> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Reciprocal Rank Fusion Reranker

> Learn about LanceDB's default Reciprocal Rank Fusion (RRF) reranker for hybrid search. Implements the Cormack et al. algorithm for optimal search result ranking.

# Reciprocal Rank Fusion Reranker

**Reciprocal Rank Fusion (RRF)** is a model-free way to merge several ranked result lists into a
single ordering. Rather than comparing raw similarity scores (which aren't directly comparable
across, say, a vector search and a full-text search), RRF looks only at each document's *rank
position* in each list. It scores every document with the formula `1 / (rank + K)`, sums those
contributions across the lists, and re-sorts by the total. Documents that rank highly in more than
one search rise to the top. Because there's no model to load or call, it's fast and cheap, which is
why it's the default reranker for LanceDB hybrid search. The implementation follows the
[Cormack et al. paper](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf).

> **Supported query types:** hybrid search and [multi-vector reranking](/reranking#multi-vector-reranking).
> Because RRF fuses two or more ranked lists, it can't rerank a single vector or full-text result set
> on its own. Calling `rerank_vector` or `rerank_fts` on an `RRFReranker` raises `NotImplementedError`.

<CodeGroup>
  <CodeBlock filename="Python" language="Python" icon="python">
    {PyRerankingRrfUsage}
  </CodeBlock>
</CodeGroup>

## Accepted Arguments

| Argument       | Type  | Default       | Description                                                                                                                                                                                                             |
| -------------- | ----- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `K`            | `int` | `60`          | A constant used in the RRF formula (default is 60). Experiments indicate that k = 60 was near-optimal, but that the choice is not critical.                                                                             |
| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score`. If "all", will return all scores from the vector and FTS search along with the relevance score. |

## Multi-vector reranking

`RRFReranker` can also fuse the results of several vector searches with `rerank_multivector`, applying
the same rank-fusion algorithm across more than two lists. Every input result set must include the
`_rowid` column, so add `.with_row_id(True)` to each `table.search(...)` call before reranking,
otherwise the call raises a `ValueError`. See [multi-vector reranking](/reranking#multi-vector-reranking)
for a full example.

## Supported Scores for each query type

You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:

### Hybrid Search

| `return_score` | Status      | Description                                                                                                 |
| -------------- | ----------- | ----------------------------------------------------------------------------------------------------------- |
| `relevance`    | ✅ Supported | Returned rows only have the `_relevance_score` column.                                                      |
| `all`          | ✅ Supported | Returned rows have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |