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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Interfacing with RAG and Gradio

In almost all cases, retrieval-augmented generation (RAG) development involves the creation of one or more applications, or apps for short. When coding RAG apps initially, you will often create a variable in your code that represents a prompt or some other type of input that in turn represents what the RAG pipeline will work off of. But is that how future users will use the app you are building? How do you test this with these users using your code? You need an interface!

In this chapter, we will provide a practical guide to making your application interactive with RAG using Gradio as a user interface (UI). It covers setting up the Gradio environment, integrating RAG models, creating a user-friendly interface that allows users to use your RAG system like a typical web application, and hosting it online in a permanent and free space. You will learn how to quickly prototype and deploy RAG-powered applications, enabling end users to interact with AI...