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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19. Other Books You May Enjoy

The architecture of RAG systems

The following are the stages of a RAG process from a user’s perspective:

A user enters a query/question.
The application thinks for a little while before checking the data it has access to so that it can see what is the most relevant.
The application provides a response that focuses on answering the user’s question, but using data that has been provided to it through the RAG pipeline.

From a technical standpoint, this captures two of the stages you will code: the retrieval and generation stages. But there is one other stage, known as indexing, which can be and is often executed before the user enters the query. With indexing, you are turning supporting data into vectors, storing them in a vector database, and likely optimizing the search functionality so that the retrieval step is as fast and effective as possible.

Once the user passes their query into the system, the following steps occur:

The user query...

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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

The architecture of RAG systems

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Table of Contents (20) Chapters

The architecture of RAG systems

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