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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? 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

The architecture of RAG for knowledge-graph-based semantic search

As established, we will build a graph-based RAG program in this chapter. The graph will enable us to visually map out the relationships between the documents of a RAG dataset. It can be created automatically with LlamaIndex, as we will do in the Pipeline 3: Knowledge graph index-based RAG section of this chapter. The program in this chapter will be designed for any Wikipedia topic, as illustrated in the following figure:

Figure 7.1: From a Wikipedia topic to interacting with a graph-based vector store index

We will first implement a marketing agency for which a knowledge graph can visually map out the complex relationships between different marketing concepts. Then, you can go back and explore any topic you wish once you understand the process. In simpler words, we will implement the three pipelines seamlessly to:

  • Select a Wikipedia topic related to marketing. Then, you can run the process with...
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