AnswersDotAI Rerankers

This integration usesAnswersDotAI'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, FTS

importnumpyimportlancedbfromlancedb.embeddingsimportget_registryfromlancedb.pydanticimportLanceModel,Vectorfromlancedb.rerankersimportAnswerdotaiRerankersembedder=get_registry().get("sentence-transformers").create()db=lancedb.connect("~/.lancedb")classSchema(LanceModel):text:str=embedder.SourceField()vector:Vector(embedder.ndims())=embedder.VectorField()data=[{"text":"hello world"},{"text":"goodbye world"}]tbl=db.create_table("test",schema=Schema,mode="overwrite")tbl.add(data)reranker=AnswerdotaiRerankers()# Run vector search with a rerankerresult=tbl.search("hello").rerank(reranker=reranker).to_list()# Run FTS search with a rerankerresult=tbl.search("hello",query_type="fts").rerank(reranker=reranker).to_list()# Run hybrid search with a rerankertbl.create_fts_index("text",replace=True)result=tbl.search("hello",query_type="hybrid").rerank(reranker=reranker).to_list()

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`).