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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 multimodal modular RAG handle different types of data, such as text and images?
  2. Are drones used solely for agricultural monitoring and aerial photography?
  3. Is the Deep Lake VisDrone dataset used in this chapter for textual data only?
  4. Can bounding boxes be added to drone images to identify objects such as trucks and pedestrians?
  5. Does the modular system retrieve both text and image data for query responses?
  6. Is building a vector index necessary for querying the multimodal VisDrone dataset?
  7. Are the retrieved images processed without adding any labels or bounding boxes?
  8. Is the multimodal modular RAG performance metric based only on textual responses?
  9. Can a multimodal system such as the one described in this chapter handle only drone-related data?
  10. Is evaluating images as easy as evaluating text in multimodal RAG?
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