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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RAG vocabulary

Now is as good a time as any to review some vocabulary that should help you become familiar with the various concepts in RAG. In the following subsections, we will familiarize ourselves with some of this vocabulary, including LLMs, prompting concepts, inference, context windows, fine-tuning approaches, vector databases, and vectors/embeddings. This is not an exhaustive list, but understanding these core concepts should help you understand everything else we will teach you about RAG in a more effective way.

LLM

Most of this book will deal with LLMs. LLMs are generative AI technologies that focus on generating text. We will keep things simple by concentrating on the type of model that most RAG pipelines use, the LLM. However, we would like to clarify that while we will focus primarily on LLMs, RAG can also be applied to other types of generative models, such as those for images, audio, and videos. We will focus on these other types of models and how they are used...