How Vector Search Works (Embeddings, Similarity, and Meaning)

Description

In this video, you'll learn how vector search allows computers to find items based on meaning rather than just matching words. We'll delve into the mechanisms behind embeddings, similarity metrics like cosine similarity, and the practical applications that make searching easier and more intuitive. Understanding vector search is essential in today's data-driven world, as it powers everything from recommendation systems to search engines, transforming user experience by making it less frustrating and more relevant. This video is part of the Enginerds Fundamentals series—clear, calm explanations of the technologies shaping modern software and systems. CHAPTERS: 0:00 - Introduction 1:28 - Embeddings and Vector Space 2:58 - Distance Metrics Compared 4:28 - Example: Product Embeddings 5:58 - Query as Point in Vector Space 7:28 - Applications of Embeddings 8:58 - High Dimensions vs Performance 10:29 - Content Moderation with Vectors 11:59 - Synthesizing Ideas in AI 13:30 - How Indexes Guide Searches 15:00 - Fast Mapping with Similarity --- Website: https://www.enginerds.com X: https://x.com/EnginerdsNews