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

Preface

In the rapidly evolving landscape of artificial intelligence (AI), retrieval-augmented generation (RAG) has emerged as a groundbreaking technology that is transforming the way we interact with and leverage AI systems. RAG combines the strengths of information retrieval and generative AI models to create powerful applications that can access and utilize vast amounts of data to generate highly accurate, contextually relevant, and informative responses.

As AI continues to permeate various industries and domains, understanding and mastering RAG has become increasingly crucial for developers, researchers, and businesses alike. RAG enables AI systems to go beyond the limitations of their training data and access up-to-date and domain-specific information, making them more versatile, adaptable, and valuable in real-world scenarios.

As this book progresses, it serves as a comprehensive guide to the world of RAG, covering both fundamental concepts and advanced techniques. It is filled with detailed coding examples showcasing the latest tools and technologies, such as LangChain, Chroma’s vector store, and OpenAI’s ChatGPT-4o and ChatGPT-4o mini models. We will cover essential topics, including vector stores, vectorization, vector search techniques, prompt engineering and design, AI agents for RAG-related applications, and methods for evaluating and visualizing RAG outcomes.

The importance of learning RAG cannot be overstated. RAG is positioned as a key facilitator of customized, efficient, and insightful AI solutions, bridging the gap between generative AI’s potential and specific business needs. Whether you are a developer looking to enhance your AI skills, a researcher exploring new frontiers in AI, or a business leader seeking to leverage AI for growth and innovation, this book will provide you with the knowledge and practical skills necessary to harness the power of RAG and unlock the full potential of AI in your projects and initiatives.