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

Code Lab – An Entire RAG Pipeline

This code lab lays the foundation for the rest of the code in this book. We will spend this entire chapter giving you an entire retrieval-augmented generation (RAG) pipeline. Then, as we step through the book, we will look at different parts of the code, adding enhancements along the way so that you have a comprehensive understanding of how your code can evolve to tackle more and more difficult problems.

We will spend this chapter walking through each component of the RAG pipeline, including the following aspects:

No interface
Setting up a large language model (LLM) account with OpenAI
Installing the required Python packages
Indexing data by web crawling, splitting documents, and embedding the chunks
Retrieving relevant documents using vector similarity search
Generating responses by integrating retrieved context into LLM prompts

As we step through the code, you will gain a comprehensive understanding of...

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 – An Entire RAG Pipeline

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 – An Entire RAG Pipeline

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you