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

Where vectors lurk in your code

One way to indicate the value of vectors in the RAG system is to show you all the places they are used. As discussed earlier, you start with your text data and convert it to vectors during the vectorization process. This occurs in the indexing stage of the RAG system. But, in most cases, you must have somewhere to put those embedding vectors, which brings in the concept of the vector store.

During the retrieval stage of the RAG system, you start with a question as input from the user, which is first converted to an embedding vector before the retrieval begins. Lastly, the retrieval process uses a similarity algorithm that determines the proximity between the question embedding and all the embeddings in the vector store. There is one more potential area in which vectors are common and that is when you want to evaluate your RAG responses, but we will cover that in Chapter 9 when we cover evaluation techniques. For now, let’s dive deeper into...

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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

Where vectors lurk in your code

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Table of Contents (20) Chapters

Where vectors lurk in your code

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