Server API reference › API reference › API implementation and reference

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

SimilaritySolution- Global

The SimilaritySolution API is a scriptable object used in Predictive Intelligence stores.

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

The solution setup-to-training flow is as follows:

Use the constructor to create a similarity solution object.
Add the solution object to the similarity solution store using the SimilaritySolutionStore - add() method.
Train the solution using the submitTrainingJob() method. This creates a version of the object that you can manage using the SimilaritySolutionVersion API.
Get predictions using the SimilaritySolutionVersion – predict() method.

Note: This API runs with full privileges before the Vancouver Patch 7 Hotfix 2b and Washington DC Patch 7 releases. With later releases, grant access using ACLs. For more information see Query ACLs.

For usage guidelines, refer to Using ML APIs.

Parent Topic:Server API reference

SimilaritySolution - SimilaritySolution(Object config)

Creates a similarity solution.

Name	Type	Description
config	Object	JavaScript object containing configuration properties of thesolution. `{ "domainName": "String", "label": "String", "lookupDataset": {Object}, "minRowCount": "String", "processingLanguage": "String", "stopwords": [Array], "testDataset": {Object}, "trainingFrequency": "String", "updateFrequency": "String" }`
config.domainName	String	Optional. Domain name associated with this dataset. Default: Current domain, for example, `"global"`.
config.label	String	Identifies the prediction task.
config.lookupDataset	Object	Name of the DatasetDefinition to use as the lookup set.
config.minRowCount	String	Optional. Minimum number of records required in the dataset for training.Default: 10000
config.processingLanguage	String	Optional. Processing language in two-letter ISO 639-1 language code format. Default: "en"
config.stopwords	Array	Optional. Preset list of strings that the system automatically generates based on the language property setting. For details, see Create a custom stopwords list. Default: English Stopwords
config.testDataset	Object	Name of the DatasetDefinition to scan for similarities with lookupDataset results.
config.trainingFrequency	String	Optional. The frequency to retrain the model. Possible values: - every\_30\_days - every\_60\_days - every\_90\_days - every\_120\_days - every\_180\_days - run\_once Default: run\_once
config.updateFrequency		The frequency at which the model for the solution definition must be rebuilt. Possible values: - do\_not\_update - every\_1\_day - every\_1\_hour - every\_6\_hours - every\_12\_hours - every\_1\_minute - every\_15\_minutes - every\_30\_minutes Default: do\_not\_update

The following example shows how to create an object and add it to the SimilaritySolution store.

var incidentData = new sn_ml.DatasetDefinition({
        'tableName' : 'incident',
        'fieldNames' : ['category', 'short_description']        
    });
    var kbData = new sn_ml.DatasetDefinition({
        'tableName' : 'kb_knowledge',
        'fieldNames' : ['short_description'],
        'encodedQuery' : 'active=true'
    });

    var mySolution = new sn_ml.SimilaritySolution({
        'label': "similarity solution",
        'lookupDataset' : kbData,
        'testDataset' : incidentData,              
    });

    // add solution
    var solutionName = sn_ml.SimilaritySolutionStore.add(mySolution);

SimilaritySolution - cancelTrainingJob()

Cancels a job for a solution object that has been submitted for training.

Name	Type	Description
None

Type	Description
None

The following example shows how to cancel an existing training job.

var mySolution = sn_ml.SimilaritySolutionStore.get('ml_sn_global_global_similarity');

mySolution.cancelTrainingJob();

SimilaritySolution - getActiveVersion()

Gets the active SimilaritySolutionVersion object.

Name	Type	Description
None

Type	Description
Object	Active SimilaritySolutionVersion object.

The following example shows how to get an active SimilaritySolution version from the store and return its training status.

var mlSolution = sn_ml.SimilaritySolutionStore.get('ml_x_snc_global_global_similarity');

gs.print(JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getStatus()), null, 2));

Output:

{
  "state": "solution_complete",
  "percentComplete": "100",
  "hasJobEnded": "true"
}

SimilaritySolution - getAllVersions()

Gets all versions of a SimilaritySolution object.

Name	Type	Description
None

Type	Description
Array	Existing versions of a solution object. See also SimilaritySolutionVersion API.

