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

Re-ranking in hybrid RAG

In Chapter 8, in addition to our hybrid RAG approach, we also introduced a form of re-ranking, another common advanced RAG technique. After the semantic search and keyword searches complete their retrieval, we re-rank the results based on the rankings across both sets depending on if they appear in both and where they ranked initially.

So, you have already stepped through three RAG techniques, including two advanced techniques! But this chapter is focused on bringing you three more advanced approaches: query expansion, query decomposition, and MM-RAG. We will also provide you a list of many more approaches you can explore, but we sorted through and picked out these three advanced RAG techniques because of their application in a wide variety of RAG applications.

In our first code lab in this chapter, we will talk about query expansion.

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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

Re-ranking in hybrid RAG

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Table of Contents (20) Chapters

Re-ranking in hybrid RAG

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