Similarity Search, Hybrid Search, and Reranking

Vector search retrieves semantically similar vectors using a distance or similarity metric. Exact search can be expensive at scale, so vector databases often use approximate nearest-neighbor indexes. Hybrid search combines lexical retrieval (such as BM25/full-text search) with vector retrieval; this can help when exact names, identifiers, or rare terms matter alongside semantic meaning. Reranking uses a second model to reorder a smaller candidate set.

Similarity scores are not universally comparable across models or databases. Tune candidate count, filters, distance metric, hybrid weights, and reranking against a labeled evaluation set. Monitor recall and latency as the corpus grows.

Hybrid retrieval concept

Candidates = merge(
  lexicalSearch(query, filters),
  vectorSearch(embed(query), filters)
)
TopResults = rerank(query, Candidates).slice(0, 8)

💡 This is pseudocode showing the stages. Deduplicate candidates, preserve source metadata, enforce filters on both retrieval paths, and benchmark whether reranking improves answer quality enough to justify latency and cost.

Key Takeaways

Tune retrieval against real queries.
Use hybrid search when exact terms matter.
Reranking adds latency and should be justified by measured gains.

Knowledge Check

Why can hybrid search outperform vector-only retrieval for some queries?

Similarity Search, Hybrid Search, and Reranking

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