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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Summary

In this chapter, we explored how AI agents and LangGraph can be combined to create more powerful and sophisticated RAG applications. We learned that an AI agent is essentially an LLM with a loop that allows it to reason and break tasks down into simpler steps, improving the chances of success in complex RAG tasks. LangGraph, an extension built on top of LCEL, provides support for building composable and customizable agentic workloads, enabling developers to orchestrate agents using a graph-based approach.

We dove into the fundamentals of AI agents and RAG integration, discussing the concept of tools that agents can use to carry out tasks, and how LangGraph’s AgentState class tracks the state of the agent over time. We also covered the core concepts of graph theory, including nodes, edges, and conditional edges, which are crucial for understanding how LangGraph works.

In the code lab, we built a LangGraph retrieval agent for our RAG application, demonstrating how...