RAG systems combine the power of large language models with external knowledge retrieval to generate more informed and accurate LLM responses. The trifecta of Llama 3, GroqCloud, and Redis provides the perfect foundation for building high-performance and privacy-centric RAG systems.

Llama 3 enables the creation of functional RAGs that prioritize data privacy, while GroqCloud's real-time generative AI inference capabilities ensure efficient processing. Meanwhile, Redis, the king of real-time databases, provides a robust and scalable solution for storing and retrieving data.

In this tutorial, I'll guide you through connecting to Redis and GroqCloud and constructing various RAG systems using Langchain Expression Language (LCEL). Specifically, I'll cover creating three distinct RAG systems: a basic RAG, a hybrid RAG, and an advanced contextual RAG that integrates customer data into the interaction.

By the end of the tutorial, you will have a comprehensive understanding of how to build high-performance and privacy-centric RAG systems that are fully customizable.

The Colab Notebook and Data

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