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)

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

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

Fundamentals of AI agents and RAG integration

When talking with new developers in generative AI, we have been told that the concept of an AI agent often tends to be one of the more challenging concepts to grasp. When experts talk about agents, they often talk about them in very abstract terms, focusing on all the things AI agents can be responsible for in a RAG application, but failing to really explain thoroughly what an AI agent is and how it works.

I find that it is easiest to dispel the mystery of the AI agent by explaining what it really is, which is actually a very simple concept. To build an AI agent in its most basic form, you are simply taking the same LLM concept you have already been working with throughout these chapters and adding a loop that terminates when the intended task is done. That’s it! It’s just a loop folks!

Figure 12.1 represents the RAG agent loop you will be working with in the code lab that you are about to dive into:

Figure 12.1 – Graph of the agent’s control flow

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Table of Contents (20) Chapters

Fundamentals of AI agents and RAG integration

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Table of Contents (20) Chapters

Fundamentals of AI agents and RAG integration

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