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

Summary

In this chapter, we went through a practical guide on creating interactive applications using RAG and Gradio as the UI. We covered setting up the Gradio environment, integrating RAG models, and creating a user-friendly interface that allows users to interact with the RAG system like a typical web application. Developers can quickly prototype and deploy RAG-powered applications, enabling end users to interact with RAG pipelines in real time.

We also discussed the benefits of using Gradio, such as its open source nature, integration with popular machine learning frameworks, and collaboration features and Gradio’s integration with Hugging Face, which provides resources for the generative AI community, including the ability to host Gradio demos permanently and for free using Hugging Face Spaces.

With the code lab, we learned how to add a Gradio interface to a RAG application. We created the Gradio interface using gr.Interface, specifying the input and output components...

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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

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Table of Contents (20) Chapters

Summary

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