3,
|
||||
fast_retrieval: bool = False,
|
||||
) -> list:
|
||||
"""从文本中提取关键词并获取相关记忆。
|
||||
|
||||
Args:
|
||||
text (str): 输入文本
|
||||
num (int, optional): 需要返回的记忆数量。默认为5。
|
||||
max_depth (int, optional): 记忆检索深度。默认为2。
|
||||
fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
|
||||
如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。
|
||||
如果为False，使用LLM提取关键词，速度较慢但更准确。
|
||||
|
||||
Returns:
|
||||
list: 记忆列表，每个元素是一个元组 (topic, memory_items, similarity)
|
||||
- topic: str, 记忆主题
|
||||
- memory_items: list, 该主题下的记忆项列表
|
||||
- similarity: float, 与文本的相似度
|
||||
"""
|
||||
if not text:
|
||||
return []
|
||||
|
||||
if fast_retrieval:
|
||||
# 使用jieba分词提取关键词
|
||||
words = jieba.cut(text)
|
||||
# 过滤掉停用词和单字词
|
||||
keywords = [word for word in words if len(word) > 1]
|
||||
# 去重
|
||||
keywords = list(set(keywords))
|
||||
# 限制关键词数量
|
||||
keywords = keywords[:5]
|
||||
else:
|
||||
# 使用LLM提取关键词
|
||||
topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
|
||||
# logger.info(f"提取关键词数量: {topic_num}")
|
||||
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
|
||||
|
||||
# 提取关键词
|
||||
keywords = re.findall(r"<([^>]+)>", topics_response[0])
|
||||
if not keywords:
|
||||
keywords = []
|
||||
else:
|
||||
keywords = [
|
||||
keyword.strip()
|
||||
for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||||
if keyword.strip()
|
||||
]
|
||||
|
||||
# logger.info(f"提取的关键词: {', '.join(keywords)}")
|
||||
|
||||
# 过滤掉不存在于记忆图中的关键词
|
||||
valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
|
||||
if not valid_keywords:
|
||||
logger.info("没有找到有效的关键词节点")
|
||||
return []
|
||||
|
||||
logger.info(f"有效的关键词: {', '.join(valid_keywords)}")
|
||||
|
||||
# 从每个关键词获取记忆
|
||||
all_memories = []
|
||||
activate_map = {} # 存储每个词的累计激活值
|
||||
|
||||
# 对每个关键词进行扩散式检索
|
||||
for keyword in valid_keywords:
|
||||
logger.debug(f"开始以关键词 '{keyword}' 为中心进行扩散检索 (最大深度: {max_depth}):")
|
||||
# 初始化激活值
|
||||
activation_values = {keyword: 1.0}
|
||||
# 记录已访问的节点
|
||||
visited_nodes = {keyword}
|
||||
# 待处理的节点队列，每个元素是(节点, 激活值, 当前深度)
|
||||
nodes_to_process = [(keyword, 1.0, 0)]
|
||||
|
||||
while nodes_to_process:
|
||||
current_node, current_activation, current_depth = nodes_to_process.pop(0)
|
||||
|
||||
# 如果激活值小于0或超过最大深度，停止扩散
|
||||
if current_activation <= 0 or current_depth >= max_depth:
|
||||
continue
|
||||
|
||||
# 获取当前节点的所有邻居
|
||||
neighbors = list(self.memory_graph.G.neighbors(current_node))
|
||||
|
||||
for neighbor in neighbors:
|
||||
if neighbor in visited_nodes:
|
||||
continue
|
||||
|
||||
# 获取连接强度
|
||||
edge_data = self.memory_graph.G[current_node][neighbor]
|
||||
strength = edge_data.get("strength", 1)
|
||||
|
||||
# 计算新的激活值
|
||||
new_activation = current_activation - (1 / strength)
|
||||
|
||||
if new_activation > 0:
|
||||
activation_values[neighbor] = new_activation
|
||||
visited_nodes.add(neighbor)
|
||||
nodes_to_process.append((neighbor, new_activation, current_depth + 1))
|
||||
logger.debug(
|
||||
f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})"
|
||||
) # noqa: E501
|
||||
|
||||
# 更新激活映射
|
||||
for node, activation_value in activation_values.items():
|
||||
if activation_value > 0:
|
||||
if node in activate_map:
|
||||
activate_map[node] += activation_value
|
||||
else:
|
||||
activate_map[node] = activation_value
|
||||
|
||||
# 输出激活映射
|
||||
# logger.info("激活映射统计:")
|
||||
# for node, total_activation in sorted(activate_map.items(), key=lambda x: x[1], reverse=True):
|
||||
# logger.info(f"节点 '{node}': 累计激活值 = {total_activation:.2f}")
|
