Use llama index to reference back to the files which are being used to answer QUERIES

Question

import openai
    from llama_index import ServiceContext, GPTVectorStoreIndex, LLMPredictor, PromptHelper, SimpleDirectoryReader, load_index_from_storage, StorageContext
    from langchain import OpenAI
    import os
    import re

    drivepath = "C://Users//"

    # Set the API key
    os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
    openai.api_key = "YOUR_OPENAI_API_KEY"
    def construct_index(drivepath):
    # set maximum input size
        max_input_size = 4096
    # set number of output tokens
        num_outputs = 2000
    # set maximum chunk overlap
        max_chunk_overlap = 0.5
    # set chunk size limit
        chunk_size_limit = 600
    # define prompt helper
        prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
    # define LLM
        llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.05, model_name="text-davinci-003", max_tokens=num_outputs, top_p=0.1, frequency_penalty=0.8, verbose=None))

        documents = SimpleDirectoryReader(drivepath, recursive=True).load_data()
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor,  prompt_helper=prompt_helper)

        index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context)
        index.storage_context.persist(persist_dir="index")
        return index

    def get_response(user_input):
        storage_context = StorageContext.from_defaults(persist_dir="index")
        index = load_index_from_storage(storage_context)
        # index = GPTVectorStoreIndex.load_from_disk('index.json')
        query_engine = index.as_query_engine()
        streaming_response = query_engine.query(user_input)
        return streaming_response.response

    # construct_index('C://Users')

    while True:
        user_input = input("Enter your query: ")
        response = get_response(user_input)
        print('ANSWER:', response)

This code is for a chatbot. It is trained on pdfs in my directory. It gives answers pertaining to the files present in the directory. What I want is that it also returns me the file it is reading the answers from in the answers so that the user knows which file is used for the information being given.

The limitation is that I have to do it using llama_index library. I found this link llama_index get the document referenced from node_sources but it doesn't work for me. Kindly help me out.