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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? 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

Questions

Answer the following questions with Yes or No:

  • Do indexes increase precision and speed in retrieval-augmented generative AI?
  • Can indexes offer traceability for RAG outputs?
  • Is index-based search slower than vector-based search for large datasets?
  • Does LlamaIndex integrate seamlessly with Deep Lake and OpenAI?
  • Are tree, list, vector, and keyword indexes the only types of indexes?
  • Does the keyword index rely on semantic understanding to retrieve data?
  • Is LlamaIndex capable of automatically handling chunking and embedding?
  • Are metadata enhancements crucial for ensuring the traceability of RAG-generated outputs?
  • Can real-time updates easily be applied to an index-based search system?
  • Is cosine similarity a metric used in this chapter to evaluate query accuracy?
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