RAG-Driven Generative AI: Build MAS-RAG with DualRAG, GraphRAG, multimodal video pipelines, and Oracle Database 23ai

Building MAS-RAG (multi-agent AI systems for RAG) that reason over real-world data using hybrid retrieval and scalable architectures for production use. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Master DualRAG by combining vector search with SQL filtering over structured enterprise data Implement GraphRAG, Spatial-RAG, and vector search natively in Oracle Database 23ai Build multimodal video pipelines with human-feedback loops and fine-tuned models Book DescriptionEnterprise AI systems often rely on moving sensitive data into external platforms, creating challenges around fragmentation, latency, and security. This edition defines an architectural shift by bringing AI closer to the enterprise data using Oracle Database 23ai as a converged engine. You will learn how to design Retrieval-Augmented Generation systems that operate directly on structured and unstructured data. Through the implementation of DualRAG, the book demonstrates how to synchronize unstructured vector semantics with the deterministic truth of structured SQL, graph, and spatial retrieval, enabling systems to reason over verified corporate facts rather than probabilistic guesses. You’ll move beyond simple pipelines to build MAS-RAG (multi-agent systems for RAG) that coordinate autonomous agents across hybrid retrieval workflows, multimodal video pipelines, and graph-based knowledge structures. Designed for developers and architects, these blueprints transform disconnected data silos into a unified engine to architect autonomous enterprise intelligence that scales with RLHF and model fine-tuning. By the end of the book, you’ll be able to design and deploy enterprise AI systems that combine retrieval, reasoning, and structured data to build reliable generative AI applications. *Email sign-up and proof of purchase requiredWhat you will learn Implement vector search and hybrid SQL–vector retrieval using Oracle Database 23ai Implement native GraphRAG, Spatial-RAG, and converged retrieval Build AI agents and orchestration systems that coordinate multiple retrieval workflows Improve model accuracy using human feedback, adaptive logic, and fine-tuning Build multimodal video pipelines for visual and semantic search Optimize high-performance AI workloads on Oracle Cloud and Exadata Who this book is forThis book is for AI engineers, ML engineers, data scientists, and MLOps professionals who want to build production-ready generative AI systems grounded in enterprise data. It will also benefit solutions architects, database engineers, and software developers looking to integrate large language models with structured and unstructured data sources using modern retrieval architectures. Readers should be comfortable with Python and have a basic understanding of machine learning concepts. Prior experience with generative AI or vector databases will help you get the most out of this book.

Table of Contents:
Table of Contents

1. Why Retrieval-Augmented Generation?
2. RAG Embeddings in Oracle Vector Stores
3. Building a Live Recruiter Agent
4. Building Sovereign Enterprise Agents
5. Building a Universal Context Engine
6. Operationalizing the Universal Context Engine
7. Empowering AI Models by Fine-Tuning RAG Data
8. Boosting RAG Performance with Human Feedback
9. Building a Conversational RAG Agent
10. Building an Agent with Spatial-RAG and GraphRAG
11. Scaling AI Workloads with Oracle Exadata
12. The Autonomous Database Architect