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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Code lab 3.1 – Adding sources to your RAG

Many of the aforementioned applications mentioned include an element of adding more data to the response. For example, you are likely going to want to quote the sources of your response if you have a RAG pipeline that crawls legal documents or scientific research papers as a part of the efforts described in the Expanding and enhancing private data with general external knowledge bases or Innovation scouting and trend analysis sections.

We will continue the code from Chapter 2 and add this valuable step of returning the retrieved documents in the RAG response.

Starting with the code from Chapter 2, we need to introduce these elements to this code, which I will walk through and explain what is happening:

from langchain_core.runnables import RunnableParallel

This is a new import: the RunnableParallel object from LangChain runnables. This introduces the concept of running the retriever and question in parallel. This can improve...