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rag_build_query.py

@@ -34,22 +34,38 @@ 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]:
+        try:
             # 在查询前加上前缀，生成嵌入向量
+            logger.info("Calling _get_query_embedding method...")
             prefix = "Represent this sentence for searching relevant passages: "
             embedding = super()._get_query_embedding(prefix + query)
 
+            # 转换为 float32 类型
+            embedding = np.array(embedding, dtype=np.float32)
+
             # 使用 logger 打印数据类型
-        logger.info(f"Query embedding dtype: {embedding.dtype}")
+            logger.info(f"Query embedding dtype after conversion: {embedding.dtype}")
             return embedding
+        except Exception as e:
+            logger.error(f"Error in _get_query_embedding: {e}")
+            raise
 
     def _get_query_embeddings(self, queries: List[str]) -> List[List[float]]:
+        try:
             # 批量生成嵌入向量
+            logger.info("Calling _get_query_embeddings method...")
             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
+            embeddings = [np.array(embedding, dtype=np.float32) for embedding in embeddings]
+
             # 使用 logger 打印数据类型
-        logger.info(f"Batch query embeddings dtype: {embeddings[0].dtype}")
+            logger.info(f"Batch query embeddings dtype after conversion: {embeddings[0].dtype}")
             return embeddings
+        except Exception as e:
+            logger.error(f"Error in _get_query_embeddings: {e}")
+            raise
 
         
 def build_user_index(user_id: str):