Did you do any sort of benchmark between embeddings and direct inference?
How did you tune the distance for relevance, how did you decide on chunking and how many results would you include?
I want to try filtering using the distance of embeddings RAG style, but I wonder about how that changes the results
The current method the app uses feels pretty good, it does get you the best matches, and sometimes it also includes a few “extra” suggestions that typical filters would have left out, but that end up being potentially interesting matches