RAG-Driven Generative AI
Build MAS-RAG with DualRAG, GraphRAG, multimodal video pipelines, and Oracle Database 23ai
Created by Denis Rothman
Explore how to architect advanced retrieval-augmented generation systems directly within Oracle Database 23ai. Learn to build multi-agent RAG pipelines, integrate video and graph data, and synchronize structured and unstructured information for reliable enterprise AI. Gain practical skills for deploying scalable, production-ready generative AI solutions.
Packt | Apr 2026 | 430 min
What You Will Learn
You will start by building a strong foundation in RAG concepts, then progressively add skills in vector retrieval, hybrid database queries, and AI agent orchestration. As you advance, you will combine these elements to create unified context engines and scale them across enterprise environments, culminating in robust, autonomous AI architectures.
Key Features
- Combine vector search with SQL and graph queries for accurate, context-rich retrieval
- Design multi-agent RAG pipelines that coordinate across structured and unstructured data
- Integrate multimodal video and spatial data into generative AI workflows using Oracle 23ai
Target Audience
Ideal for AI engineers, data scientists, and architects ready to build production-grade generative AI grounded in enterprise data. If you are comfortable with Python and basic machine learning, and want to integrate large language models with structured and unstructured data, you will find actionable strategies and practical guidance here.
