MRR Reranker

This reranker uses the Mean Reciprocal Rank (MRR) algorithm to combine and rerank search results from vector and full-text search. You can use this reranker by passing MRRReranker() to the rerank() method. The MRR algorithm calculates the average of reciprocal ranks across different search results, providing a balanced way to merge results from multiple ranking systems.

Note: Supported query types – Hybrid and Multivector search.

Accepted Arguments

Argument	Type	Default	Description
`weight_vector`	`float`	`0.5`	Weight for vector search results (0.0 to 1.0).
`weight_fts`	`float`	`0.5`	Weight for FTS search results (0.0 to 1.0).
`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.

Note: weight_vector + weight_fts must equal 1.0.

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	Results only have the `_relevance_score` column.
`all`	✅ Supported	Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`).

Multivector Search

`return_score`	Status	Description
`relevance`	✅ Supported	Results only have the `_relevance_score` column.
`all`	✅ Supported	Results have vector distances from all searches along with `_relevance_score`.

Linear Combination Cross Encoder

⌘I

​MRR Reranker

​Accepted Arguments

​Supported Scores for each query type

​Hybrid Search

​Multivector Search

MRR Reranker

Accepted Arguments

Supported Scores for each query type

Hybrid Search

Multivector Search