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

View More author details

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

Why subscribe?

19. Other Books You May Enjoy

Code lab 8.1 – Semantic distance metrics

The file you need to access from the GitHub repository is titled CHAPTER8-1_DISTANCEMETRICS.ipynb.

Our first code lab in this chapter will focus on the different ways you can calculate the distance between vectors, giving you a hands-on view of the difference between each of these approaches. We will use a brand new notebook called CHAPTER8-1_DISTANCEMETRICS.ipynb that has separate code from what we have used up to this point. We will install and import the packages we need, create the embeddings for the sentences we discussed, and then we will step through three types of distance metric formulas that are very common in NLP, generative AI, and RAG systems.

We first install the open source sentence_transformers library that will set up our embedding algorithm:

%pip install sentence_transformers -q --user

The sentence_transformers package provides an easy way to compute dense vector representations for sentences and paragraphs...

The rest of the chapter is locked

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 8.1 – Semantic distance metrics

Authors (1)

Personalised recommendations for you

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 8.1 – Semantic distance metrics

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you