.

rag_build_query.py

@@ -34,24 +34,40 @@ 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
+        try:
-        prefix = "Represent this sentence for searching relevant passages: "
+            # 在查询前加上前缀，生成嵌入向量
-        embedding = super()._get_query_embedding(prefix + query)
+            logger.info("Calling _get_query_embedding method...")
+            prefix = "Represent this sentence for searching relevant passages: "
+            embedding = super()._get_query_embedding(prefix + query)
 
-        # 使用logger打印数据类型
+            # 转换为 float32 类型
-        logger.info(f"Query embedding dtype: {embedding.dtype}")
+            embedding = np.array(embedding, dtype=np.float32)
-        return embedding
+
+            # 使用 logger 打印数据类型
+            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]]:
-        # 批量生成嵌入向量
+        try:
-        prefix = "Represent this sentence for searching relevant passages: "
+            # 批量生成嵌入向量
-        embeddings = super()._get_query_embeddings([prefix + q for q in queries])
+            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])
 
-        # 使用logger打印数据类型
+            # 转换为 float32 类型
-        logger.info(f"Batch query embeddings dtype: {embeddings[0].dtype}")
+            embeddings = [np.array(embedding, dtype=np.float32) for embedding in embeddings]
-        return embeddings
 
+            # 使用 logger 打印数据类型
+            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):
     logger.info(f"开始为用户 {user_id} 构建索引...")
 
@@ -81,7 +97,7 @@ def build_user_index(user_id: str):
     # 直接检查模型嵌入方法是否被调用
     logger.info(f"Embedding method being used: {embed_model._get_query_embedding('test query')}")
 
-    
+
     # 使用 Faiss 向量存储
     faiss_index = faiss.IndexFlatL2(1024)
     vector_store = FaissVectorStore(faiss_index=faiss_index)