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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? 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

Multimodal Modular RAG for Drone Technology

We will take generative AI to the next level with modular RAG in this chapter. We will build a system that uses different components or modules to handle different types of data and tasks. For example, one module processes textual information using LLMs, as we have done until the last chapter, while another module manages image data, identifying and labeling objects within images. Imagine using this technology in drones, which have become crucial across various industries, offering enhanced capabilities for aerial photography, efficient agricultural monitoring, and effective search and rescue operations. They even use advanced computer vision technology and algorithms to analyze images and identify objects like pedestrians, cars, trucks, and more. We can then activate an LLM agent to retrieve, augment, and respond to a user’s question.

In this chapter, we will build a multimodal modular RAG program to generate responses to queries...

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