# Source: https://docs.lancedb.com/integrations/reranking/mrr.md

> ## 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.

# MRR Reranker

> Combine and rerank search results using Mean Reciprocal Rank (MRR) algorithm in LanceDB. Supports weighted scoring for hybrid and multivector search.

export const PyRerankingMrrUsage = "import lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import MRRReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = MRRReranker(weight_vector=0.7, weight_fts=0.3)\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n\n# Run multivector search across multiple vector columns\nrs1 = tbl.search(\"hello\").limit(10).with_row_id(True).to_arrow()\nrs2 = tbl.search(\"greeting\").limit(10).with_row_id(True).to_arrow()\ncombined = MRRReranker().rerank_multivector([rs1, rs2])\n";

# 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.

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

## 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`. |