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Release: Australia · Updated: 2026-03-12 · Official documentation · View source

MLSolution- Global

The MLSolution API provides methods for handling Predictive Intelligence predictions and retrieving solution objects for all capabilities: similarity, classification, and clustering).

This API provides methods commonly used among all solution capabilities and methods unique to classification and clustering solutions.

Common methods used for all solution objects:
- getCapability()
- getVersion()
- isActive()
- predict()
Methods only used for classification and regression solutions:
- applyPrediction()
Methods only used for clustering solutions:
- getClusterAssignments()
- getClusterForRecord()
- getClusterInfo()

The MLSolution API requires the Predictive Intelligence plugin (com.glide.platform_ml) and is provided within the sn_ml namespace.

The methods in this object are instantiated using MLSolutionFactory.

Parent Topic:Server API reference

Related topics

MLSolutionUtil

REST API: Get predictions for multiple solutions

MLSolution - applyPrediction(GlideRecord now_GR)

Gets the prediction result for a classification solution and applies it to the input GlideRecord if the confidence value is higher than the threshold.

Name	Type	Description
now_GR	GlideRecord	GlideRecord object containing values on which to run a prediction and apply the results.

Type	Description
Boolean	Flag that indicates whether the prediction was applied: - true: Prediction applied - false: Prediction rejected

var mlSolution = sn_ml.MLSolutionFactory.getSolution("ml_incident_categorization");

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

mlSolution.applyPrediction(inputGR);

MLSolution - getCapability()

Gets the capability information of a trained solution.

Name	Type	Description
None

Type	Description
String	Type of trained solution.Possible values: - Similarity - Classification - Clustering

var mlSolution = sn_ml.MLSolutionFactory.getSolution("ml_x_global_clustering");

// configure optional parameters
var options = {};
options.group_by = 'network';
options.cluster_id = 1;
options.top_n_per_cluster = 3;

if (mlSolution.getCapability() == 'clustering') {
    var results = mlSolution.getClusterAssignments(options);
    // pretty print JSON results
    gs.print(JSON.stringify(JSON.parse(results), null, 2));
}

MLSolution - getClusterAssignments(Object options)

Gets assignments for a clustering solution.

Name	Type	Description
options	Object	Optional. Values to use to narrow down the returned results by group and level within a clustering solution. Default: Return cluster memberships for all clusters.
options.group\_by	String	Optional. Identifies the segmentation field for which to retrieve cluster memberships, for example, assignment_group.This field provides the same grouping as options provided in the Use Group By check box in the Clustering Definition form. The information provided varies based on the table selected in the Table field. For more information, see Create and train a clustering solution.
options.cluster\_id	String	Optional. Cluster ID of a trained solution in the Cluster Summary \[ml\_cluster\_summary\] table.
options.top\_n\_per\_cluster	Number	Number of top results to receive for each cluster.

Type	Description
Object	JSON array containing cluster information in increasing order by cluster\_id:- cluster\_id: String. Unique cluster number within a solution of clusters. - rec\_display\_id: String. Record type and number. - rec\_sys\_id: String. Record sys\_id. - group\_by: If grouped, name of the segmentation field associated with this cluster.

var mlSolution = sn_ml.MLSolutionFactory.getSolution("ml_x_global_clustering");

// configure optional parameters
var options = {};
options.group_by = 'network';
options.cluster_id = '1';
// returns top 3 results per cluster
options.top_n_per_cluster = 3;

var results = mlSolution.getClusterAssignments(options);

Output:

[{"cluster_id":"1","rec_display_id":"Incident: INC0014483","rec_sys_id":"04e33e7adb401300864adfea5e961900","group_by":"network"},
 {"cluster_id":"1","rec_display_id":"Incident: INC0011133","rec_sys_id":"5bd23af2db401300864adfea5e96194d","group_by":"network"}]

MLSolution - getClusterForRecord(GlideRecord now_GR)

Gets the cluster information for a clustering solution.

Name	Type	Description
now_GR	GlideRecord	Name of the input GlideRecord.

