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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

Building a semantic search engine and generative agent for drone technology

In this section, we will build a semantic index-based search engine and generative AI agent engine using Deep Lake vector stores, LlamaIndex, and OpenAI. As mentioned earlier, drone technology is expanding in domains such as fire detection and traffic control. As such, the program’s goal is to provide an index-based RAG agent for drone technology questions and answers. The program will demonstrate how drones use computer vision techniques to identify vehicles and other objects. We will implement the architecture illustrated in Figure 3.1, described in the Architecture section of this chapter.

Open 2-Deep_Lake_LlamaIndex_OpenAI_indexing.ipynb from the GitHub repository of this chapter. The titles of this section are the same as the section titles in the notebook, so you can match the explanations with the code.

We will first begin by installing the environment. Then, we will build...

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