Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of Large Language Models (LLMs) by dynamically incorporating external information during the generation process. This approach combines the generative strengths of LLMs with the retrieval of relevant information from a large dataset, allowing for more informed, accurate, and contextually relevant outputs.
By enabling real time analytics capabilities within RAG systems, we can ensure users obtain the latest information.
In this repository, we provide code and pipelines to build scalable real-time RAG systems using Bytewax.