AI Development with Azure Cosmos DB for NoSQL and Vector Search

Modern AI applications require robust, scalable databases capable of handling high-dimensional vector data alongside traditional transactional workloads. This text-based course guides you through utilizing Azure Cosmos DB for NoSQL as the backbone of your intelligent applications. You will transition from understanding basic NoSQL concepts to designing and implementing advanced retrieval-augmented generation (RAG) pipelines, enabling semantic search capabilities directly within your database. What you'll learn: - Understand foundational NoSQL concepts and how Azure Cosmos DB stores structured and semi-structured data. - Configure vector indexing and search capabilities within Azure Cosmos DB for NoSQL containers. - Build semantic retrieval systems that connect large language models with your stored vector embeddings. - Optimize query performance and index policies specifically designed for high-dimensional vector data. - Apply best practices for structuring data in RAG pipelines to ensure fast, relevant AI responses. The course starts with essential terminology and database foundations before moving into step-by-step written explanations on vector configuration, query optimization, and real-world AI integration patterns. Designed for software developers and data enthusiasts who are new to AI database integrations, this course requires no prior experience with vector databases. Start reading today to unlock the power of AI-driven search in your database applications.