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 8.2 – Hybrid search with a custom function

The file you need to access from the GitHub repository is titled CHAPTER8-2_HYBRID_CUSTOM.ipynb.

In this code lab, we are going to start with the notebook from Chapter 5: CHAPTER5-3_BLUE_TEAM_DEFENDS.ipynb. Note that we are not using the Chapter 6 or 7 code, which has a lot of miscellaneous code we won’t use going forward. There is an added bonus in this code lab though; we are going to introduce some new elements that will carry us through the next couple of chapters, such as a new type of document loader for PDFs rather than web pages, a new larger document with more data to search, and a new text splitter. We will also clean out any code we no longer need as a result of these changes.

Once we have updated the code for these changes, we can focus on the task at hand, which is to use BM25 to generate our sparse vectors, combining those vectors with the dense vectors we have already used to form a hybrid search...

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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 8.2 – Hybrid search with a custom function

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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 8.2 – Hybrid search with a custom function

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