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

Code lab 9.1 – ragas

Retrieval-augmented generation assessment (ragas) is an evaluation platform designed specifically for RAG. In this code lab, we will step through the implementation of ragas in your code, generating a synthetic ground truth, and then establishing a comprehensive set of metrics that you can integrate into your RAG system. But evaluation systems are meant to evaluate something, right? What will we evaluate in our code lab?

If you remember in Chapter 8, we introduced a new search method for our retrieval stage called hybrid search. In this code lab, we will both implement the original dense vector semantic-based search and then use ragas to evaluate the impact of using the hybrid search method. This will give you a real-world working example of how a comprehensive evaluation system can be implemented in your own code!

Before we dive into how to use ragas, it is important to note that it is a highly evolving project. New features and API changes are happening...

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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

Code lab 9.1 – ragas

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Table of Contents (20) Chapters

Code lab 9.1 – ragas

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