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How is that better than embeddings? You’re using embeddings to get a finite list of keywords, throwing out the extra benefits of embeddings (support for every h

How is that better than embeddings? You’re using embeddings to get a finite list of keywords, throwing out the extra benefits of embeddings (support for every human language, for instance), using a conventional index, and then going back to embeddings space for the final LLM? That whole thing can be simplified to: compute and store embeddings for docs, compute embeddings for query, find most similar docs.

amelius 2y ago
Yes, you can do the "old school search" part with embeddings.
- brookst 2y ago
  Ah, I had interpreted “old school search” to mean classic text indexing and Boolean style search. I’d argue that if it’s using embeddings and cosine similarity, it’s not old school. But that’s just semantics.