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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Other component-wise evaluation

Component-wise evaluation involves evaluating individual components of the pipeline, such as the retrieval and generation stages, to gain insights into their effectiveness and identify areas for improvement. We already shared two metrics for each of these stages, but here are a couple more that are available in the ragas platform:

Context relevancy: This metric gauges the relevancy of the retrieved context, calculated based on both the question and contexts. The values fall within the range of (0-1), with higher values indicating better relevancy.
Context entity recall: This metric gives the measure of recall of the retrieved context, based on the number of entities present in both ground_truth data and contexts data relative to the number of entities present in the ground_truth data alone. Simply put, it is a measure of what fraction of entities are recalled from ground_truth data. This metric is particularly useful in fact-based use cases...