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

Vector space

The concept of a vector space is highly related to the vector similarity search, as the search is conducted within the vector space represented by the vectors. Technically, a vector space is a mathematical construct that represents a collection of vectors in a high-dimensional space. The dimensions of the vector space correspond to the number of features or attributes associated with each vector. In this space, the vectors of text that are most similar have more similar embeddings and, therefore, are located closer to each other in the space. You will hear the concept of vector space referred to often when talking in more technical ways about similarity searches. Other common names for this space are embedding space or latent space.

The concept of vector space can be helpful in visualizing how the distance algorithms that find the nearest vectors to our user query embedding are working. Ignoring the fact that these vectors are sometimes thousands of dimensions, we can...

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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

Vector space

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Table of Contents (20) Chapters

Vector space

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