OpenAI Reranker (Experimental)

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

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

Warning: This reranker is experimental. OpenAI does not have a dedicated reranking model, so it uses a chat model under the hood.

Accepted Arguments

Argument	Type	Default	Description
`model_name`	`str`	`"gpt-4-turbo-preview"`	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.
`api_key`	str	`None`	The API key to use. If None, will use the OPENAI_API_KEY environment variable.

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`).