How We Built Emoji Search at Dub with Upstash Vector

Dub replaced its Jev-based semantic emoji-search fallback with a single Upstash Vector index to let users find emoji by meaning while reducing recurring input-token costs. Jev required sending and scoring the full catalog of 1,941 emoji on every fallback search, forcing the catalog to be chunked and making costs scale with both catalog size and search volume. The vector approach embeds each emoji’s label and tags once during a seed process using data from emojibase, stores the resulting vectors with emoji metadata, and embeds only the user’s short query at search time to retrieve the closest matches. Searches return up to 18 results above a 0.45 similarity threshold, while exact name matching remains the first step; the semantic route is session-protected, rate-limited, debounced on the client, and cached in memory. Because the emoji catalog is small and rarely changes, it fits within Upstash Vector’s free tier of 10,000 daily requests, with pay-as-you-go pricing estimated at about $4 for one million searches.

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