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 2 – Components of RAG

In this part, you will learn about key components of a RAG system and how to implement them using LangChain. You’ll explore interfacing with RAG using Gradio to create interactive user interfaces, the crucial role of vectors and vector stores in enhancing RAG performance, and techniques for evaluating RAG quantitatively and with visualizations. Additionally, you’ll dive into using LangChain components such as document loaders, text splitters, and output parsers to further optimize your RAG pipeline.

This part contains the following chapters:

Chapter 6, Interfacing with RAG and Gradio
Chapter 7, The Key Role Vectors and Vector Stores Play in RAG
Chapter 8, Similarity Searching with Vectors
Chapter 9, Evaluating RAG Quantitatively and with Visualizations
Chapter 10, Key RAG Components in LangChain
Chapter 11, Using LangChain to Get More from RAG

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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 2 – Components of RAG

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Table of Contents (20) Chapters

Part 2 – Components of RAG

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