MLSolutionUtil - Global

  • 릴리스 버전: Australia
  • 업데이트 날짜 2026년 03월 12일
  • 소요 시간: 4분

    • The MLSolutionUtil script include provides methods for getting Predictive Intelligence predictions.

    This script include requires the Predictive Intelligence plugin (com.glide.platform_ml) and is provided within the sn_ml namespace.

    For more information, see Using ML APIs.

    MLSolutionUtil - MLSolutionUtil()

    Instantiates a new MLSolutionUtil object.

    표 1. Parameters
    Name Type Description
    None 
    
var mlSolutionUtil = new MLSolutionUtil();

    MLSolutionUtil - getPredictions(Object input, Array solutionNames, Object options)

    Gets predictions for one or more specified solutions.

    표 2. Parameters
    Name Type Description
    input Object GlideRecord or array of JSON objects as key-value pairs.
    solutionNames Array Array of solution names to retrieve predictions from.
    options Object Optional. JSON object key-value pair with the following properties:
    • top_n: Number. If provided, returns the top results, up to the specified number of predictions.
    • apply_threshold: Boolean. Checks the threshold value for the solution and applies it to the result set. The threshold value is solution threshold for similarity or class-level threshold for classification. Default value is true.
    • custom_results_filter: String. Similarity solutions only. Specifies the allowed set from which results are returned using an encoded query.
    표 3. Returns
    Type Description
    Array JSON key-value pair containing the prediction result grouped by solution name and sorted by sys_id or record_number.
    • predictedValue: String. Value representing the prediction result.
    • predictedSysId: String. The sys_id of the predicted value. Results can be from any table on which information is being predicted.
    • confidence: Number. Value of the confidence associated with the prediction. For example, 53.84.
    • threshold: Number. Value of the configured threshold associated with the prediction.
    		 
    var solutionNames = ['soluton1', 'solution2'];

var input = new GlideRecord("incident");
input.get("0ef47232db801300864adfea5e961912");

// configure optional parameters
var options = {};
options.top_n = 3;
options.apply_threshold = false;

var mlSolutionUtil = new MLSolutionUtil();
var results = mlSolutionUtil.getPredictions(input, solutionNames, options);

// pretty print JSON results
gs.print(JSON.stringify(JSON.parse(results), null, 2));

    Output:

    {
  solution1:  {
    input_gr_sys_id1: [
                {
                    predictedValue : xxx,
                    predictedSysId : xx0,
                    confidence : xxx,
                    threshold : xxx

                }, 
                {
                    predictedValue : yyy,
                    predictedSysId : xx1,
                    confidence : xxx,
                    threshold : xxx
                }
        ],
    input_gr_sys_id2 : [
                {
                    predictedValue : xxx,
                    predictedSysId : xx0,
                    confidence : xxx,
                    threshold : xxx

                }, 
            ...
        ]
  }

  solution2:  {
      ...
}