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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Naïve RAG and its limitations

So far, we have worked with three types of RAG approaches, naïve RAG, hybrid RAG, and re-ranking. Initially, we were working with what is called naïve RAG. This is the basic RAG approach that we had in our starter code in Chapter 2 and multiple code labs after. Naive RAG models, the initial iterations of RAG technology, provide a foundational framework for integrating retrieval mechanisms with generative models, albeit with limitations in flexibility and scalability.

Naïve RAG retrieves numerous fragmented context chunks, the chunks of text that we vectorize, to put into the LLM context window. If you do not use large enough chunks of text, your context will experience higher levels of fragmentation. This fragmentation leads to decreased understanding and capture of the context and semantics within your chunks, reducing the effectiveness of the retrieval mechanism of your RAG application. In the typical naïve RAG application...