The following example shows how to get all SimilaritySolution version objects and call the getVersionNumber() and getStatus() solution version methods on them.

var mlSolution = sn_ml.SimilaritySolutionStore.get('ml_x_snc_global_global_Similarity');

var mlSolutionVersions = mlSolution.getAllVersions();

for (i = 0; i < mlSolutionVersions.length; i++) {
gs.print("Version " + mlSolutionVersions[i].getVersionNumber() + " Status: " + mlSolutionVersions[i].getStatus() +"\n");

Output:

Version 3 Status: {"state":"solution_complete","percentComplete":"100","hasJobEnded":"true"}

Version 2 Status: {"state":"solution_complete","percentComplete":"100","hasJobEnded":"true"}

Version 1 Status: {"state":"solution_cancelled","percentComplete":"0","hasJobEnded":"true"}

SimilaritySolution - getLatestVersion()

Gets the latest version of a solution.

Name	Type	Description
None

Type	Description
Object	SimilaritySolutionVersion object corresponding to the latest version of a SimilaritySolution.

The following example shows how to get the latest version of a solution and return its training status.

var mlSolution = sn_ml.SimilaritySolutionStore.get('ml_x_snc_global_global_Similarity');

gs.print(JSON.stringify(JSON.parse(mlSolution.getLatestVersion().getStatus()), null, 2));

Output:

{
  "state": "solution_complete",
  "percentComplete": "100",
  "hasJobEnded": "true"
}

SimilaritySolution - getName()

Gets the name of the object to use for interaction with the store.

Name	Type	Description
None

Type	Description
String	Name of the solution object.

The following example shows how to update SimilaritySolution dataset information and print the name of the object.

// Update solution
var myIncidentData = new sn_ml.DatasetDefinition({
   'tableName' : 'incident',
   'fieldNames' : ['category', 'short_description', 'priority'],
   'encodedQuery' : 'activeANYTHING'
});

var eligibleFields = JSON.parse(myIncidentData.getEligibleFields('Similarity'));

var mySimilarity = new sn_ml.SimilaritySolution({
   'label': "my Similarity solution",
   'dataset' : myIncidentData,
   'inputFieldNames': eligibleFields['eligibleInputFieldNames'],
   'predictedFieldName': 'category'
});

// update solution
sn_ml.SimilaritySolutionStore.update('ml_x_snc_global_global_my_solution_definition_4', mySimilarity);

// print solution name
gs.print('Solution Name: '+mySimilarity.getName());

Output:

Solution Name: ml_x_snc_global_global_my_solution_definition_4

SimilaritySolution - getProperties()

Gets solution object properties.

Name	Type	Description
None

Type	Description
Object	Contents of the Dataset and SimilaritySolution() object details in the SimilaritySolutionStore.`{ "domainName": "String", "label": "String", "lookupDatasetProperties": {Object}, "name": "String", "processingLanguage": "String", "scope": "String", "stopwords": [Array], "testDatasetProperties": {Object}, "trainingFrequency": "String", "updateFrequency": "String" }`
<Object>.domainName	Domain name associated with this dataset. See Domain separation and Predictive Intelligence.Type: String
<Object>.label	Identifies the prediction task. `{ "label": "my first prediction" }` Data type: String.
<Object>.lookupDatasetProperties	Details of the DatasetDefinition() object used as the lookup set. `{ "encodedQuery": "String", "fieldDetails": [Array], "fieldNames": [Array], "tableName": "String" }` Data type: Object.
<Object>.lookupDatasetProperties.tableName	Name of the table for the dataset. For example, `"tableName" : "Incident"`. Data type: String.
<Object>.lookupDatasetProperties.fieldNames	List of field names from the specified table as strings. For example, `"fieldNames" : ["short_description", "priority"]`. Data type: Array.
<Object>.lookupDatasetProperties.fieldNames.fieldDetails	List of JavaScript objects that specify field properties. `[ { "name": "String", "type": "String" } ]` Data type: Array.
<Object>.lookupDatasetProperties.fieldNames.fieldDetails.<object>.name	Name of the field defining the type of information to restrict this dataset to. Data type: String.
<Object>.lookupDatasetProperties.fieldDetails.<object>.type	Machine-learning field type. Data type: String.
<Object>.lookupDatasetProperties.fieldDetails.encodedQuery	Encoded query string in the standard platform format. See Encoded query strings.Data type: String.
<Object>.name	System-assigned name. Data type: String.
<Object>.processingLanguage	Processing language in two-letter ISO 639-1 language code format. Data type: String.
<Object>.scope	Object scope. Currently the only valid value is `global`.Data type: String
<Object>.stopwords	Optional. Preset list of strings that the system automatically generates based on the language property setting. For details, see Create a custom stopwords list. Data type: Array.
<Object>.testDatasetProperties	Details of the DatasetDefinition() object used to retrieve similarities between results searched in this model and results found in the lookupDataset. `{ "encodedQuery": "String", "fieldDetails": [Array], "fieldNames": [Array], "tableName": "String" }` Data type: Object.
<Object>.testDatasetProperties.tableName	Name of the table for the dataset. For example, `"tableName" : "Incident"`. Data type: String.
<Object>.testDatasetProperties.fieldNames	List of field names from the specified table as strings. For example, `"fieldNames" : ["short_description", "priority"]`. Data type: Array.
<Object>.testDatasetProperties.fieldNames.fieldDetails	List of JavaScript objects that specify field properties. `[ { "name": "String", "type": "String" } ]` Data type: Array.
<Object>.testDatasetProperties.fieldNames.fieldDetails.<object>.name	Name of the field defining the type of information to restrict this dataset to. Data type: String.
<Object>.testDatasetProperties.fieldDetails.<object>.type	Machine-learning field type. Data type: String.
<Object>.testDatasetProperties.fieldDetails.encodedQuery	Encoded query string in the standard platform format. Data type: String.
<Object>.trainingFrequency	The frequency to retrain the model. Possible values: - every\_30\_days - every\_60\_days - every\_90\_days - every\_120\_days - every\_180\_days - run\_once Default: run\_once Data type: String.
<Object>.updateFrequency	The frequency at which the model for the solution definition must be rebuilt. Possible values: - do\_not\_update - every\_1\_day - every\_1\_hour - every\_6\_hours - every\_12\_hours - every\_1\_minute - every\_15\_minutes - every\_30\_minutes Default: do\_not\_update Datatype: String

