mickymultani/RAG-with-Cross-Encoder-Reranker
Testing speed and accuracy of RAG with, and without Cross Encoder Reranker.
This project helps AI developers understand the trade-offs when building Retrieval-Augmented Generation (RAG) systems. It compares the accuracy and speed of RAG models when retrieving information from lengthy documents, with and without a 'Cross Encoder Reranker.' The input is a long document and questions; the output is an answer along with performance metrics. This is for developers building AI chatbots or knowledge retrieval systems that need to answer questions from specific documents.
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Use this if you are developing a RAG-based AI application and need to decide whether to prioritize response speed or the accuracy and contextual understanding of answers drawn from extensive documents.
Not ideal if you are looking for a ready-to-use RAG solution, as this project focuses on evaluating underlying architectural choices rather than providing an end-user application.
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Jan 12, 2024
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