▲ 39 r/Rag
Do we really need embeddings vectors?
Re-embedding source documents that update 10+ times a day is incredibly expensive and slow. It's making me question if we actually need the embedding layer at all.
Has anyone tried completely dropping vector similarity and relying purely on keyword search?
My thought: What if we use a fast LLM upfront to expand the user's prompt into multiple keyword variations (simple terms, complex phrases, synonyms), and run those against a standard keyword index?
Has anyone run this pattern? Can LLM query expansion + pure keyword search actually match the accuracy of dense embeddings? Would love to hear if this actually saves money or just creates a new bottleneck.
u/sotpak_ — 2 days ago