You're reading from RAG-Driven Generative AI Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone

Product type Paperback

Published in Sep 2024

Publisher Packt

ISBN-13 9781836200918

Length 334 pages

Edition 1st Edition

Languages

Python

Tools

Docker

Concepts

GPT/LLMs

Author (1):

Denis Rothman

View More author details

Table of Contents (14) Chapters

Preface

1. Why Retrieval Augmented Generation? FREE CHAPTER

2. RAG Embedding Vector Stores with Deep Lake and OpenAI

3. Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI

4. Multimodal Modular RAG for Drone Technology

5. Boosting RAG Performance with Expert Human Feedback

6. Scaling RAG Bank Customer Data with Pinecone

7. Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex

8. Dynamic RAG with Chroma and Hugging Face Llama

9. Empowering AI Models: Fine-Tuning RAG Data and Human Feedback

10. RAG for Video Stock Production with Pinecone and OpenAI

11. Other Books You May Enjoy

12. Index

Appendix

To get the most out of this book

You should have basic Natural Processing Language (NLP) knowledge and some experience with Python. Additionally, most of the programs in this book are provided as Jupyter notebooks. To run them, all you need is a free Google Gmail account, allowing you to execute the notebooks on Google Colaboratory’s free virtual machine (VM). You will also need to generate API tokens for OpenAI, Activeloop, and Pinecone.

The following modules will need to be installed when running the notebooks:

Modules	Version
`deeplake`	`3.9.18 (with Pillow)`
`openai`	`1.40.3 (requires regular upgrades)`
`transformers`	`4.41.2`
`numpy`	`>=1.24.1 (Upgraded to satisfy chex)`
`deepspeed`	`0.10.1`
`bitsandbytes`	`0.41.1`
`accelerate`	`0.31.0`
`tqdm`	`4.66.1`
`neural_compressor`	`2.2.1`
`onnx`	`1.14.1`
`pandas`	`2.0.3`
`scipy`	`1.11.2`
`beautifulsoup4`	`4.12.3`
`requests`	`2.31.0`

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/Denis2054/RAG-Driven-Generative-AI. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781836200918.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “self refers to the current instance of the class to access its variables, methods, and functions”.

A block of code is set as follows:

# Cosine Similarity
score = calculate_cosine_similarity(query, best_matching_record)
print(f"Best Cosine Similarity Score: {score:.3f}")

Any command-line input or output is written as follows:

Best Cosine Similarity Score: 0.126

Bold: Indicates a new term, an important word, or words that you see on the screen. For example, text in menus or dialog boxes appears like this. Here is an example: “Modular RAG implementing flexible retrieval methods”.

Warnings or important notes appear like this.

Tips and tricks appear like this.