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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Code lab 14.2 – Query decomposition

The code for this lab can be found in the CHAPTER14-2_DECOMPOSITION.ipynb file in the CHAPTER14 directory of the GitHub repository.

Query decomposition is a strategy focused on improving question-answering within the GenAI space. It falls under the category of query translation, which is a set of approaches that focuses on improving the initial stage of the RAG pipeline, retrieval. With query decomposition, we will decompose or break down a question into smaller questions. These smaller questions can either be approached sequentially or independently, depending on your needs, giving more flexibility across different scenarios you might use RAG for. After each question is answered, there is a consolidation step that delivers a final response that often has a broader perspective than the original response with naïve RAG.

There are other query translation approaches such as RAG-Fusion and multi-query, which are focused on sub-questions...