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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 338 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? 2. RAG Embedding Vector Stores with Deep Lake and OpenAI FREE CHAPTER 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

Chapter 10, RAG for Video Stock Production with Pinecone and OpenAI

  1. Can AI now automatically comment and label videos?

Yes, we now create video stocks automatically to a certain extent.

  1. Does video processing involve splitting a video into frames?

Yes, we can split a video into frames before analyzing the frames.

  1. Can the programs in this chapter create a 200-minute movie?

No, for the moment, this cannot be done directly. We would have to create many videos and then stitch them together with a video editor.

  1. Do the programs in this chapter require a GPU?

No, only a CPU is required, which is cost-effective because the processing times are reasonable, and the programs mostly rely on API calls.

  1. Are the embedded vectors of the video content stored on disk?

No, the embedded vectors are upserted in a Pinecone vector database.

  1. Do the scripts involve querying a database for retrieving data...
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