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Continuing from the previous section, we can now rerank the results using more complex rerankers.

Try it yourself - Open In Colab

Reranking search results

You can rerank any search results using a reranker. The syntax for reranking is as follows:

from lancedb.rerankers import LinearCombinationReranker

reranker = LinearCombinationReranker()[0], query_type="hybrid").rerank(reranker=reranker).limit(5).to_pandas()
Based on the query_type, the rerank() function can accept other arguments as well. For example, hybrid search accepts a normalize param to determine the score normalization method.


LanceDB provides a Reranker base class that can be extended to implement custom rerankers. Each reranker must implement the rerank_hybrid method. rerank_vector and rerank_fts methods are optional. For example, the LinearCombinationReranker only implements the rerank_hybrid method and so it can only be used for reranking hybrid search results.

Choosing a Reranker

There are many rerankers available in LanceDB like CrossEncoderReranker, CohereReranker, and ColBERT. The choice of reranker depends on the dataset and the application. You can even implement you own custom reranker by extending the Reranker class. For more details about each available reranker and performance comparison, refer to the rerankers documentation.

In this example, we'll use the CohereReranker to rerank the search results. It requires cohere to be installed and COHERE_API_KEY to be set in the environment. To get your API key, sign up on Cohere.

from lancedb.rerankers import CohereReranker

# use Cohere reranker v3
reranker = CohereReranker(model_name="rerank-english-v3.0") # default model is "rerank-english-v2.0"

Reranking search results

Now we can rerank all query type results using the CohereReranker:

# rerank hybrid search results[0], query_type="hybrid").rerank(reranker=reranker).limit(5).to_pandas()

# rerank vector search results[0], query_type="vector").rerank(reranker=reranker).limit(5).to_pandas()

# rerank fts search results[0], query_type="fts").rerank(reranker=reranker).limit(5).to_pandas()

Each reranker can accept additional arguments. For example, CohereReranker accepts top_k and batch_size params to control the number of documents to rerank and the batch size for reranking respectively. Similarly, a custom reranker can accept any number of arguments based on the implementation. For example, a reranker can accept a filter that implements some custom logic to filter out documents before reranking.


Let us take a look at the same datasets from the previous sections, using the same embedding table but with Cohere reranker applied to all query types.


When reranking fts or vector search results, the search results are over-fetched by a factor of 2 and then reranked. From the reranked set, top_k (5 in this case) results are taken. This is done because reranking will have no effect on the hit-rate if we only fetch the top_k results.

Synthetic LLama2 paper dataset

Query Type Hit-rate@5
Vector 0.640
FTS 0.595
Reranked vector 0.677
Reranked fts 0.672
Hybrid 0.759

SQuAD Dataset

Uber10K sec filing Dataset

Query Type Hit-rate@5
Vector 0.608
FTS 0.824
Reranked vector 0.671
Reranked fts 0.843
Hybrid 0.849