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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

Summary

As we wrap up our hands-on approach to pragmatic AI implementations, it’s worth reflecting on the transformative journey we’ve embarked on together, exploring the dynamic world of adaptive RAG. We first examined how HF not only complements but also critically enhances generative AI, making it a more powerful tool customized to real-world needs. We described the adaptive RAG ecosystem and then went hands-on, building from the ground up. Starting with data collection, processing, and querying, we integrated these elements into a RAG-driven generative AI system. Our approach wasn’t just about coding; it was about adding adaptability to AI through continuous HF loops.

By augmenting GPT-4’s capabilities with expert insights from previous sessions and end-user evaluations, we demonstrated the practical application and significant impact of HF. We implemented a system where the output is not only generated but also ranked by end-users. Low rankings...

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