Vector Search and Embeddings with Vertex AI

Modern search applications require more than simple keyword matching; they need to understand the semantic meaning behind user queries. This text-based course introduces you to the fundamentals of vector search and embeddings, enabling you to build intelligent search systems on the cloud. You will transition from a beginner to a confident practitioner capable of representing unstructured data as dense vectors and querying them in milliseconds. You will learn how to leverage Vertex AI Vector Search to power semantic search, recommendation engines, and retrieval-augmented generation (RAG) pipelines. What you'll learn: - Understand the core concepts of vector spaces, dense embeddings, and similarity metrics like cosine distance. - Generate high-quality text embeddings using modern machine learning APIs. - Configure and deploy vector databases and index endpoints on Vertex AI. - Query vector indexes efficiently to retrieve semantically similar documents. - Apply vector search patterns to power Retrieval-Augmented Generation (RAG) for large language models. - Implement best practices for updating, managing, and scaling vector indexes in production. We begin with foundational definitions of vector embeddings and semantic search, ensuring you understand the theory before writing any code. From there, you will read through step-by-step written explanations and code configurations to build, deploy, and query your first cloud-based vector index. This course is designed for beginner developers, data enthusiasts, and cloud practitioners who want to learn semantic search from scratch. No prior experience with vector databases or machine learning is required. Start reading today to unlock the power of semantic search in your applications.