The Rise of Vector Search: From Word Embeddings to Production Systems

Vector search represents a paradigm shift from keyword matching to semantic understanding. By converting text into dense vector representations using models like BERT, E5, or BGE-m3, search systems can find conceptually similar content even when exact keywords differ. This article traces the evolution from early word2vec embeddings through transformer-based models to modern production systems. We examine approximate nearest neighbor (ANN) algorithms including HNSW, IVF, and product quantization that make billion-scale vector search practical. Integration patterns with traditional lexical search (hybrid search) combine the precision of keyword matching with the recall of semantic search. Practical considerations include embedding model selection, vector dimensions vs accuracy tradeoffs, index update strategies, and monitoring embedding drift over time.