# Hybrid Search and Reranking — Embeddings & Vector Search

Source: https://www.geekswithgeeks.com/en/embeddings/a-hybrid

> Combine keyword and vector results and re-order the shortlist.

## Two retrievers, then a judge

**Hybrid search** runs a keyword retriever (BM25) and a vector retriever, then merges the two lists. Because their scores are on different scales, a robust merge is **Reciprocal Rank Fusion**: each document gets `1 / (k + rank)` from each list and the sums decide the order. After retrieval, a **reranker** (a **cross-encoder** that reads the query and a candidate together) re-orders the top 20 to 100 candidates much more accurately than vector similarity alone, at a higher cost per pair, so it is applied only to a shortlist. The usual pipeline is: **filter → hybrid retrieve ~50 → rerank → keep 3 to 5**. Add a **score threshold** so weak matches are dropped.

## The usual pipeline

Each stage trades a little cost for quality.

```text
query
  -> hard filters (tenant, country, date, permissions)
  -> keyword retrieve (BM25, top 50)  +  vector retrieve (ANN, top 50)
  -> merge with Reciprocal Rank Fusion  (score = sum of 1/(60 + rank) over the lists)
  -> cross-encoder rerank (top 50 -> best 5)
  -> drop anything below a minimum score  -> answer / show results
```

## Tune the merge on your data

RRF with k=60 is a good default; if one retriever is clearly stronger on your golden set, weight it higher and re-measure.

**Quiz:** Why is a cross-encoder applied only to a shortlist?

- [ ] It replaces the filter
- [ ] It cannot read text
- [ ] It only works on images
- [x] It is accurate but too costly to run on the whole collection

*Answer:* It is accurate but too costly to run on the whole collection. Fast retrieval narrows candidates; the expensive model refines them.