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

Summary

This chapter hasn’t provided an exhaustive list of components for a RAG system. However, these are components that tend to be in every successful RAG system. Keep in mind that RAG systems are constantly evolving and new types of components are appearing every day. The key aspect of your RAG system should be to add the components that will deliver what your users need. This can be very specific to your project but is often an intuitive outgrowth of what your company does.

This chapter provided a comprehensive overview of the essential components that make up a successful RAG system. It delved into the three main stages: Indexing, Retrieval, and Generation, and explained how these stages work together to deliver enhanced responses to user queries.

In addition to the core stages, this chapter highlighted the importance of the UI and evaluation components. The UI serves as the primary point of interaction between the user and the RAG system, allowing users to input...

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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

Summary

Authors (1)

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Table of Contents (20) Chapters

Summary

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