# The leash that kept Vector Search on topic

A marketplace’s lexical search - matching the actual words in a query against the actual words in a listing - missed an entire class of searches. *“Warm jacket for winter hiking”* returned nothing for an *“insulated shell parka”,* even though any shopper could see the results could be the same. The fix was vector search: an embedding model placing queries and products as points in a high-dimensional space, so *“warm jacket”* and *“insulated shell”* sit near each other without sharing a word. For every query, the seventy nearest product points were merged into the lexical results.

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Some queries started showing no sense results. A search for *“apple watch band”* started returning a dog collars - no shared word anywhere between the two, a leap lexical search would never make. The embedding model was giving poor results some times.

The instinct was to make the embeddings smarter - retrain, bake category signals into the vectors. That was slow to iterate on and hard to fully debug: a bad result meant a retrain-and-hope cycle, not a cheap lever to pull.

The fix that shipped sat in front of vector search instead of inside it. The catalog already had a taxonomy - a broad category tree, and a more specific L2 subcategory beneath it. If a query’s plausible L2 categories could be guessed cheaply, the seventy vector results could be filtered down to the ones that matched, with the embeddings themselves untouched.

Most traffic didn’t need a model for that. A small number of distinct queries accounted for the bulk of searches, and each already had years of purchase history. A nightly SQL job counted, per exact query string, which L2 categories its purchases had landed in, and kept the ones above a minimum share — no training, just a lookup table.

That table only covered queries it had seen enough of. Typos, unusual phrasing, brand-new searches had no row to check. For those, a small multi-label classifier trained with FastText — light enough to run on every search — predicted plausible categories from the words themselves. It wasn’t asked to figure out exactly what the shopper meant, only which categories a match couldn’t possibly belong to: a coarse net for nonsense, not a fine one for intent.

The catch was how strict that net was set. Too strict, and it rejected a query’s own correct category along with the insane ones, whenever odd phrasing left the model unconfident. Too loose, and it let through some of the very noise it existed to catch.