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Reranking is the process of re-ordering search results to improve relevance, often using a different model than the one used for the initial search. LanceDB has built-in support for reranking with models from Cohere, Sentence-Transformers, and more.

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Quickstart

To use a reranker, you perform a search and then pass the results to the rerank() method. 
import lancedb
from lancedb.rerank import CohereReranker

db = lancedb.connect("/tmp/lancedb")
table = db.open_table("my_table")

query = "what is the capital of france"

# Search with reranking
reranker = CohereReranker()
reranked_results = table.search(query).limit(10).rerank(reranker=reranker).to_df()

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Supported Rerankers

LanceDB supports several rerankers out of the box. Here are a few examples:
RerankerDefault Model
CohereRerankerrerank-english-v2.0
CrossEncoderRerankercross-encoder/ms-marco-MiniLM-L-6-v2
ColbertRerankercolbert-ir/colbertv2.0
You can find more details about these and other rerankers in the integrations section.

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Multi-vector reranking

Most rerankers support reranking based on multiple vectors. To rerank based on multiple vectors, you can pass a list of vectors to the rerank method. Here’s an example of how to rerank based on multiple vector columns using the CrossEncoderReranker: 
from lancedb.rerankers import CrossEncoderReranker

reranker = CrossEncoderReranker()

query = "hello"

res1 = table.search(query, vector_column_name="vector").limit(3)
res2 = table.search(query, vector_column_name="text_vector").limit(3)
res3 = table.search(query, vector_column_name="meta_vector").limit(3)

reranked = reranker.rerank_multivector([res1, res2, res3],  deduplicate=True)

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Creating Custom Rerankers

LanceDB also you to create custom rerankers by extending the base Reranker class. The custom reranker should implement the rerank method that takes a list of search results and returns a reranked list of search results. This is covered in more detail in the creating custom rerankers section.