Implementing Vector Search in Azure AI Search

Modern search experiences require more than simple keyword matching; they need to understand the semantic meaning behind user queries. This text-based course guides you through the core concepts of vector search and how to implement it using Azure AI Search to power next-generation information retrieval.\n\nYou will transition from traditional keyword search to building sophisticated search systems that utilize vector embeddings. By reading detailed explanations and analyzing practical code examples, you will learn how to index, query, and rank data to deliver highly relevant search results for modern AI applications.\n\nWhat you'll learn:\n- Understand the fundamentals of vector embeddings, distance metrics, and vector databases\n- Configure vector fields and index schemas within Azure AI Search\n- Generate embeddings and ingest vector data using modern integration patterns\n- Execute vector queries and fine-tune search results using similarity metrics\n- Implement hybrid search strategies that combine keyword and vector retrieval\n- Apply semantic ranking to significantly improve the relevance of search outputs\n- Integrate search retrieval with Large Language Models for Retrieval-Augmented Generation (RAG) patterns\n\nThe course begins with foundational concepts of vector search and embedding models before moving into the step-by-step configuration of search indexes. You will then progress to executing hybrid queries and optimizing relevance using semantic rankers.\n\nThis course is designed for software developers, database administrators, and cloud professionals who are new to vector databases and want to learn how to implement intelligent search. No prior experience with vector search is required.\n\nStart reading today to unlock the power of semantic search in your applications.