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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Fundamentals of vectors in RAG

In this section, we will cover several important topics related to vectors and embeddings in the context of natural language processing (NLP) and RAG. We will begin by clarifying the relationship between vectors and embeddings, explaining that embeddings are a specific type of vector representation used in NLP. We then discuss the properties of vectors, such as their dimensions and size, and how these characteristics impact the precision and effectiveness of text search and similarity comparisons.

What is the difference between embeddings and vectors?

Vectors and embeddings are key concepts in NLP and play a crucial role in building language models and RAG systems. But what are they and how do they relate to each other? To put it simply, you can think of embeddings as a specific type of vector representation. When we are talking about the large language models (LLMs) we use in RAG, which are part of a larger universe called NLP, the vectors we use...