||||
|
||||
# 基于激活值平方的独立概率选择
|
||||
remember_map = {}
|
||||
# logger.info("基于激活值平方的归一化选择:")
|
||||
|
||||
# 计算所有激活值的平方和
|
||||
total_squared_activation = sum(activation**2 for activation in activate_map.values())
|
||||
if total_squared_activation > 0:
|
||||
# 计算归一化的激活值
|
||||
normalized_activations = {
|
||||
node: (activation**2) / total_squared_activation for node, activation in activate_map.items()
|
||||
}
|
||||
|
||||
# 按归一化激活值排序并选择前max_memory_num个
|
||||
sorted_nodes = sorted(normalized_activations.items(), key=lambda x: x[1], reverse=True)[:max_memory_num]
|
||||
|
||||
# 将选中的节点添加到remember_map
|
||||
for node, normalized_activation in sorted_nodes:
|
||||
remember_map[node] = activate_map[node] # 使用原始激活值
|
||||
logger.debug(
|
||||
f"节点 '{node}' (归一化激活值: {normalized_activation:.2f}, 激活值: {activate_map[node]:.2f})"
|
||||
)
|
||||
else:
|
||||
logger.info("没有有效的激活值")
|
||||
|
||||
# 从选中的节点中提取记忆
|
||||
all_memories = []
|
||||
# logger.info("开始从选中的节点中提取记忆:")
|
||||
for node, activation in remember_map.items():
|
||||
logger.debug(f"处理节点 '{node}' (激活值: {activation:.2f}):")
|
||||
node_data = self.memory_graph.G.nodes[node]
|
||||
memory_items = node_data.get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
|
||||
if memory_items:
|
||||
logger.debug(f"节点包含 {len(memory_items)} 条记忆")
|
||||
# 计算每条记忆与输入文本的相似度
|
||||
memory_similarities = []
|
||||
for memory in memory_items:
|
||||
# 计算与输入文本的相似度
|
||||
memory_words = set(jieba.cut(memory))
|
||||
text_words = set(jieba.cut(text))
|
||||
all_words = memory_words | text_words
|
||||
v1 = [1 if word in memory_words else 0 for word in all_words]
|
||||
v2 = [1 if word in text_words else 0 for word in all_words]
|
||||
similarity = cosine_similarity(v1, v2)
|
||||
memory_similarities.append((memory, similarity))
|
||||
|
||||
# 按相似度排序
|
||||
memory_similarities.sort(key=lambda x: x[1], reverse=True)
|
||||
# 获取最匹配的记忆
|
||||
top_memories = memory_similarities[:max_memory_length]
|
||||
|
||||
# 添加到结果中
|
||||
for memory, similarity in top_memories:
|
||||
all_memories.append((node, [memory], similarity))
|
||||
# logger.info(f"选中记忆: {memory} (相似度: {similarity:.2f})")
|
||||
else:
|
||||
logger.info("节点没有记忆")
|
||||
|
||||
# 去重（基于记忆内容）
|
||||
logger.debug("开始记忆去重:")
|
||||
seen_memories = set()
|
||||
unique_memories = []
|
||||
for topic, memory_items, activation_value in all_memories:
|
||||
memory = memory_items[0] # 因为每个topic只有一条记忆
|
||||
if memory not in seen_memories:
|
||||
seen_memories.add(memory)
|
||||
unique_memories.append((topic, memory_items, activation_value))
|
||||
logger.debug(f"保留记忆: {memory} (来自节点: {topic}, 激活值: {activation_value:.2f})")
|
||||
else:
|
||||
logger.debug(f"跳过重复记忆: {memory} (来自节点: {topic})")
|
||||
|
||||
# 转换为(关键词, 记忆)格式
|
||||
result = []
|
||||
for topic, memory_items, _ in unique_memories:
|
||||
memory = memory_items[0] # 因为每个topic只有一条记忆
|
||||
result.append((topic, memory))
|
||||
logger.info(f"选中记忆: {memory} (来自节点: {topic})")
|
||||
|
||||
return result
|
||||
|
||||
async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> float:
|
||||
"""从文本中提取关键词并获取相关记忆。
|
||||
|
||||
Args:
|
||||
text (str): 输入文本
|
||||
num (int, optional): 需要返回的记忆数量。默认为5。
|
||||
max_depth (int, optional): 记忆检索深度。默认为2。
|
||||
fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
|
||||
如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。
|
||||
如果为False，使用LLM提取关键词，速度较慢但更准确。
|
||||
|
||||
Returns:
|
||||
float: 激活节点数与总节点数的比值
|
||||
"""
|
||||
if not text:
