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

Cyclical graph setup

The next big step in our code is setting up our graphs using LangGraph:

First, we import some important packages to get us started:
```
from langgraph.graph import END, StateGraph
```
```
from langgraph.prebuilt import ToolNode
```
This code imports the following necessary classes and functions from the langgraph library:
- END: A special node representing the end of the workflow
- StateGraph: A class for defining the state graph of the workflow
- ToolNode: A class for defining a node that represents a tool or action
We then pass AgentState as an argument to the StateGraph class we just imported for defining the state graph of the workflow:
```
workflow = StateGraph(AgentState)
```
This creates a new instance of StateGraph called workflow and defines a new graph for that workflow StateGraph instance.

Next, we define the nodes we will cycle between and assign our node functions to them:

workflow.add_node("agent", agent)  # agent

retrieve = ToolNode(tools)

workflow.add_node...

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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

Table of Contents (20) Chapters

Cyclical graph setup

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Table of Contents (20) Chapters

Cyclical graph setup

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