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 11.1 – Document loaders

The file you need to access from the GitHub repository is titled CHAPTER11-1_DOCUMENT_LOADERS.ipynb.

Document loaders play a key role in accessing, extracting, and pulling in the data that makes our RAG application function. Document loaders are used to load and process documents from various sources such as text files, PDFs, web pages, or databases. They convert the documents into a format suitable for indexing and retrieval.

Let’s install some new packages to support our document loading, which, as you might have guessed, involves some different file format-related packages:

%pip install bs4
%pip install python-docx
%pip install docx2txt
%pip install jq

The first one may look familiar, bs4 (for Beautiful Soup 4), as we used it in Chapter 2 for parsing HTML. We also have a couple of Microsoft Word-related packages, such as python_docx, which helps with creating and updating Microsoft Word (.docx) files, and docx2txt, which...