Improve the relevance of query responses with a reranker model in Amazon Bedrock
Amazon Bedrock provides access to reranker models that you can use when querying to improve the relevance of the retrieved results. A reranker model calculates the relevance of chunks to a query and reorders the results based on the scores that it calculates. By using a reranker model, you can return responses that are better suited to answering the query. Or, you can include the results in a prompt when running model inference to generate more pertinent and accurate responses. With a reranker model, you can retrieve fewer, but more relevant, results. By feeding these results to the foundation model that you use to generate a response, you can also decrease cost and latency.
Reranker models are trained to identify relevance signals based on a query and then use those signals to rank documents. Because of this, the models can provide more relevant, more accurate results.
Note
You can use reranking for only textual data.
For information about pricing for reranking models, see Amazon Bedrock Pricing
Reranking requires the following input, at the minimum:
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A reranker model that takes a user query and assesses the relevance of the data sources that it can access.
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The user query.
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A list of documents that the reranker must reorder according to to their relevance to the query.
You can use reranker models in Amazon Bedrock in the following ways:
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Call the Rerank
operation directly through the Amazon Bedrock API. The Rerankoperation sends the query, documents, and any additional configurations as input to a reranker model. The model then reranks the documents by relevance to the query and returns the documents in the response. -
If you're using Amazon Bedrock Knowledge Bases for building your Retrieval Augmented Generation (RAG) application, use a reranker model while calling the Retrieve or RetrieveAndGenerate operation. The results from reranking override the default ranking that Amazon Bedrock Knowledge Bases determines.
