---
title: "Semantic, lexical, hybrid search, and reranking"
chapter: "09"
---

# Semantic, lexical, hybrid search, and reranking

Semantic search retrieves meaning, lexical search retrieves exact terms, and
hybrid search combines both. Production search usually needs all three plus
metadata filtering and reranking.

## Retrieval pipeline

`query → normalize → intent/tenant filter → lexical candidates + vector
candidates → fuse → rerank → threshold → diversify → evidence`

Vector search is strong for paraphrases. BM25/keyword search is strong for
error codes, product IDs, names, dates, and rare exact terms. Reciprocal rank
fusion combines rankings without pretending incomparable raw scores match.

## Reranking

A cross-encoder or capable reranking model scores query-document pairs more
precisely after broad retrieval. Retrieve perhaps 30–100 candidates, rerank,
then send a small evidence set to generation. Measure added latency and cost.

## Filters and authorization

Apply tenant, region, document status, effective date, and permission filters
inside retrieval—not after the model sees results. Metadata is part of the
security boundary.

## Evaluation

Use labeled queries with relevant document IDs. Measure recall@k, precision@k,
MRR, nDCG, no-result rate, latency, and permission violations. Then measure
answer groundedness separately. A fluent answer cannot prove retrieval worked.

## Thresholds

A similarity threshold is a rejection policy, not a magic truth line. Tune per
embedding model and corpus. If no candidate qualifies, ask a narrower question
or abstain rather than filling context with noise.

## Feynman check

Keyword search finds the same label; semantic search finds the same idea;
reranking is a careful second reader choosing which results deserve attention.