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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
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Denis Rothman
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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:

  1. Do embeddings convert text into high-dimensional vectors for faster retrieval in RAG?
  2. Are keyword searches more effective than embeddings in retrieving detailed semantic content?
  3. Is it recommended to separate RAG pipelines into independent components?
  4. Does the RAG pipeline consist of only two main components?
  5. Can Activeloop Deep Lake handle both embedding and vector storage?
  6. Is the text-embedding-3-small model from OpenAI used to generate embeddings in this chapter?
  7. Are data embeddings visible and directly traceable in an RAG-driven system?
  8. Can a RAG pipeline run smoothly without splitting into separate components?
  9. Is chunking large texts into smaller parts necessary for embedding and storage?
  10. Are cosine similarity metrics used to evaluate the relevance of retrieved information?
You have been reading a chapter from
RAG-Driven Generative AI
Published in: Sep 2024
Publisher: Packt
ISBN-13: 9781836200918
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