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

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Code lab 12.1 – adding a LangGraph agent to RAG

In this code lab, we will add an agent to our existing RAG pipeline that can make decisions about whether to retrieve from an index or use a web search. We will show the inner thoughts of the agent as it processes data that it retrieves toward the goal of providing you with a more thorough response to your question. As we add the code for our agent, we will see new components, such as tools, toolkits, graphs, nodes, edges, and, of course, the agent itself. For each component, we will go more in-depth into how that component interacts and supports your RAG application. We will also add code so that this functions more like a chat session, rather than a Q&A session:

First, we will install some new packages to support our agent development:
```
%pip install tiktoken
```
```
%pip install langgraph
```
In the first line, we install the tiktoken package, which is an OpenAI package used for tokenizing text data before feeding it into language...