EmergentDB: How Evolutionary Algorithms Made My Vector Database 82x Faster

Vector databases power semantic search, RAG, and recommendation systems—but tuning them is a nightmare. Should HNSW use M=8 or M=32? What’s the right ef_construction value? Most teams guess and pray.

EmergentDB solves this through evolutionary computation. Instead of manual tuning, it uses the MAP-Elites algorithm to evolve the optimal index configuration for your specific workload. The system maintains a “quality-diversity grid” that explores the trade-off space between recall, latency, and memory—automatically selecting between HNSW, Flat, and IVF indices with evolved hyperparameters.