OpenAI Reranker (Experimental)

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

Note

Supported Query Types: Hybrid, Vector, FTS

Warning

This re-ranker is experimental. OpenAI doesn't have a dedicated reranking model, so we are using the chat model for reranking.

import numpy
import lancedb
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
from lancedb.rerankers import OpenaiReranker

embedder = get_registry().get("sentence-transformers").create()
db = lancedb.connect("~/.lancedb")

class Schema(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 = OpenaiReranker()

# Run vector search with a reranker
result = tbl.search("hello").rerank(reranker=reranker).to_list() 

# Run FTS search with a reranker
result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()

# Run hybrid search with a reranker
tbl.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_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.

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	Returns only have the `_relevance_score` column
`all`	❌ Not Supported	Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`)

Vector Search

`return_score`	Status	Description
`relevance`	✅ Supported	Returns only have the `_relevance_score` column
`all`	✅ Supported	Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`)

FTS Search

`return_score`	Status	Description
`relevance`	✅ Supported	Returns only have the `_relevance_score` column
`all`	✅ Supported	Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`)