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Total Size:
331.7 MB
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908B15DD52DEEFD3780E7877956795FB6C5930BC
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April 21, 2026, 12:05 a.m.
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(Last updated: April 21, 2026, 12:11 a.m.)
| File | Size |
|---|---|
| Rothman D. RAG-Driven Generative AI 2024.pdf | 15.7 MB |
| Code.zip | 316.0 MB |
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331.7 MB
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2026-04-21
| Uploaded by andryold1 | Size 331.7 MB | Health [ 12 /1 ] | Added 2026-04-21 |
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941.7 MB
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| Uploaded by TvTeam | Size 941.7 MB | Health [ 692 /470 ] | Added 2025-11-05 |
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36.9 MB
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| Uploaded by indexFroggy | Size 36.9 MB | Health [ 15 /3 ] | Added 2025-02-12 |
NOTE
SOURCE: Rothman D. RAG-Driven Generative AI 2024
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COVER
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MEDIAINFO
Textbook in PDF format Topic Covers: Chapter 1, Why Retrieval Augmented Generation?, introduces RAG’s foundational concepts, outlines its adaptability across different data types, and navigates the complexities of integrating the RAG framework into existing AI platforms. By the end of this chapter, you will have gained a solid understanding of RAG and practical experience in building diverse RAG configurations for naïve, advanced, and modular RAG using Python, preparing you for more advanced applications in subsequent chapters. Chapter 2, RAG Embedding Vector Stores with Deep Lake and OpenAI, dives into the complexities of RAG-driven generative AI by focusing on embedding vectors and their storage solutions. We explore the transition from raw data to organised vector stores using Active loop Deep Lake and OpenAI models, detailing the process of creating and managing embeddings that capture deep semantic meanings. You will learn to build a scalable, multi-team RAG pipeline from scratch in Python by dissecting the RAG ecosystem into independent components. By the end, you’ll be equipped to handle large datasets with sophisticated retrieval capabilities, enhancing generative AI outputs with embedded document vectors. Chapter 3, Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI, dives into index-based RAG, focusing on enhancing AI’s precision, speed, and transparency through indexing. We’ll see how LlamaIndex, Deep Lake, and OpenAI can be integrated to put together a traceable and efficient RAG pipeline. Through practical examples, including a domain-specific drone technology project, you will learn to manage and optimize index-based retrieval systems. By the end, you will be proficient in using various indexing types and understand how to enhance the data integrity and quality of your AI outputs. Chapter 4, Multimodal Modular RAG for Drone Technology, raises the bar of all generative AI applications by introducing a multimodal modular RAG framework tailored for drone technology. We’ll develop a generative AI system that not only processes textual information but also integrates advanced image recognition capabilities. You’ll learn to build and optimize a Python-based multimodal modular RAG system, using tools like LlamaIndex, Deep Lake, and OpenAI, to produce rich, context-aware responses to queries. “Chapter 5, Boosting RAG Performance with Expert Human Feedback, introduces adaptive RAG, an innovative enhancement to standard RAG that incorporates human feedback into the generative AI process. By integrating expert feedback directly, we will create a hybrid adaptive RAG system using Python, exploring the integration of human feedback loops to refine data continuously and improve the relevance and accuracy of AI responses. Chapter 6, Scaling RAG Bank Customer Data with Pinecone, guides you through building a recommendation system to minimize bank customer churn, starting with data acquisition and exploratory analysis using a Kaggle dataset. You’ll move onto embedding and upserting large data volumes with Pinecone and OpenAI’s technologies, culminating in developing AI-driven recommendations with GPT-4o. By the end, you’ll know how to implement advanced vector storage techniques and AI-driven analytics to enhance customer retention strategies. Chapter 7, Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex, details the development of three pipelines: data collection from Wikipedia, populating a Deep Lake vector store, and implementing a knowledge graph index-based RAG. You’ll learn to automate data retrieval and preparation, create and query a knowledge graph to visualize complex data relationships, and enhance AI-generated responses with structured data insights. You’ll be equipped by the end to build and manage a knowledge graph-based RAG system, providing precise, context-aware output. Chapter 8, Dynamic RAG with Chroma and Hugging Face Llama, explores dynamic RAG using Chroma and Hugging Face’s Llama technology. It introduces the concept of creating temporary data collections daily, optimized for specific meetings or tasks, which avoids long-term data storage issues. You will learn to build a Python program that manages and queries these transient datasets efficiently, ensuring that the most relevant and up-to-date information supports every meeting or decision point. By the end, you will be able to implement dynamic RAG systems that enhance responsiveness and precision in data-driven environments. Chapter 9, Empowering AI Models: Fine-Tuning RAG Data and Human Feedback, focuses on fine-tuning techniques to streamline RAG data, emphasizing how to transform extensive, non-parametric raw data into a more manageable, parametric format with trained weights suitable for continued AI interactions. You’ll explore the process of preparing and fine-tuning a dataset, using OpenAI’s tools to convert data into prompt and completion pairs for machine learning. Additionally, this chapter will guide you through using OpenAI’s GPT-4o-mini model for fine-tuning, assessing its efficiency and cost-effectiveness. Chapter 10, RAG for Video Stock Production with Pinecone and OpenAI, explores the integration of RAG in video stock production, combining human creativity with AI-driven automation. It details constructing an AI system that produces, comments on, and labels video content, using OpenAI’s text-to-video and vision models alongside Pinecone’s vector storage capabilities. Starting with video generation and technical commentary, the journey extends to managing embedded video data within a Pinecone vector store
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