You're reading from Unlocking Data with Generative AI and RAG Enhance generative AI systems by integrating internal data with large language models using RAG

Product type Paperback

Published in Sep 2024

Publisher Packt

ISBN-13 9781835887905

Length 346 pages

Edition 1st Edition

Concepts

GPT/LLMs

Author (1):

Keith Bourne

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Table of Contents (20) Chapters

Preface

1. Part 1 – Introduction to Retrieval-Augmented Generation (RAG) FREE CHAPTER

2. Chapter 1: What Is Retrieval-Augmented Generation (RAG)

3. Chapter 2: Code Lab – An Entire RAG Pipeline

4. Chapter 3: Practical Applications of RAG

5. Chapter 4: Components of a RAG System

6. Chapter 5: Managing Security in RAG Applications

7. Part 2 – Components of RAG

8. Chapter 6: Interfacing with RAG and Gradio

9. Chapter 7: The Key Role Vectors and Vector Stores Play in RAG

10. Chapter 8: Similarity Searching with Vectors

11. Chapter 9: Evaluating RAG Quantitatively and with Visualizations

12. Chapter 10: Key RAG Components in LangChain

13. Chapter 11: Using LangChain to Get More from RAG

14. Part 3 – Implementing Advanced RAG

15. Chapter 12: Combining RAG with the Power of AI Agents and LangGraph

16. Chapter 13: Using Prompt Engineering to Improve RAG Efforts

17. Chapter 14: Advanced RAG-Related Techniques for Improving Results

18. Index

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Indexing

The first stage in the RAG system we will examine more closely is indexing. Note that we are skipping the setup, where we install and import packages, as well as set up OpenAI and related accounts. That is a typical step in every generative artificial intelligence (AI) project, not just RAG systems. We provided a thorough setup guide in Chapter 2, so jump back there if you want to review the libraries we’ve added to support these next steps.

Indexing occurs as the first main stage of RAG. As Figure 4.2 indicates, it is the step after the user query:

Figure 4.2 – The Indexing stage of RAG highlighted

In our code from Chapter 2, Indexing is the first section of code you see. This is the step where the data you are introducing to the RAG system is processed. As you can see in the code, the data in this scenario is the web document that is being loaded by WebBaseLoader. This is the beginning of that document (Figure 4.3):

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