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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19. Other Books You May Enjoy

The Key Role Vectors and Vector Stores Play in RAG

Vectors are a key component of retrieval-augmented generation (RAG) to understand, as they are the secret ingredient that helps the entire process work well. In this chapter, we dive back into our code from previous chapters with an emphasis on how it is impacted by vectors. In simplistic terms, this chapter will talk about what a vector is, how vectors are created, and then where to store them. In more technical terms, we will talk about vectors, vectorization, and vector stores. This chapter is all about vector creation and why they are important. We are going to focus on how vectors relate to RAG, but we encourage you to spend more time and research gaining as in-depth of an understanding about vectors as you can. The more you understand vectors, the more effective you will be at improving your RAG pipelines.

The vector discussion is so important, though, that we will span it across two chapters. While this chapter focuses on...

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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

Table of Contents (20) Chapters

The Key Role Vectors and Vector Stores Play in RAG

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Table of Contents (20) Chapters

The Key Role Vectors and Vector Stores Play in RAG

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