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OpenAI Reranker in LanceDB

OpenAI Reranker (Experimental) in LanceDB

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

💡 Supported Query Types: Hybrid, Vector, FTS

💡 This reranker is experimental. OpenAI doesn’t have a dedicated reranking model, so we are using the chat model for reranking.
python 
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:

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).