Speed

`
def data_ingestion_indexing(directory_path):

    #constraint parameters
    max_input_size = 4096
    num_outputs = 512
    max_chunk_overlap = 20
    chunk_size_limit = 600

    #allows the user to explicitly set certain constraint parameters
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

    #LLMPredictor is a wrapper class around LangChain's LLMChain that allows easy integration into LlamaIndex
    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_outputs))

    #loads data from the specified directory path
    documents = SimpleDirectoryReader(directory_path).load_data()

    #constructs service_context
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)

    #when first building the index
    index = GPTVectorStoreIndex.from_documents(
        documents, service_context=service_context
    )

    #persist index to disk, default "storage" folder
    index.storage_context.persist()

    return index

def data_querying(input_text):

    #rebuild storage context
    storage_context = StorageContext.from_defaults(persist_dir="./storage")

    #loads index from storage
    index = load_index_from_storage(storage_context)
    
    #queries the index with the input text
    response = index.as_query_engine().query(input_text)
    
    return response.response

**********
Trace: query
    |_query ->  35.440498 seconds
      |_retrieve ->  0.140897 seconds
        |_embedding ->  0.125669 seconds
      |_synthesize ->  35.299303 seconds
        |_llm ->  35.281847 seconds
**********