What if SQL could search by meaning? Meet VelesQL

You know SQL. You have been writing SELECT, WHERE, and JOIN for years. But the moment you need to search by meaning, traverse a knowledge graph, or rank results by relevance, SQL cannot help you. You reach for a proprietary API, a different client, a different mental model. What if you did not have to? VelesQL is a query language that starts where SQL stops. It keeps the syntax you already know and adds three things SQL never had: vector similarity search (NEAR), graph pattern matching (MATCH), and full-text BM25 ranking. One language. One query. One result set. This article walks through VelesQL from the ground up, with working Python code you can run right now. The problem with multi-model queries A typical AI application needs three types of search: 1. Vector search → "Find documents similar to this embedding" 2. Graph traversal → "Walk relationships between entities" 3. Full-text search → "Rank documents containing these keywords" Today, most developers use three separate systems f