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OpenAI Reranker (Experimental)

Explore experimental search reranking using OpenAI's GPT models in LanceDB. Features configurable model selection, API key management, and comprehensive scoring options for all search types.

OpenAI Reranker (Experimental)

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

Replace your-api-key with your provider API key. In application code, load credentials from environment variables or a secrets manager.

import tempfile
from pathlib import Path

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(str(Path(tempfile.mkdtemp()) / "openai-rerank"))

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)
tbl.add(data)
reranker = OpenaiReranker(api_key="your-api-key")

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

# Build the full-text index before either FTS or hybrid search.
tbl.create_fts_index("text", replace=True)

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

# Run hybrid search with a reranker
hybrid_results = (
    tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
)
print(hybrid_results)

Accepted Arguments

Argument	Type	Default	Description
model_name	str	"gpt-5.6-sol"	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 column. 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	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).