RAGuru: A Tool to Create and Automatically Deploy Workload Optimized RAG

Retrieval Augmented Generation (RAG) architectures have emerged as a powerful solution to enhance the accuracy and relevance of large language models (LLMs) by integrating retrieval mechanisms with generative capabilities. However, the design of an effective RAG pipeline is inherently complex, involving multiple components such as chunking strategies, embedding models, retrieval systems, and choice of LLMs. Each of these components offers numerous configuration options and the selection of the optimal combination is often a daunting task. The challenge is compounded by the need to consider trade-offs between performance, accuracy, and cost, which are not always straightforward and can vary significantly depending on the workload.