40ce4d46eb
Two of five food-app placeholder images shipped unfilled because
Openverse returned `[]` for the model's 3-keyword queries:
- "burger combo fries" → 0 results
- "sakura sushi platter" → 0 results
The same queries truncated to the first two words have plenty:
- "burger fries" → 240 results
- "sushi platter" → 240 results
Openverse uses strict AND-search across all keywords, so a 3-word
query that includes any low-frequency or non-matching token
zero-results even when the photos exist. The skill prompt already
nudges models toward "2-3 English keywords" but they often pick three
when the brief mentions a third descriptor (e.g. "Tasty BURGER COMBO
fries" → "burger combo fries").
Endpoint now cascades:
1. Openverse with full query.
2. If `[]` and query has > 2 words: re-query with first 2 words.
3. If still nothing usable: fall through to Wikimedia (existing path)
with the same 2-word retry safety net.
Returning the original empty result was wrong: the placeholder stays
unfilled even though a satisfactory photo for "burger fries" was one
keyword-trim away. The trade-off is losing a small amount of relevance
on the dropped 3rd keyword — but that's better than no photo at all,
and the model still drives the first two keywords which carry the
core subject.
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