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RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information ...

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R2R (RAG to Riches), the Elasticsearch for RAG, bridges the gap between experimenting with and deploying state of the art Retrieval-Augmented Generation (RAG) applications. It's a complete platform ...

Retrieval-augmented generation (RAG) has emerged as a pivotal framework in AI, significantly enhancing the accuracy and relevance of responses generated by large language models (LLMs) leveraging ...