vector_store = WeaviateVectorStore(weaviate_client=client)
        # print(dir(vector_store))
        storage_context = StorageContext.from_defaults(
            vector_store=vector_store)
        index = VectorStoreIndex.from_documents(
            documents,
            storage_context=storage_context
        )

        retriever = index.as_retriever(retriever_mode='embedding')
        query_engine = RetrieverQueryEngine.from_args(
            retriever, response_mode='no_text')

chat_engine = index.as_chat_engine()
response = chat_engine.query("What did the author do growing up?")
print("get_formatted_sources():", response.get_formatted_sources()) # <-- BLANK
# get_formatted_sources(): < -- BLANK HERE
print("metadata:", response.metadata)
# metadata: None
print("response:", response.response)
# response: Growing up, the author wrote short stories, programmed on an IBM 1401, and eventually convinced his father to buy him a TRS-80 microcomputer. He wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He studied philosophy in college, but eventually switched to AI. He wrote essays and published them online, and worked on spam filters and painting. He also hosted dinners for a group of friends every Thursday night and bought a building in Cambridge.
print("source_nodes:", response.source_nodes)
# source_nodes: []