Type	Description
String	The cluster_id from the Cluster Summary [ml_cluster_summary] table if the record belongs to a cluster. Empty string returns if the record does not belong to a cluster.

var mlSolution = sn_ml.MLSolutionFactory.getSolution("solution_name");
var now_GR = new GlideRecord('incident');
if (mlSolution.getCapability() == 'clustering') {
var clusterId = mlSolution.getClusterForRecord(now_GR);
}

MLSolution - getClusterInfo(Object options)

Gets information for a specified clustering solution.

Name	Type	Description
options	Object	Optional. Narrows down the returned results by group and level within a clustering solution. Default: Return cluster memberships for all clusters.
options.group\_by	String	Optional. Identifies the segmentation field for which to retrieve cluster memberships, for example, assignment_group.This field provides the same grouping as options provided in the Use Group By check box in the Clustering Definition form. The information provided varies based on the table selected in the Table field. For more information, see Create and train a clustering solution.
options.cluster\_id	String	Optional. Cluster ID of a trained solution in the Cluster Summary \[ml\_cluster\_summary\] table.

Type

Description

Object

JSON array containing cluster information in increasing order by cluster\_id:- cluster\_id: String. Unique cluster number within a solution of clusters. - cluster\_quality: String. Number from 0 to 100. Higher numbers indicate higher cluster density. - cluster\_size: String. Number of records in a cluster. - group\_by: If grouped, name of the segmentation field associated with this cluster. - cluster\_concept: String. Set of words that describe the cluster in descending order of frequency.

var mlSolution = sn_ml.MLSolutionFactory.getSolution("ml_x_global_clustering");

// configure optional parameters
var options = {};
options.group_by = 'network';
options.cluster_id = 1;

var results = mlSolution.getClusterInfo(options);

Output:

[ {"cluster_id":"1","cluster_quality":"56.6","cluster_size":"46","group_by":"SLA","cluster_concept":"issue occur capacity ..."},
  {"cluster_id":"2","cluster_quality":"55.47","cluster_size":"75","group_by":"SLA","cluster_concept":"clone instance request ..."},
... ]

MLSolution - getVersion()

Gets the version of the active solution.

Name	Type	Description
None

Type	Description
String	Version of the active solution.

var mlSolution = sn_ml.MLSolutionFactory.getSolution("solution_name");

var solutionVersion = mlSolution.getVersion();

MLSolution - isActive()

Determines if a solution is active.

Name	Type	Description
None

Type	Description
Boolean	Flag that indicates whether the solution is active.- true: Solution active - false: Solution inactive

var mlSolution = sn_ml.MLSolutionFactory.getSolution("solution_name");

var isActive = mlSolution.isActive();

MLSolution - predict(Object input, Object options)

Gets the prediction results from the prediction server given a GlideRecord or an array of key-value pairs.

You can use this method to call prediction with multiple input records because GlideRecord is an iterator.

Name	Type	Description
input	Object	GlideRecord or array of JSON objects containing field names and values as key-value pairs.
options	Object	Optional. JSON 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

Object

JSON object containing the prediction results 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 mlSolution = sn_ml.MLSolutionFactory.getSolution("ml_incident_categorization");

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

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

var results = mlSolution.predict(input, options);
// pretty print JSON results
gs.print(JSON.stringify(JSON.parse(results), null, 2));

var mlSolution = sn_ml.MLSolutionFactory.getSolution("ml_incident_categorization");

// multiple GlideRecord input
var input = new GlideRecord("incident");
input.addQuery("sys_created_onONLast week@javascript:gs.beginningOfLastWeek()@javascript:gs.endOfLastWeek()");
input.query();

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

var results = mlSolution.predict(input, options);
// pretty print JSON results
gs.print(JSON.stringify(JSON.parse(results), null, 2));

Output:

{
    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

                }, 
            ...
        ]
}

var mlSolution = sn_ml.MLSolutionFactory.getSolution("ml_incident_categorization");

// key-value pairs input
var input = [{"short_description":"my email is not working"}, 
            {short_description:"need help with password"}];

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

var results = mlSolution.predict(input, options);
// pretty print JSON results
gs.print(JSON.stringify(JSON.parse(results), null, 2));