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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19. Other Books You May Enjoy

Submitting a question for RAG

So far, you have defined the chain, but you haven’t run it. So, let’s run the entire RAG pipeline in this one line, using a query you are feeding in:

rag_chain.invoke("What are the advantages of using RAG?")

As mentioned when stepping through what happens in the chain, "What are the advantages of using RAG?" is the string we are going to pass into the chain to begin with. The first step in the chain expects this string as the question we discussed in the previous section as one of the two expected variables. In some applications, this may not be in the proper format and will need an extra function to prepare it, but for this application, it is already in the string format we are expecting, so we pass it right into that RunnablePassThrough() object.

In the future, this prompt will include a query from a user interface, but for now, we will represent it as this variable string. Keep in mind that this is not the...

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Table of Contents (20) Chapters

Submitting a question for RAG

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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

Submitting a question for RAG

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