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Voyage AI Reranker

Integrate Voyage AI's cutting-edge reranking models with LanceDB. Features model selection, API key management, and comprehensive scoring options for all search types.

Voyage AI Reranker

Voyage AI provides cutting-edge embedding and rerankers.

This reranker uses the Voyage AI API to rerank the search results. You can use this reranker by passing VoyageAIReranker() to the rerank() method. Note that you'll either need to set the VOYAGE_API_KEY environment variable or pass the api_key argument to use this reranker.

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

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 VoyageAIReranker

embedder = get_registry().get("sentence-transformers").create()
db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "voyageai-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 = VoyageAIReranker(api_key="your-api-key", model_name="rerank-2")

# 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	None	The name of the reranker model to use. Available models are: rerank-2, rerank-2-lite
column	str	"text"	The name of the column to use as input to the cross encoder model.
top_n	str	None	The number of results to return. If None, will return all results.
api_key	str	None	The API key for the Voyage AI API. If not provided, the VOYAGE_API_KEY environment variable is used.
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
truncation	bool	None	Whether to truncate the input to satisfy the "context length limit" on the query and the documents.

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)