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 14.1 – Query expansion

The code for this lab can be found in the CHAPTER14-1_QUERY_EXPANSION.ipynb file in the CHAPTER14 directory of the GitHub repository.

Many techniques for enhancing RAG focus on improving one area, such as retrieval or generation, but query expansion has the potential to improve both. We have already talked about the concept of expansion in Chapter 13, but that was focused on the LLM output. Here, we focus the concept on the input to the model, augmenting the original prompt with additional keywords or phrases. This approach can improve the retrieval model’s understanding as you add more context to the user query that is used for retrieval, increasing the chances of fetching relevant documents. With an improved retrieval, you are already helping to improve the generation, giving it better context to work with, but this approach also has the potential to produce a more effective query, which in turn also helps the LLM deliver an improved...

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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 14.1 – Query expansion

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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 14.1 – Query expansion

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