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

Combining RAG with the Power of AI Agents and LangGraph

One call to an large language model (LLM) can be powerful, but put your logic in a loop with a goal toward achieving a more sophisticated task and you can take your retrieval-augmented generation (RAG) development to a whole new level. That is the concept behind agents. The past year of development for LangChain has focused significant energy on improving support for agentic workflows, adding functionality that enables more precise control over agent behavior and capabilities. Part of this progress has been in the emergence of LangGraph, another relatively new part of LangChain. Together, agents and LangGraph pair well as a powerful approach to improving RAG applications.

In this chapter, we will focus on gaining a deeper understanding of the elements of agents that can be utilized in RAG and then tie them back to your RAG efforts, covering topics such as the following: