The bm25() relevance score in FTS5 returns a negative float. Lower means more relevant. I ordered results DESC for three days and wondered why garbage surfaced first.
I run 12 production Workers handling Korean D2C ad-ops. One indexes ~400K ad keywords — brand names, product slugs, mixed Korean/Latin search terms — and drives a dashboard that rerenders on every keystroke. The original LIKE '%키워드%' scans were clocking 180–220ms in D1. That's a hard no for typeahead. FTS5 is shipped enabled in D1, no opt-in required, so the migration looked straightforward. It wasn't.
The first real trap is Korean tokenization. FTS5's default unicode61 tokenizer splits on whitespace and punctuation. Korean doesn't use spaces between morphemes consistently — a single compound word can contain what a user types as two separate search tokens. That mismatch kills recall on single-morpheme queries. By week 3 at 180K rows I was measuring 88% recall on a hand-verified 500-row golden set, down from 91% at 40K rows. The drop isn't catastrophic but it's directional, and it gets worse as the index grows with noisier long-tail terms. The mitigation I landed on was query-side: splitting user input and joining tokens with OR before passing to MATCH, which recovers most of the recall loss without touching the tokenizer.
