Beyond Keywords: How Vector Search Is Transforming Document Discovery

The evolution from lexical-based to embedding-based similarity search marks a fundamental shift in how we discover related documents. While traditional keyword-matching approaches fail to connect documents written differently but discussing the same topic, modern vector embeddings capture semantic meaning, enabling search systems to find truly similar content regardless of terminology. This transition powers critical use cases in e-commerce recommendations, customer support automation, and enterprise search—making search engines smarter and more intuitive for end users.