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

Graphs, AI agents, and LangGraph

LangChain introduced LangGraph in 2024, so it is still relatively new. It is an extension built on top of LangChain Expression Language (LCEL) to create composable and customizable agentic workloads. LangGraph leans heavily on graph theory concepts, such as nodes and edges (described earlier), but with a focus on using them to manage your AI agents. While an older way to manage agents, the AgentExecutor class, still exists, LangGraph is now the recommended way to build agents in LangChain.

LangGraph adds two important components for supporting agents:

The ability to easily define cycles (cyclical graphs)
Built-in memory

It provides a pre-built object equivalent to AgentExecutor, allowing developers to orchestrate agents using a graph-based approach.

Over the past couple of years, numerous papers, concepts, and approaches have emerged for building agents into RAG applications, such as orchestration agents, ReAct agents, self...

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Table of Contents (20) Chapters

Graphs, AI agents, and LangGraph

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Table of Contents (20) Chapters

Graphs, AI agents, and LangGraph

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