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

Part 3 – Implementing Advanced RAG

In this part, you will learn advanced techniques for enhancing your RAG applications, including integrating AI agents with LangGraph for more sophisticated control flows, leveraging prompt engineering strategies to optimize retrieval and generation, and exploring cutting-edge approaches such as query expansion, query decomposition, and multi-modal RAG. You’ll gain hands-on experience in implementing these techniques through code labs and discover a wealth of additional methods covering indexing, retrieval, generation, and the entire RAG pipeline.

This part contains the following chapters:

Chapter 12, Combining RAG with the Power of AI Agents and LangGraph
Chapter 13, Using Prompt Engineering to Improve RAG Efforts
Chapter 14, Advanced RAG-Related Techniques for Improving Results

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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

Part 3 – Implementing Advanced RAG

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Table of Contents (20) Chapters

Part 3 – Implementing Advanced RAG

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