The approach used here for breaking down large documents into summarized chunks that can more easily be reasoned about is how a lot of AI systems deal with large documents that surpass effective context limits in-general, but in my experience this approach will only work up to a certain point and then the summaries will start to hide enough detail that you do need semantic search or another RAG approach like GraphRAG. I think the efficacy of this approach will really fall apart after a certain number of documents.
Would've loved to seen the author run experiments about how they compare to other RAG approaches or what the limitations are to this one.
Thanks, that’s a great point! That’s why we use the tree structure, which can search layer by layer without putting the whole tree into the context (to compromise the summary quality). We’ll update with more examples and experiments on this. Thanks for the suggestion!