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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19. Other Books You May Enjoy

Code lab 7.1 – Common vectorization techniques

Vectorization algorithms have evolved significantly over the past few decades. Understanding how these have changed, and why, will help you gain more perspective on how to choose the one that fits your needs the most. Let’s walk through some of these vectorization algorithms, starting with some of the earliest ones and ending with the most recent, more advanced options. This is nowhere close to an exhaustive list, but these select few should be enough to give you a sense of where this part of the field came from and where it is going. Before we start, let’s install and import some new Python packages that play important roles in our coding journey through vectorization techniques:

%pip install gensim --user
%pip install transformers
%pip install torch

This code should go near the top of the previous code in the same cell as the other package installations.

Term frequency-inverse document frequency (TF-IDF...

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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 7.1 – Common vectorization techniques

Term frequency-inverse document frequency (TF-IDF...

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Code lab 7.1 – Common vectorization techniques

Term frequency-inverse document frequency (TF-IDF...

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Table of Contents (20) Chapters