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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

Tree index query engine

The tree index in LlamaIndex creates a hierarchical structure for managing and querying text documents efficiently. However, think of something other than a classical hierarchical structure! The tree index engine optimizes the hierarchy, content, and order of the nodes, as shown in Figure 3.5:

Figure 3.5: Optimized tree index

The tree index organizes documents in a tree structure, with broader summaries at higher levels and detailed information at lower levels. Each node in the tree summarizes the text it covers. The tree index is efficient for large datasets and queries large collections of documents rapidly by breaking them down into manageable optimized chunks. Thus, the optimization of the tree structure allows for rapid retrieval by traversing the relevant nodes without wasting time.

Organizing this part of the pipeline and adjusting parameters such as tree depth and summary methods can be a specialized task for a team member. Depending...

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