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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

Product type Paperback

Published in Sep 2024

Publisher Packt

ISBN-13 9781836200918

Length 338 pages

Edition 1st Edition

Languages

Python

Tools

Docker

Concepts

GPT/LLMs

Author (1):

Denis Rothman

View More author details

Table of Contents (14) Chapters

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

Further reading

Test of Fine-Tuning GPT by Astrophysical Data by Yu Wang et al. is an interesting article on fine-tuning hard science data, which requires careful data preparation: https://arxiv.org/pdf/2404.10019

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Authors (1)

Denis Rothman

Denis Rothman

Expert in AI Transformers including ChatGPT/GPT-4, Bestselling Author

See other products by Denis Rothman

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