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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Not all semantics are created equal!

A common mistake made in RAG applications is choosing the first vectorization algorithm that is implemented and just assuming that provides the best results. These algorithms take the semantic meaning of text and represent them mathematically. However, these algorithms are generally large NLP models themselves, and they can vary in capabilities and quality as much as the LLMs. Just as we, as humans, often find it challenging to comprehend the intricacies and nuances of text, these models can grapple with the same challenge, having varying abilities to grasp the complexities inherent in written language. For example, models in the past could not decipher the difference between bark (a dog noise) and bark (the outer part of most trees), but newer models can detect this based on the surrounding text and the context in which it is used. This area of the field is adapting and evolving just as fast as other areas.

In some cases, it is possible that...