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RAG-Driven Generative AI

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

1. Preparing the dataset for fine-tuning

Fine-tuning an OpenAI model requires careful preparation; otherwise, the fine-tuning job will fail. In this section, we will carry out the following steps:

Download the dataset from Hugging Face and prepare it by processing its columns.
Stream the dataset to a JSON file in JSONL format.

The program begins by downloading the dataset.

1.1. Downloading and visualizing the dataset

We will download the SciQ dataset we embedded in Chapter 8. As we saw, embedding thousands of documents takes time and resources. In this section, we will download the dataset, but this time, we will not embed it. We will let the OpenAI model handle that for us while fine-tuning the data.

The program downloads the same Hugging Face dataset as in Chapter 8 and filters the training portion of the dataset to include only non-empty records with the correct answer and support text to explain the answer to the questions:

# Import required...

The rest of the chapter is locked

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Authors (1)

Denis Rothman

Denis Rothman

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, and as a student, he wrote and registered a patent for one of the earliest word2vector embeddings and word piece tokenization solutions. He started a company focused on deploying AI and went on to author one of the first AI cognitive NLP chatbots, applied as a language teaching tool for Moët et Chandon (part of LVMH) and more. Denis rapidly became an expert in explainable AI, incorporating interpretable, acceptance-based explanation data and interfaces into solutions implemented for major corporate projects in the aerospace, apparel, and supply chain sectors. His core belief is that you only really know something once you have taught somebody how to do it.

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