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Send a plain-language description in the query field. Pureframe AI searches visual content and speech simultaneously by default — no need to know in advance whether the moment you want is something someone said or something that appeared on screen.
collection_id is required for text-only queries — unlike image search, there’s no whole-library text search today.

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How matching works

A text query is embedded with the same visual encoder used to index frames, so it can match frame segments by conceptual similarity even when no words overlap with anything spoken. In parallel, the raw query runs against transcript text via full-text search, matching transcript segments on shared vocabulary. See Combined Queries for how the two are merged.

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Natural language queries

For longer or more conversational queries — "find the best tracking shots of someone running on a rooftop" — set natural_language=true. An LLM decomposition step parses the query into a structured visual/transcript split before it hits the underlying search, rather than embedding the raw sentence as-is.
This is available on paid plans. The web app and MCP server enable it automatically for queries that look conversational; the flag is opt-in for direct REST and SDK calls.

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No results?

If a query returns nothing, Pureframe AI automatically tries up to two LLM-generated rephrasings of the same query before giving up — this only fires when the literal query found zero matches, so it doesn’t add latency to queries that already work.