update code

libs/community/langchain_google_community/bq_storage_vectorstores/_base.py

@@ -573,28 +573,3 @@ def max_marginal_relevance_search_by_vector(
             np.array(embedding), doc_embeddings, lambda_mult=lambda_mult, k=k
         )
         return [doc_tuples[i][0] for i in mmr_doc_indexes]  # type: ignore[index]
-
-    def _convert_vector_store(self, vector_store_class, **kwargs: Any) -> Any:
-        """
-        Converts the current object's parameters into another Vector Store instance.
-
-        This method combines the base parameters of the current object to create a
-            `BigQueryVectorStore` or a `VertexFSVectorStore` object.
-
-        Args:
-            **kwargs: Additional keyword arguments to be passed to the `
-                class constructor. These override any matching
-                parameters in the base object.
-
-        Returns:
-            BaseBigQueryVectorStore: A child of `BaseBigQueryVectorStore` object ready
-                for vector search operations.
-
-        Raises:
-            ValueError: If any of the combined parameters are invalid
-        """
-        base_params = self.dict(include=BaseBigQueryVectorStore.__fields__.keys())
-        base_params["embedding"] = self.embedding
-        all_params = {**base_params, **kwargs}
-        bq_obj = vector_store_class(**all_params)
-        return bq_obj