{
  "opencollection": "1.0.0",
  "info": {
    "name": "Cerebrium Cortex Inference API",
    "version": "v4"
  },
  "request": {
    "auth": {
      "type": "bearer",
      "token": "{{bearerToken}}"
    }
  },
  "items": [
    {
      "info": {
        "name": "Inference",
        "type": "folder"
      },
      "items": [
        {
          "info": {
            "name": "Invoke a deployed function",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://api.aws.us-east-1.cerebrium.ai/v4/:projectId/:appName/:functionName",
            "headers": [
              {
                "name": "Accept",
                "value": ""
              }
            ],
            "params": [
              {
                "name": "projectId",
                "value": "",
                "type": "path",
                "description": "Cerebrium project identifier, for example p-xxxxxxxx."
              },
              {
                "name": "appName",
                "value": "",
                "type": "path",
                "description": "Deployed app name from cerebrium.toml."
              },
              {
                "name": "functionName",
                "value": "",
                "type": "path",
                "description": "Public function exposed by the app (for example run or predict). Functions prefixed with an underscore are private and not callable."
              },
              {
                "name": "async",
                "value": "",
                "type": "query",
                "description": "When true, the request is accepted for asynchronous execution and the call returns 202 with a run_id instead of the result."
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Calls a deployed Cortex function. The JSON request body maps directly to the function's parameters. Returns a JSON object containing run_id, run_time_ms, and result. Use the function name `run` (or `predict`, or any public function defined in the app) as documented in examples. To stream output, send Accept: text/event-stream and have the function yield data; the response is a text/event-stream of `data:` lines."
        }
      ]
    }
  ],
  "bundled": true
}