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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

Pipeline 3: The Video Expert

The role of the OpenAI GPT-4o Video Expert is to analyze the comment made by the Commentator OpenAI LLM agent, point out the cognitive dissonances (things that don’t seem to fit together in the description), rewrite the comment, and provide a label. The workflow of the Video Expert, as illustrated in the following figure, also includes the code of the Metrics calculations and display section of Chapter 7, Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex.

The Commentator’s role was only to describe what it saw. The Video Expert is there to make sure it makes sense and also label the videos so they can be classified in the dataset for further use.

Figure 10.10: Workflow of the Video Expert for automated dynamics descriptions and labeling

The Pinecone index will connect to the Pinecone index as described in the Pipeline 2. The Vector Store Administrator section of this chapter. This time,...

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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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