Answer.AI Rerankers

Use AnswerDotAI’s lightweight reranking library with LanceDB. Features unified API for common reranking models, configurable model selection, and comprehensive scoring options.

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).