Equation 1 · Building Production RAG: An Advanced Technical Guide
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The fix is hybrid retrieval: combine a lexical score, which is exact-match strong and semantically blind, with a dense score, which is semantically strong and exact-match weak. The complication is that the two scores are not on comparable scales. Pinecone’s documentation states the problem directly: dense vectors scored by inner product against unit-normalized embeddings fall roughly in the range [-1, 1] , while BM25-style sparse scores are unbounded positive values that grow with term frequency, document length, and vocabulary rarity, so that “without explicit weighting, the sparse component dominates the combined score” [ 4 ] . The documented fix is a convex combination of normalized…
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The fix is hybrid retrieval: combine a lexical score, which is exact-match strong and semantically blind, with a dense score, which is semantically strong and exact-match weak. The complication is that the two scores are not on comparable scales. Pinecone’s documentation states the problem directly: dense vectors scored by inner product against unit-normalized embeddings fall roughly in the range [-1, 1] , while BM25-style sparse scores are unbounded positive values that grow with term frequency, document length, and vocabulary rarity, so that “without explicit weighting, the sparse component dominates the combined score” [ 4 ] . The documented fix is a convex combination of normalized scores, governed by a single weighting parameter:
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