我对使用llama-index库来训练GPT-3以及通过标准API使用ChatGPT（都是在Python中）还是相当陌生的。我注意到，标准ChatGPT API我可以简单地执行以下代码，让ChatGPT获取消息历史作为上下文：

message_history=[]
completion = openai.ChatCompletion.create(model="gpt-3.5-turbo",messages=message_history)

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现在我正在使用llama-index库在更具体的上下文上训练GPT-3，但是我不知道如何让模型考虑message_history以及，这里是我目前正在编写的一个代码，我不知道如何实现message history：

def construct_index(directory_path):
    # set maximum input size
    max_input_size = 4096
    # set number of output tokens
    num_outputs = 2000
    # set maximum chunk overlap
    max_chunk_overlap = 20
    # 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=0.5, model_name="text-ada-001", max_tokens=num_outputs))
    # define context (dataset)
    documents = SimpleDirectoryReader(directory_path).load_data()
    # transform context to index format
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
    # important: index are are like map, has latitutdes and logntitudes to indicate how each city (texts) are close to each other
    index.save_to_disk("index.json")
    return index

index = GPTSimpleVectorIndex.load_from_disk("index.json")
dbutils.widgets.text("user_input", "user: ")
response = index.query(dbutils.widgets.get("user_input"),response_mode='compact')
print("Response: ", response.response)

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