Building Retrieval-Augmented Generation (RAG) Systems
Large Language Models often lack access to specific, private, or very recent information. Retrieval-Augmented Generation (RAG) provides a method to address this limitation by grounding the model's responses in external data sources. This technique combines the generative capabilities of LLMs with information retrieval mechanisms.
In this chapter, you will learn the fundamental concepts behind RAG. We will cover how to integrate tools like LangChain and LlamaIndex to build RAG systems. You will gain an understanding of vector stores and embeddings, which are essential for efficient information retrieval. We will walk through the steps of constructing a basic RAG pipeline and discuss methods for evaluating its performance. By the end of this chapter, you will be able to implement a simple RAG application using Python.
Sections
7.1 Retrieval-Augmented Generation Concepts
7.2 Integrating LlamaIndex/LangChain for RAG
7.3 Vector Stores and Embeddings Overview
7.4 Setting Up a Basic Vector Store
7.5 Constructing a RAG Pipeline
7.6 Evaluating RAG Performance Metrics
7.7 Practice: Creating a Simple RAG Application
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