Rethinking Vector Search: Why Personal AI Agents Don't Need Embeddings

Vector databases are widely recommended for AI agents, but at personal scale (under 1,000 documents), simpler full-text search with SQLite FTS5 and BM25 ranking can outperform embeddings while eliminating operational complexity. The article demonstrates 90%+ search accuracy using basic tools, challenging the assumption that semantic search through embeddings is always necessary. This matters because it reveals how tool selection should match actual problem scale rather than blindly following established architectural patterns.