Cross Encoder Reranker

Implement semantic search reranking in LanceDB using Cross Encoder models. Features configurable model selection, device optimization, and comprehensive scoring options for all search types.

Cross Encoder Reranker

This reranker uses Cross Encoder models from sentence-transformers to rerank the search results. You can use this reranker by passing CrossEncoderReranker() to the rerank() method.

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

Accepted Arguments

Argument	Type	Default	Description
model_name	str	"cross-encoder/ms-marco-TinyBERT-L-6"	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.
device	str	None	The device to use for the cross encoder model. If None, will use “cuda” if available, otherwise “cpu”.
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).