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

To get the most out of this book

Readers should have a basic understanding of Python programming and familiarity with machine learning concepts. Knowledge of natural language processing (NLP) and LLMs would be beneficial. Experience with data processing and database management is also helpful. This book assumes readers have some experience with AI development environments, are comfortable working with APIs, and have experience working in a Jupyter notebook environment.

Software/hardware covered in the book	Operating system requirements
Python 3.x	Windows, macOS, or Linux
LangChain	Windows, macOS, or Linux
OpenAI API	Windows, macOS, or Linux
Jupyter notebooks	Windows, macOS, or Linux

You will need access to a Python development environment that supports Jupyter notebooks. An OpenAI API key is required for many of the examples. Some chapters may require additional API keys for services such as Tavily or Together AI, but you will be walked through setting those up in those chapters. A machine with at least 8 GB of RAM is recommended for running the more complex examples, especially those involving LLMs.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

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