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

View More author details

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

Why subscribe?

19. Other Books You May Enjoy

Code lab 5.3 – Blue team defend!

This code can be found in the CHAPTER5-3_SECURING_YOUR_KEYS.ipynb file in the CHAPTER5 directory of the GitHub repository.

There are a number of solutions we can implement to prevent this attack from revealing our prompt. We are going to address this with a second LLM that acts as the guardian of the response. Using a second LLM to check the original response or to format and understand the input is a common solution for many RAG-related applications. We will show how to use it to better secure the code.

It is important to note up front, though, that this is just one example of a solution. The great security battle against potential adversaries is always shifting and changing. You must continuously stay diligent and come up with new and better solutions to prevent security breaches.

Add this line to your imports:

from langchain_core.prompts import PromptTemplate

This imports the PromptTemplate class from the langchain_core.prompts...

The rest of the chapter is locked

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

Code lab 5.3 – Blue team defend!

Authors (1)

Personalised recommendations for you

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

Code lab 5.3 – Blue team defend!

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you