Building a Simple RAG Application Using LlamaIndex
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In this tutorial, we will explore Retrieval-Augmented Generation (RAG) and the LlamaIndex AI framework. We will learn how to use LlamaIndex to build a RAG-based application for Q&A over the private documents and enhance the application by incorporating a memory buffer. This will enable the LLM to generate the response using the context from both the document and previous interactions.
What is RAG in LLMs?
Retrieval-Augmented Generation (RAG) is an advanced methodology designed to enhance the performance of large language models (LLMs) by integrating external knowledge sources into the generation process.
RAG involves two main phases: retrieval and content