AI Search 1.30.0: Products Are Now Found Through Their Categories and Tags

A shopper types “trainers”. Your store sells them – but the product is tagged “Sneakers” and the description never uses the word. Until this release, that product stayed invisible. AI Search 1.30.0 fixes the oldest known limitation in the plugin: products are now found through their categories and tags, not only through their own text.

What was broken

Since version 1.26 the plugin has understood taxonomy terms: every category, tag and attribute term gets its own meaning fingerprint, and a product whose term matches the query gets a scoring boost. The catch was that this only ever worked as a bonus on products the search had already picked up through their own description. A product whose only real signal was its category could never clear the bar – its content score was near zero, and a bonus on almost nothing is still almost nothing.

In practice that meant well-tagged catalogs were leaving matches on the table, precisely for the queries where customers use their own vocabulary instead of yours.

What changed

Search now runs a second retrieval pass. The query is compared once against every term that has an embedding; terms that genuinely match pull in the products attached to them, straight from the taxonomy tables. Those products join the results with a deliberately conservative score – a term-only match is real evidence, but weaker than a product whose own text matched, and it can never outrank an exact SKU or title match. Nothing that already worked ranks lower; the results simply include products that were previously missing.

How we picked the cutoff

A term only pulls in its products when it is close enough to the query – and picking “close enough” deserved measurement rather than a guess. Our first instinct was 0.60 similarity. Testing against a real “Sneakers” tag showed why that would have shipped a feature that does nothing: “trainers” scores 0.53 against it, “shoes” 0.61, “sneakers” itself 0.82. Terms are short strings, so their similarity scores sit lower than document-length text.

The same test run against unrelated queries gave the other half of the answer: “kitchen table” 0.19, “umbrella” 0.18, “winter coat” 0.26. Synonyms cluster above 0.5, unrelated queries below 0.3, and the cutoff belongs in the gap – so it sits at 0.45. If your taxonomy needs something different, the new ai_search_term_match_threshold filter takes a per-taxonomy value.

Do I need to do anything?

Update the plugin, and make sure term embeddings are switched on and generated – that is the Indexing tab, where “Term embeddings” shows how many of your terms are indexed. WooCommerce stores have this on by default. If your terms were indexed back in 1.26, there is nothing else to do: the new retrieval pass uses the embeddings you already have.

The ranking order, unchanged

Worth repeating, because it is the promise the whole plugin rests on: exact SKU and title matches first, then everything your traditional keyword search finds, then AI results – and now, among those AI results, products reachable through their categories and tags. You can watch all of it happen on the live demo, including what happens when you push the similarity threshold too high.

Get AI Search 1.30.0 on WordPress.org – free up to 10,000 embeddings, no API key required.

Published August 2026.