fix(vector_store): 修复 ids 列表检查逻辑，避免 numpy 数组歧义

fix(memory_tools): 优化邻居节点数据检查逻辑，确保嵌入数据有效
fix(manager): 更新配置项名称为 search_max_expand_depth

src/memory_graph/manager.py

@@ -137,7 +137,7 @@ class MemoryManager:
                 graph_store=self.graph_store,
                 persistence_manager=self.persistence,
                 embedding_generator=self.embedding_generator,
-                max_expand_depth=getattr(self.config, 'max_expand_depth', 1),  # 从配置读取默认深度
+                max_expand_depth=getattr(self.config, 'search_max_expand_depth', 1),  # 从配置读取默认深度
             )
             
             self._initialized = True

src/memory_graph/storage/vector_store.py

@@ -210,15 +210,19 @@ class VectorStore:
             import orjson
             similar_nodes = []
             # 修复：检查 ids 列表长度而不是直接判断真值（避免 numpy 数组歧义）
-            if results.get("ids") is not None and len(results["ids"]) > 0 and len(results["ids"][0]) > 0:
-                for i, node_id in enumerate(results["ids"][0]):
+            ids = results.get("ids")
+            if ids is not None and len(ids) > 0 and len(ids[0]) > 0:
+                distances = results.get("distances")
+                metadatas = results.get("metadatas")
+                
+                for i, node_id in enumerate(ids[0]):
                     # ChromaDB 返回的是距离，需要转换为相似度
                     # 余弦距离: distance = 1 - similarity
-                    distance = results["distances"][0][i] if results["distances"] else 0.0  # type: ignore
+                    distance = distances[0][i] if distances is not None and len(distances) > 0 else 0.0  # type: ignore
                     similarity = 1.0 - distance
 
                     if similarity >= min_similarity:
-                        metadata = results["metadatas"][0][i] if results["metadatas"] else {}  # type: ignore
+                        metadata = metadatas[0][i] if metadatas is not None and len(metadatas) > 0 else {}  # type: ignore
                         
                         # 解析 JSON 字符串回列表/字典
                         for key, value in list(metadata.items()):
@@ -368,13 +372,18 @@ class VectorStore:
         try:
             result = self.collection.get(ids=[node_id], include=["metadatas", "embeddings"])
 
-            # 修复：检查 ids 列表长度而不是直接判断真值（避免 numpy 数组歧义）
-            if result and result.get("ids") is not None and len(result["ids"]) > 0:
-                return {
-                    "id": result["ids"][0],
-                    "metadata": result["metadatas"][0] if result["metadatas"] else {},
-                    "embedding": np.array(result["embeddings"][0]) if result["embeddings"] else None,
-                }
+            # 修复：直接检查 ids 列表是否非空（避免 numpy 数组的布尔值歧义）
+            if result is not None:
+                ids = result.get("ids")
+                if ids is not None and len(ids) > 0:
+                    metadatas = result.get("metadatas")
+                    embeddings = result.get("embeddings")
+                    
+                    return {
+                        "id": ids[0],
+                        "metadata": metadatas[0] if metadatas is not None and len(metadatas) > 0 else {},
+                        "embedding": np.array(embeddings[0]) if embeddings is not None and len(embeddings) > 0 and embeddings[0] is not None else None,
+                    }
 
             return None
 

src/memory_graph/tools/memory_tools.py

@@ -910,10 +910,12 @@ class MemoryTools:
                                 continue
                             
                             neighbor_vector_data = await self.vector_store.get_node_by_id(neighbor_id)
-                            if not neighbor_vector_data or neighbor_vector_data.get("embedding") is None:
+                            if neighbor_vector_data is None:
                                 continue
                             
-                            neighbor_embedding = neighbor_vector_data["embedding"]
+                            neighbor_embedding = neighbor_vector_data.get("embedding")
+                            if neighbor_embedding is None:
+                                continue
                             
                             # 计算语义相似度
                             semantic_sim = self._cosine_similarity(