ColBERT Reranker

This reranker uses ColBERT model to rerank the search results. You can use this reranker by passing ColbertReranker() to the rerank() method.

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

Accepted Arguments

Argument	Type	Default	Description
`model_name`	`str`	`"colbert-ir/colbertv2.0"`	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.

You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:

`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`).

`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`).

`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`).