How to save the indexes being created in

def get_query_engine(llm, filename):
    # use Huggingface embeddings

    embed_model = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-mpnet-base-v2"
    )
    # create a service context
    service_context = ServiceContext.from_defaults(
        llm=llm,
        embed_model=embed_model,
    )

    # set_global_service_context(service_context)

    # # load documents
    documents = SimpleDirectoryReader(
        input_files = [f"./docs/{filename}"]
    ).load_data()

    # documents = SimpleDirectoryReader("./test_docs").load_data()
    
    # # create vector store index
    index = VectorStoreIndex.from_documents(documents, service_context=service_context)

    index.storage_context.persist("./test_indexcache_one/")

    # set up query engine
    query_engine = index.as_query_engine(
        streaming=True,
        similarity_top_k=1
    )
    return query_engine