RAG-Driven Generative AI E-Book

Denis Rothman

• Herausgeber: Packt Publishing
• Kategorie: Lebensstil
• Sprache: Englisch

Seitenzahl: 430

Veröffentlichungsjahr: 2024

Beschreibung

RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs.
This book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. You’ll discover techniques to optimize your project’s performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs.
You’ll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project.

Leseprobe

RAG-Driven Generative AI

Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone

Denis Rothman

RAG-Driven Generative AI

Copyright © 2024 Packt Publishing

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First published: September 2024

Production reference: 1250924

Published by Packt Publishing Ltd.

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ISBN: 978-1-83620-091-8

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Contributors

About the author

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, and as a student, he wrote and registered a patent for one of the earliest word2vector embeddings and word piece tokenization solutions. He started a company focused on deploying AI and went on to author one of the first AI cognitive NLP chatbots, applied as a language teaching tool for Moët et Chandon (part of LVMH) and more. Denis rapidly became an expert in explainable AI, incorporating interpretable, acceptance-based explanation data and interfaces into solutions implemented for major corporate projects in the aerospace, apparel, and supply chain sectors. His core belief is that you only really know something once you have taught somebody how to do it.

About the reviewers

Alberto Romero has always had a passion for technology and open source, from programming at the age of 12 to hacking the Linux kernel by 14 back in the 90s. In 2017, he co-founded an AI startup and served as its CTO for six years, building an award-winning InsurTech platform from scratch. He currently continues to design and build generative AI platforms in financial services, leading multiple initiatives in this space. He has developed and productionized numerous AI products that automate and improve decision-making processes, already serving thousands of users. He serves as an advisor to an advanced data security and governance startup that leverages predictive ML and Generative AI to address modern enterprise data security challenges.

I would like to express my deepest gratitude to my wife, Alicia, and daughters, Adriana and Catalina, for their unwavering support throughout the process of reviewing this book. Their patience, encouragement, and love have been invaluable, and I am truly fortunate to have them by my side.

Shubham Garg is a senior applied scientist at Amazon, specializing in developing Large Language Models (LLMs) and Vision-Language Models (VLMs). He has led innovative projects at Amazon and IBM, including developing Alexa’s translation features, dynamic prompt construction, and optimizing AI tools. Shubham has contributed to advancements in NLP, multilingual models, and AI-driven solutions. He has published at major NLP conferences, reviewed for conferences and journals, and holds a patent. His deep expertise in AI technologies makes his perspective as a reviewer both valuable and insightful.

Tamilselvan Subramanian is a seasoned AI leader and two-time founder, specializing in generative AI for text and images. He has built and scaled AI-driven products, including an AI conservation platform to save endangered species, a medical image diagnostic platform, an AI-driven EV leasing platform, and an Enterprise AI platform from scratch. Tamil has authored multiple AI articles published in medical journals and holds two patents in AI and image processing. He has served as a technical architect and consultant for finance and energy companies across Europe, the US, and Australia, and has also worked for IBM and Wipro. Currently, he focuses on cutting-edge applications of computer vision, text, and generative AI.

My special thanks go to my wife Suganthi, my son Sanjeev, and my mom and dad for their unwavering support, allowing me the personal time to work on this book.

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