from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, SummaryPromptfrom lla

1. Use a smaller model: If you're using a large language model like gpt-3.5-turbo, you might want to consider using a smaller one. Smaller models usually generate responses faster, although the quality of the responses might be slightly lower.

1. Reduce the context window: If you're using a large context window, reducing it might speed up the response time. However, this might also affect the quality of the responses, especially if the relevant information is located outside the reduced context window.

1. Use streaming: If you're not already using it, consider enabling streaming. This allows the response to be generated in chunks, which might speed up the overall response time. Here's how you can enable streaming:

query_engine = index.as_query_engine(streaming=True)
response = query_engine.query("What did the author do growing up?")
response.print_response_stream()

1. Use a different response mode: If you're using a complex response mode like tree_summarize, consider using a simpler one like retrieve. Simpler response modes usually generate responses faster, although the quality of the responses might be slightly lower.

query_engine = index.as_query_engine(response_mode='retrieve')
response = query_engine.query("What did the author do growing up?")
print(response)

Define your custom prompt

Load your documents

Use the custom prompt with TreeIndex

Configure the query engine with the custom prompt

1. Use a smaller model: If you're using a large language model like gpt-3.5-turbo, you might want to consider using a smaller one. Smaller models usually generate responses faster, although the quality of the responses might be slightly lower.

1. Reduce the context window: If you're using a large context window, reducing it might speed up the response time. However, this might also affect the quality of the responses, especially if the relevant information is located outside the reduced context window.

1. Use a different response mode: If you're using a complex response mode like tree_summarize, consider using a simpler one like retrieve. Simpler response modes usually generate responses faster, although the quality of the responses might be slightly lower.

query_engine = index_with_query.as_query_engine(response_mode='retrieve')
response = query_engine.query("What did the author do growing up?")
print(response)