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)

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

Why subscribe?

19. Other Books You May Enjoy

Code lab 14.3 – MM-RAG

The code for this lab can be found in the CHAPTER14-3_MM_RAG.ipynb file in the CHAPTER14 directory of the GitHub repository.

This is a good example of when an acronym can really help us talk faster. Try to say multi-modal retrieval augmented regeneration out loud once, and you will likely want to use MM-RAG from now on! But I digress. This is a groundbreaking approach that will likely gain a lot of traction in the near future. It better represents how we as humans process information, so it must be amazing, right? Let’s start by revisiting the concept of using multiple modes.

Multi-modal

Up to this point, everything we have discussed has been focused on text: taking the text as input, retrieving text based on that input, and passing that retrieved text to an LLM that then generates a final text output. But what about non-text? As the companies building these LLMs have started to offer powerful multi-modal capabilities, how can we incorporate...

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

Code lab 14.3 – MM-RAG

Multi-modal

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

Code lab 14.3 – MM-RAG

Multi-modal

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you