fix：尝试优化pfc复读

src/plugins/memory_system/Hippocampus.py

@@ -63,7 +63,8 @@ def calculate_information_content(text):
     """计算文本的信息量（熵）"""
     char_count = Counter(text)
     total_chars = len(text)
-
+    if total_chars == 0:
+        return 0
     entropy = 0
     for count in char_count.values():
         probability = count / total_chars
@@ -1256,6 +1257,174 @@ class Hippocampus:
             logger.info(f"选中记忆: {memory} (来自节点: {topic})")
 
         return result
+    
+    async def get_memory_from_topic(
+        self,
+        keywords: list[str],
+        max_memory_num: int = 3,
+        max_memory_length: int = 2,
+        max_depth: int = 3,
+    ) -> list:
+        """从文本中提取关键词并获取相关记忆。
+
+        Args:
+            topic (str): 记忆主题
+            max_memory_num (int, optional): 返回的记忆条目数量上限。默认为3，表示最多返回3条与输入文本相关度最高的记忆。
+            max_memory_length (int, optional): 每个主题最多返回的记忆条目数量。默认为2，表示每个主题最多返回2条相似度最高的记忆。
+            max_depth (int, optional): 记忆检索深度。默认为3。值越大，检索范围越广，可以获取更多间接相关的记忆，但速度会变慢。
+
+        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 keywords:
+            return []
+
+        # 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.trace(
+                            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
+
+
+        # 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(keywords)
+                    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。
@@ -1772,6 +1941,26 @@ class HippocampusManager:
             logger.error(f"文本激活记忆失败: {e}")
             response = []
         return response
+    
+    async def get_memory_from_topic(
+        self,
+        valid_keywords: list[str],
+        max_memory_num: int = 3,
+        max_memory_length: int = 2,
+        max_depth: int = 3,
+        fast_retrieval: bool = False,
+    ) -> list:
+        """从文本中获取相关记忆的公共接口"""
+        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_topic(
+                valid_keywords, 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"""