---
title: Answer.AI Rerankers
sidebarTitle: "Answer.AI"
description: Use AnswerDotAI's lightweight reranking library with LanceDB. Features unified API for common reranking models, configurable model selection, and comprehensive scoring options.

---

import { PyRerankingAnswerdotaiUsage } from '/snippets/integrations.mdx';

# Answer.AI Rerankers

This integration uses [AnswersDotAI's rerankers](https://github.com/AnswerDotAI/rerankers) to rerank the search results, providing a lightweight, low-dependency, unified API to use all common reranking and cross-encoder models.

> **Note:** Supported query types – Hybrid, Vector, and FTS.


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

## Accepted Arguments {#accepted-arguments}
| Argument | Type | Default | Description |
| --- | --- | --- | --- |
| `model_type` | `str` | `"colbert"` | The type of model to use. Supported model types can be found here: https://github.com/AnswerDotAI/rerankers. |
| `model_name` | `str` | `"answerdotai/answerai-colbert-small-v1"` | The name of the reranker model to use. |
| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
| `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" is supported, will return relevance score along with the vector and/or fts scores depending on query type. |



## Supported Scores for each query type {#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 {#hybrid-search}
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |

### Vector Search {#vector-search}
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |

### FTS Search {#fts-search}
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |