MLSolutionUtil - Global

concept brazil Updated Sep 10, 2026 2 min read MLSolutionUtilAPI.md
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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.

Parent Topic:Server API reference

Related topics

MLSolution

MLSolutionFactory

MLSolutionUtil - MLSolutionUtil()

Instantiates a new MLSolutionUtil object.

Name Type Description
None
JavaScript
var mlSolutionUtil = new MLSolutionUtil();

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

Gets predictions for one or more specified solutions.

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.

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));

Text
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: { ... }

Text

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