author here - apologies for it not being clear. the idea here is:
step 1: sparse index retrieval (FTS/BM25) - say with k = 10
step 2: re-rank the 10 records using embeddings
the difference in this approach is during step 2, you convert text to embeddings on the fly - when you're running the retrieval pipeline, meaning you don't need to have all of your corpus pre-embedded in a vector db
