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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

Questions

Answer the following questions with Yes or No:

  1. Does the script ensure that the Hugging Face API token is never hardcoded directly into the notebook for security reasons?
  2. In the chapter’s program, is the accelerate library used here to facilitate the deployment of ML models on cloud-based platforms?
  3. Is user authentication separate from the API token required to access the Chroma database in this script?
  4. Does the notebook use Chroma for temporary storage of vectors during the dynamic retrieval process?
  5. Is the notebook configured to use real-time acceleration of queries through GPU optimization?
  6. Can this notebook’s session time measurements help in optimizing the dynamic RAG process?
  7. Does the script demonstrate Chroma’s capability to integrate with ML models for enhanced retrieval performance?
  8. Does the script include functionality for adjusting the parameters of the Chroma database based on session performance...
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