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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 chapter focus on building a scalable knowledge-graph-based RAG system using the Wikipedia API and LlamaIndex?
  2. Is the primary use case discussed in the chapter related to healthcare data management?
  3. Does Pipeline 1 involve collecting and preparing documents from Wikipedia using an API?
  4. Is Deep Lake used for creating a relational database in Pipeline 2?
  5. Does Pipeline 3 utilize LlamaIndex to build a knowledge graph index?
  6. Is the system designed to only handle a single specific topic, such as marketing, without flexibility?
  7. Does the chapter describe how to retrieve URLs and metadata from Wikipedia pages?
  8. Is a GPU required to run the pipelines described in the chapter?
  9. Does the knowledge graph index visually map out relationships between pieces of data?
  10. Is human intervention required at every step to query the knowledge graph index?
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