use case · multi-model
The two-stage pipeline behind real search and RAG, running entirely in your browser with two models. Stage 1: a MiniLM bi-encoder embeds every passage and the query, and ranks by cosine similarity — fast, but only surface-level. Stage 2: the BGE cross-encoder reads each shortlisted (query, passage) pair together and reorders for precision. Compare the two orders below.
The left column is what embeddings alone would return; the right column is after the cross-encoder rereads the shortlist. When the top result changes, that's stage 2 earning its keep — the reranker is slower per pair, which is exactly why you only run it on the shortlist the retriever already narrowed down.
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