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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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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

Dynamic RAG with Chroma and Hugging Face Llama

This chapter will take you into the pragmatism of dynamic RAG. In today’s rapidly evolving landscape, the ability to make swift, informed decisions is more crucial than ever. Decision-makers across various fields—from healthcare and scientific research to customer service management—increasingly require real-time data that is relevant only within the short period it is needed. A meeting may only require temporary yet highly prepared data. Hence, the concept of data permanence is shifting. Not all information must be stored indefinitely; instead, in many cases, the focus is shifting toward using precise, pertinent data tailored for specific needs at specific times, such as daily briefings or critical meetings.

This chapter introduces an innovative and efficient approach to handling such data through the embedding and creation of temporary Chroma collections. Each morning, a new collection is assembled containing...

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