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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

Why use index-based RAG?

Index-based search takes advanced RAG-driven generative AI to another level. It increases the speed of retrieval when faced with large volumes of data, taking us from raw chunks of data to organized, indexed nodes that we can trace from the output back to the source of a document and its location.

Let’s understand the differences between a vector-based similarity search and an index-based search by analyzing the architecture of an index-based RAG.

Architecture

Index-based search is faster than vector-based search in RAG because it directly accesses relevant data using indices, while vector-based search sequentially compares embeddings across all records. We implemented a vector-based similarity search program in Chapter 2, RAG Embedding Vector Stores with Deep Lake and OpenAI, as shown in Figure 3.1:

  • We collected and prepared data in Pipeline #1: Data Collection and Preparation
  • We embedded the data and stored the prepared...
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