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

Indexing

The next few steps represent the indexing stage, where we obtain our target data, pre-process it, and vectorize it. These steps are often done offline, meaning they are done to prepare the application for usage later. But in some cases, it may make sense to do this all in real time, such as in rapidly changing data environments where the data that is used is relatively small. In this particular example, the steps are as follows:

Web loading and crawling.
Splitting the data into digestible chunks for the Chroma DB vectorizing algorithm.
Embedding and indexing those chunks.
Adding those chunks and embeddings to the Chroma DB vector store.

Let’s start with the first step: web loading and crawling.

Web loading and crawling

To start, we need to pull in our data. This could be anything of course, but we have to start somewhere!

For our example, I am providing a web page example based on some of the content from Chapter 1. I have adopted...

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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

Indexing

Web loading and crawling

Authors (1)

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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

Indexing

Web loading and crawling

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Authors (1)

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