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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

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781836200918
Length 334 pages
Edition 1st Edition
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Toc

Table of Contents (14) Chapters Close

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 1: Generator and Commentator

A revolution is on its way in computer vision with automated video generation and analysis. We will introduce the Generator AI agent with Sora in The AI-generated video dataset section. We will explore how OpenAI Sora was used to generate the videos for this chapter with a text-to-video diffusion transformer. The technology itself is something we have expected and experienced to some extent in professional film-making environments. However, the novelty relies on the fact that the software has become mainstream in a few clicks, with inVideo, for example!

In the The Generator and the Commentator section, we will extend the scope of the Generator to collecting and processing the AI-generated videos. The Generator splits the videos into frames and works with the Commentator, an OpenAI LLM, to produce comments on samples of video frames.

The Generator’s task begins by producing the AI-generated video dataset.

The AI-generated video...

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