The following example gets properties of a solution object in the store.

var mySolution = sn_ml.SimilaritySolutionStore.get('ml_sn_global_global_similarity_solution');

gs.print(JSON.stringify(JSON.parse(mySolution.getProperties()), null, 2));

Output:

*** Script: {
  "domainName": "global",
  "label": "similarity",
  "lookupDatasetProperties": {
    "tableName": "incident",
    "fieldNames": [
      "short_description"
    ]
  },
  "name": "ml_x_snc_global_global_similarity",
  "processingLanguage": "en",
  "scope": "global",
  "stopwords": [
    "Default English Stopwords"
  ],
  "testDatasetProperties": {
    "tableName": "incident",
    "fieldNames": [
      "short_description"
    ]
  },
  "trainingFrequency": "every_30_days",
  "updateFrequency": "do_not_update"
}

SimilaritySolution - getVersion(String version)

Gets a solution by provided version number.

Name	Type	Description
version	String	Existing version number of a solution.

Type	Description
Object	Specified version of the SimilaritySolution() object on which you can call SimilaritySolutionVersion API methods.

The following example shows how to get the training status of a solution by version number.

var mlSolution = sn_ml.SimilaritySolutionStore.get('ml_x_snc_global_global_similarity');

gs.print(JSON.stringify(JSON.parse(mlSolution.getVersion('1').getStatus()), null, 2));

Output:

{
  "state": "solution_complete",
  "percentComplete": "100",
  "hasJobEnded": "true"
}

SimilaritySolution - setActiveVersion(String version)

Activates a specified version of a solution in the store.

Name	Type	Description
version	String	Name of the SimilaritySolution() object version to activate.Activating this version deactivates any other version.

Type	Description
None

The following example shows how to activate a solution version in the store.

sn_ml.SimilaritySolution.setActiveVersion("ml_x_snc_global_similarity_solution");

SimilaritySolution - submitTrainingJob()

Submits a training job.

Note: Before running this method, you must first add a solution to the store using the SimilaritySolutionStore - add() method.

Name	Type	Description
None

Type	Description
Object	SimilaritySolutionVersion object corresponding to the SimilaritySolution being trained.

// Create a dataset 
var incidentData = new sn_ml.DatasetDefinition({
    'tableName' : 'incident',
    'fieldNames' : ['category', 'short_description']
});

var kbData = new sn_ml.DatasetDefinition({
    'tableName' : 'kb_knowledge',
    'fieldNames' : ['short_description'],
    'encodedQuery' : 'active=true'
});

// Create a solution 
var mySolution = new sn_ml.SimilaritySolution({
    'label': "similarity solution",
    'lookupDataset' : kbData,
    'testDataset' : incidentData
});

// Add solution
var solutionName = sn_ml.SimilaritySolutionStore.add(mySolution);


// Train the solution - this is a long running job 
var mySimilarityVersion = mySolution.submitTrainingJob();