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

View More author details

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

Why subscribe?

19. Other Books You May Enjoy

Getting started with vector stores

Vector stores, combined with other data stores (databases, data warehouses, data lakes, and any other data sources) are the fuel for your RAG system engine. Not to state the obvious, but without a place to store your RAG-focused data, which typically involves the creating, management, filtering, and search of vectors, you will not be able to build a capable RAG system. What you use and how it is implemented will have significant implications for how your entire RAG system performs, making it a critical decision and effort. To start this section, let’s first go back to the original concept of a database.

Data sources (other than vector)

In our basic RAG example so far, we are keeping it simple (for now) and have not connected it to an additional database resource. You could consider the web page that the content is pulled from as the database, although the most accurate description in this context is probably to call it an unstructured...

The rest of the chapter is locked

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

Getting started with vector stores

Data sources (other than vector)

Authors (1)

Personalised recommendations for you

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

Getting started with vector stores

Data sources (other than vector)

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you