pgvector Works Without AI Models: How Feature Vectors Can Beat Semantic Search

Most developers associate pgvector with AI-powered semantic search, but the Postgres extension is fundamentally just a tool for finding nearest vectors — regardless of how those vectors are created. Engineers building a baseball 'find similar players' feature discovered that hand-crafted feature vectors, built from structured data using plain SQL arithmetic, can outperform embedding models for certain similarity problems. Unlike embeddings produced by machine learning models, feature vectors are explicitly designed by the developer, with each dimension carrying a named, interpretable meaning. The key distinction is that embeddings suit unstructured data where relevant features are unknown, while feature vectors excel when the data is structured and similarity criteria are well understood. The broader lesson is that pgvector's distance operators and approximate-nearest-neighbor indexes work equally well on any numeric vector, opening a wider design space than AI-centric tutorials typically suggest.