>"Retrieval based on reasoning — say goodbye to approximate semantic search ("vibe retrieval" How is this not precisely "vibe retrieval" and much more approximate, where approximate in this case is uncertainty over the precise reasoning? Similarity with conversion to high-dimensional vectors and then something like kNN seems significantly less approximate, less "vibe" based, than this. This also appears to be complet…
It is just as "vibe-ish" as vector search and notably does require chunking (document chunks are fed to the indexer to build the table of contents). That said, I don't find vector search any less "vibey". While "mathematical similarity" is a structured operation, the "conversion to high-dimensional vectors" part is predicated on the encoder, which can be trained towards any objective. > scaling will become problemati…
{json.dumps(tree_without_text, indent=2)}
The pageIndex value add here is ostensibly the creation of that summary structure, but this too is done with LLM assistance. I've been through the code now, and what I see is essentially JSON creation and parsing during the index process that has LLM prompts as the creation engine for all of that as well.
Yes, it is technically vectorless-RAG, but it gets there completely and totally with iterative and recursive calls to an LLM on all sides.
Looking through the rest of their code & API, the API exists to do these things:
1: Create your ToC using unsupervised[1] LLM calls.
2: Serve your ToC to an LLM when searching or querying your doc base
3: Be your document store to return hits from #2