Answer.AI Rerankers
This integration uses AnswersDotAI’s 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.
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
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 | ❌ Not Supported | Results have vector(_distance) and FTS(score) along with Hybrid Search score(_relevance_score). |
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
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). |