|
||||
return 0
|
||||
|
||||
if fast_retrieval:
|
||||
# 使用jieba分词提取关键词
|
||||
words = jieba.cut(text)
|
||||
# 过滤掉停用词和单字词
|
||||
keywords = [word for word in words if len(word) > 1]
|
||||
# 去重
|
||||
keywords = list(set(keywords))
|
||||
# 限制关键词数量
|
||||
keywords = keywords[:5]
|
||||
else:
|
||||
# 使用LLM提取关键词
|
||||
topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
|
||||
# logger.info(f"提取关键词数量: {topic_num}")
|
||||
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
|
||||
|
||||
# 提取关键词
|
||||
keywords = re.findall(r"<([^>]+)>", topics_response[0])
|
||||
if not keywords:
|
||||
keywords = []
|
||||
else:
|
||||
keywords = [
|
||||
keyword.strip()
|
||||
for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||||
if keyword.strip()
|
||||
]
|
||||
|
||||
# logger.info(f"提取的关键词: {', '.join(keywords)}")
|
||||
|
||||
# 过滤掉不存在于记忆图中的关键词
|
||||
valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
|
||||
if not valid_keywords:
|
||||
logger.info("没有找到有效的关键词节点")
|
||||
return 0
|
||||
|
||||
logger.info(f"有效的关键词: {', '.join(valid_keywords)}")
|
||||
|
||||
# 从每个关键词获取记忆
|
||||
activate_map = {} # 存储每个词的累计激活值
|
||||
|
||||
# 对每个关键词进行扩散式检索
|
||||
for keyword in valid_keywords:
|
||||
logger.debug(f"开始以关键词 '{keyword}' 为中心进行扩散检索 (最大深度: {max_depth}):")
|
||||
# 初始化激活值
|
||||
activation_values = {keyword: 1.0}
|
||||
# 记录已访问的节点
|
||||
visited_nodes = {keyword}
|
||||
# 待处理的节点队列，每个元素是(节点, 激活值, 当前深度)
|
||||
nodes_to_process = [(keyword, 1.0, 0)]
|
||||
|
||||
while nodes_to_process:
|
||||
current_node, current_activation, current_depth = nodes_to_process.pop(0)
|
||||
|
||||
# 如果激活值小于0或超过最大深度，停止扩散
|
||||
if current_activation <= 0 or current_depth >= max_depth:
|
||||
continue
|
||||
|
||||
# 获取当前节点的所有邻居
|
||||
neighbors = list(self.memory_graph.G.neighbors(current_node))
|
||||
|
||||
for neighbor in neighbors:
|
||||
if neighbor in visited_nodes:
|
||||
continue
|
||||
|
||||
# 获取连接强度
|
||||
edge_data = self.memory_graph.G[current_node][neighbor]
|
||||
strength = edge_data.get("strength", 1)
|
||||
|
||||
# 计算新的激活值
|
||||
new_activation = current_activation - (1 / strength)
|
||||
|
||||
if new_activation > 0:
|
||||
activation_values[neighbor] = new_activation
|
||||
visited_nodes.add(neighbor)
|
||||
nodes_to_process.append((neighbor, new_activation, current_depth + 1))
|
||||
# logger.debug(
|
||||
# f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})") # noqa: E501
|
||||
|
||||
# 更新激活映射
|
||||
for node, activation_value in activation_values.items():
|
||||
if activation_value > 0:
|
||||
if node in activate_map:
|
||||
activate_map[node] += activation_value
|
||||
else:
|
||||
activate_map[node] = activation_value
|
||||
|
||||
# 输出激活映射
|
||||
# logger.info("激活映射统计:")
|
||||
# for node, total_activation in sorted(activate_map.items(), key=lambda x: x[1], reverse=True):
|
||||
# logger.info(f"节点 '{node}': 累计激活值 = {total_activation:.2f}")
|
||||
|
||||
# 计算激活节点数与总节点数的比值
|
||||
total_activation = sum(activate_map.values())
|
||||
logger.info(f"总激活值: {total_activation:.2f}")
|
||||
total_nodes = len(self.memory_graph.G.nodes())
|
||||
# activated_nodes = len(activate_map)
|
||||
activation_ratio = total_activation / total_nodes if total_nodes > 0 else 0
|
||||
activation_ratio = activation_ratio * 60
|
||||
logger.info(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
|
||||
|
||||
return activation_ratio
|
||||
|
||||
|
||||
class HippocampusManager:
|
||||
_instance = None
|
||||
|
||||
Reference in New Issue
Block a user