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) 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

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Core concepts of graph theory

To better understand how we are going to use LangGraph in the next few blocks of code, it is helpful to review some key concepts in graph theory. Graphs are mathematical structures that can be used to represent relationships between different objects. The objects are called nodes and the relationships between them, typically drawn with a line, are called edges. You have already seen these concepts in Figure 12.1, but it is important to understand how they relate to any graph and how that is used in LangGraph.

With LangGraph, there are also specific types of edges representing different types of these relationships. The “conditional edge” that we mentioned along with Figure 12.1, for example, represents when you need to make a decision about which node you should go to next; so, they represent the decisions. When talking about the ReAct paradigm, this has also been called the action edge, as it is where the action takes place, relating...