gpt_index does that. A tree of document chunks (leafs) is built with parent nodes as increasingly summarized versions of the child nodes built with GPT. The tr

gpt_index does that. A tree of document chunks (leafs) is built with parent nodes as increasingly summarized versions of the child nodes built with GPT. The tree is then traversed to find the most relevant chunk asking GPT to compare entries based on relevance to the question. This results in an original document chunk, which is given as context in a final prompt asking to answer the query. This is great and powerful, but very not cost effective. Log(n) requests to completion API, for n documents. The embedding search is probably necessary for bigger datasets.