Is hybrid search expensive?
It requires maintaining both a keyword index and a vector index, which can increase storage and indexing times.
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Sparse retrieval matches exact words (like TF-IDF). Dense retrieval matches vector meanings (embeddings).
Running a high-quality model on the top retrieved results to re-order them based on relevance before sending them to the LLM.
Because it combines the conceptual understanding of embeddings with the precision of keyword matching for codes and terms.
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