diff --git a/app/api/search.py b/app/api/search.py
index b62c9df..ae12f5f 100644
--- a/app/api/search.py
+++ b/app/api/search.py
@@ -57,7 +57,7 @@ def search_docs(request: QueryRequest, user_id: str = Query(..., description="
         logger.info("VectorStoreIndex loaded successfully.")
 
         # 将用户查询通过本地模型生成嵌入向量
-        query_vector = embedder._get_query_embedding(request.query)  # 使用本地模型生成查询的嵌入向量
+        query_vector = embedder.encode([request.query])  # 使用本地模型生成查询的嵌入向量
         logger.info(f"Generated query embedding: {query_vector}")
 
         # 使用 FaissVectorStore 检索最相似的节点
diff --git a/app/core/embedding.py b/app/core/embedding.py
index 86ddb8a..e3aa4ca 100644
--- a/app/core/embedding.py
+++ b/app/core/embedding.py
@@ -28,3 +28,4 @@ class BGEEmbedding:
         return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
 
 embedder = BGEEmbedding()
+