Classify each scene against your own taxonomy

Reuses the classification that Elemental Inference has already produced, and asks the model only to map it onto categories of your own – a much cheaper prompt than sending video. Requires an enabled contextual metadata output on the feed, and a per-scene trigger.

Request expression (Anthropic Claude models)

{
  "anthropic_version": "bedrock-2023-05-31",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Scene summary: " & (media.contextual.summary != null ? media.contextual.summary : "none")
                & "\nIAB categories: " & $join(media.contextual.categories, ", ")
                & "\nObjects: " & $join(media.contextual.objects, ", ")
                & "\nBrand-safety risk: " & media.contextual.garm.risk
                & "\n\nChoose the single best match from this list: sport-live, sport-studio, advertising, news, other."
                & " Reply with only a JSON object with keys segment and confidence, where confidence is a number from 0 to 1."
                & " Don't wrap it in a code block."
        }
      ]
    }
  ]
}

summary is null when the contextual metadata output doesn't generate summaries, so the test is != null. $exists() returns true for a null value, which would put the word null in the prompt.

Response expression. Parsing the model's text stores structured data rather than a string that your application has to parse again.

(
  $answer := $parse(content[type = "text"][0].text);
  {
    "segment": $answer.segment,
    "confidence": $answer.confidence
  }
)

Function output

{
  "name": "segment-class",
  "status": "ENABLED",
  "outputConfig": {
    "function": {
      "functionArn": "arn:aws:elemental-inference:us-west-2:111122223333:function/function-id",
      "trigger": { "perScene": {} },
      "contextualMetadataSettings": { "outputName": "ctx-md" }
    }
  }
}

Result

{"segment":"sport-live","confidence":0.92}