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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 338 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

Chapter 7, Building Scalable Knowledge-Graph-based RAG with Wikipedia API and LlamaIndex

  1. Does the chapter focus on building a scalable knowledge graph-based RAG system using the Wikipedia API and LlamaIndex?

Yes, it details creating a knowledge graph-based RAG system using these tools.

  1. Is the primary use case discussed in the chapter related to healthcare data management?

No, the primary use case discussed is related to marketing and other domains.

  1. Does Pipeline 1 involve collecting and preparing documents from Wikipedia using an API?

Yes, Pipeline 1 automates document collection and preparation using the Wikipedia API.

  1. Is Deep Lake used to create a relational database in Pipeline 2?

No, Deep Lake is used to create and populate a vector store, not a relational database.

  1. Does Pipeline 3 utilize LlamaIndex to build a knowledge graph index?

Yes, Pipeline 3 uses LlamaIndex to build a knowledge...

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