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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? 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

Index

A

Activeloop

URL 40

Activeloop Deep Lake 32, 33

adaptive RAG 116-118

selection system 123

advanced RAG 4, 20

index-based search 23

vector search 21

Agricultural Marketing Service (AMS) 201

AI-generated video dataset 261

diffusion transformer model video dataset, analyzing 264

diffusion transformer, working 262, 263

Amazon Web Services (AWS) 144

Apollo program

reference link 41

augmented generation, RAG pipeline 50, 51

augmented input 53, 54

input and query retrieval 51-53

B

bag-of-words (BoW) model 219

Bank Customer Churn dataset

collecting 144-149

environment, installing for Kaggle 146, 147

exploratory data analysis 149-151

ML model, training 152

preparing 144-146

C

Chroma 212, 213

Chroma collection

completions, embedding 218, 219

completions, storing 218, 219

data, embedding 216, 217

data, upserting 216, 217

embeddings, displaying 219

...
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