diff --git a/scripts/rag_build_query.py b/scripts/rag_build_query.py
index bc28d29..c268217 100644
--- a/scripts/rag_build_query.py
+++ b/scripts/rag_build_query.py
@@ -22,19 +22,6 @@ logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
 
-# # BGEEmbedding 类，继承自 HuggingFaceEmbedding，用于生成查询的嵌入
-# class BGEEmbedding(HuggingFaceEmbedding):
-#     def _get_query_embedding(self, query: str) -> List[float]:
-#         # 在查询前加上前缀，生成嵌入向量
-#         prefix = "Represent this sentence for searching relevant passages: "
-#         return super()._get_query_embedding(prefix + query)
-
-#     def _get_query_embeddings(self, queries: List[str]) -> List[List[float]]:
-#         # 批量生成嵌入向量
-#         prefix = "Represent this sentence for searching relevant passages: "
-#         return super()._get_query_embeddings([prefix + q for q in queries])
-
-
 # BGEEmbedding 类，继承自 HuggingFaceEmbedding，用于生成查询的嵌入
 class BGEEmbedding(HuggingFaceEmbedding): 
     def _get_query_embedding(self, query: str) -> List[float]:
@@ -44,14 +31,14 @@ class BGEEmbedding(HuggingFaceEmbedding):
             prefix = "Represent this sentence for searching relevant passages: "
             embedding = super()._get_query_embedding(prefix + query)
 
-            # 转换为 float32 类型
+            # 转换为 numpy 数组并记录 dtype
             embedding = np.array(embedding, dtype=np.float32)
 
-            # 将 numpy 数组转换为列表，确保 embedding 是一个列表
-            embedding = embedding.tolist()
-
-            # 使用 logger 打印数据类型
+            # 使用 logger 打印数据类型（dtype 在 numpy 数组上有效）
             logger.info(f"Query embedding dtype after conversion: {embedding.dtype}")
+
+            # 转换为列表返回
+            embedding = embedding.tolist()  # 转换为 Python 列表
             return embedding
         except Exception as e:
             logger.error(f"Error in _get_query_embedding: {e}")
@@ -64,17 +51,23 @@ class BGEEmbedding(HuggingFaceEmbedding):
             prefix = "Represent this sentence for searching relevant passages: "
             embeddings = super()._get_query_embeddings([prefix + q for q in queries])
 
-            # 转换为 float32 类型并转换为列表
-            embeddings = [np.array(embedding, dtype=np.float32).tolist() for embedding in embeddings]
+            # 转换为 numpy 数组并记录 dtype
+            embeddings = [np.array(embedding, dtype=np.float32) for embedding in embeddings]
 
-            # 使用 logger 打印数据类型
+            # 使用 logger 打印数据类型（dtype 在 numpy 数组上有效）
             logger.info(f"Batch query embeddings dtype after conversion: {embeddings[0].dtype}")
+
+            # 转换为列表返回
+            embeddings = [embedding.tolist() for embedding in embeddings]
             return embeddings
         except Exception as e:
             logger.error(f"Error in _get_query_embeddings: {e}")
             raise
 
-        
+
+
+
+
 def build_user_index(user_id: str):
     logger.info(f"开始为用户 {user_id} 构建索引...")