Implementing a reranker for your RAG
Deploying a reranker on a Sagemaker endpoint with Hugging Face Text Embedding Inference
- Initial Candidates: When a query is made, the system first retrieves a set of potentially relevant documents or pieces of information based on traditional retrieval methods (like keyword matching or vector similarity).
- Reranking Process: After this initial retrieval, reranking takes this initial set of results and applies more sophisticated algorithms (such as BGE) to re-order them.
- The goal is to bring the most relevant results to the top of the list, improving the overall quality and use

It is essential to validate our approach through robust evaluations. Implementing reranking should be done thoughtfully, with careful testing to ensure it truly enhances system performance. These evaluations will help determine if the additional complexity and computational cost of reranking truly yields meaningful improvements in relevance and accuracy.
You can also look at how to deploy cohere rerank (another great reranker) in a managed fashion : https://aws.amazon.com/blogs/machine-learning/improve-rag-performance-using-cohere-rerank/
Any opinions in this post are those of the individual author and may not reflect the opinions of AWS.