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

Semantic versus keyword search

As we’ve already said many times, vectors capture the meaning behind our data in a mathematical representation. To find data points similar in meaning to a user query, we can search and retrieve the closest objects in a vector space such as the one we just showed. This is known as semantic or vector search. A semantic search, as opposed to keyword matching, is searching for documents that have similar semantic meaning, rather than just the same words. As humans, we can say the same or similar things in so many different ways! Semantic search can capture that aspect of our language because it assigns similar mathematical values to similar concepts, whereas keyword search focuses on specific word matching and often misses similar semantic meanings partially or entirely.

From a technical standpoint, semantic search utilizes the meaning of the documents we have vectorized that is mathematically embedded in the vector that represents it. For math...

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Table of Contents (20) Chapters

Semantic versus keyword search

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Table of Contents (20) Chapters

Semantic versus keyword search

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