SimilaritySolutionVersion - Global

  • Release version: Yokohama
  • Updated January 30, 2025
  • 7 minutes to read

    • The SimilaritySolutionVersion 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.

    This API is used is used for working with solution versions based on SimilaritySolution API objects in the SimilaritySolution store.

    The system creates a solution version each time you train a solution definition. Most versions are created during scheduled solution training.

    Methods in this API are accessible using the following SimilaritySolution methods:

    SimilaritySolutionVersion - getProperties()

    Gets solution object properties and version number.

    Table 1. Parameters
    Name Type Description
    None
    Table 2. Returns
    Type Description
    Object Contents of the Dataset and SimilaritySolution version details. Results vary by object property setup.
    {
  "domainName": "String",
  "isActive": "String",
  "label": "String",
  "lookupDatasetProperties": {Object},
  "name": "String",
  "predictedFieldName": "String",
  "processingLanguage": "String",
  "scope": "String",
  "stopwords": [Array],
  "testDatasetProperties": {Object},
  "trainingFrequency": "String",
  "updateFrequency": "String",
  "versionNumber": "String"
}
    <Object>.domainName Domain name associated with this dataset. See Domain separation and Predictive Intelligence.

    Type: String
    <Object>.isActive Flag that indicates whether this version is active.
    Valid values:
    • true: Version is active.
    • false: Version is not active.

    Data 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>.predictedFieldName Identifies a field to be trained for predictability.

    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
    <Object>.versionNumber Version number of the SimilaritySolution object.

    Data type: String.

    The following example gets properties of the active object version in the store.

    // Get properties
var mlSolution = sn_ml.SimilaritySolutionStore.get('ml_x_snc_global_similarity_solution');

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

    Output:

    *** Script: {
  "domainName": "global",
  "isActive": "true",
  "label": "similarity",
  "lookupDatasetProperties": {
    "encodedQuery": "",
    "fieldNames": [
      "short_description"
    ],
    "tableName": "incident"
  },
  "name": "ml_x_snc_global_global_similarity",
  "processingLanguage": "en",
  "stopwords": [
    "Default English Stopwords"
  ],
  "testDatasetProperties": {
    "fieldNames": [
      "short_description"
    ],
    "tableName": "incident"
  },
  "versionNumber": "3"
}

    SimilaritySolutionVersion - getStatus(Boolean includeDetails)

    Gets training completion status.

    Table 3. Parameters
    Name Type Description
    includeDetails Boolean Flag that indicates whether to return status details.
    Valid values:
    • true: Return additional details.
    • false: Don't return additional details.

    Default: False
    Table 4. Returns
    Type Description
    Object JavaScript object containing training status information for a SimilaritySolution object.
    {
  "state": "String",
  "percentComplete": "Number as a String",
  "hasJobEnded": "Boolean value as a String",
  "details": {Object}
}
    <Object>.state Training completion state. If the training job reaches a terminal state, the job does not leave that state. If the state is terminal, the hasJobEnded property is set to true.
    Possible values:
    • fetching_files_for_training
    • preparing_data
    • retry
    • solution_cancelled (terminal)
    • solution_complete (terminal)
    • solution_error (terminal)
    • solution_incomplete
    • training_request_received
    • training_request_timed_out (terminal)
    • training_solution
    • uploading_solution
    • waiting_for_training

    Data type: String
    <Object>.hasJobEnded Flag that indicates whether training is complete.
    Valid values:
    • true: Training is complete.
    • false: Training is incomplete.

    Data type: Boolean value as a String
    <Object>.percentComplete Training percent complete. If the completion percentage is less than 100, the job might be in a terminal state. For example, if training times out.

    Data type: Number as a String

    Range: 0 thru 100
    <Object>.details Object containing a list of additional training details.

    Data type: Object

    The following example shows a successful result with training complete.

    // Get status
var mlSolution = sn_ml.SimilaritySolutionStore.get('ml_x_snc_global_similarity_solution');

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

    Output:

    {
 "state":"solution_complete",
 "percentComplete":"100",
 "hasJobEnded":"true",
 "details":{"stepLabel":"Solution Complete"} // This information is only returned if getStatus(true);
}

    The following example shows an unsuccessful result with training complete.

    // Get status
var solutionName = 'ml_x_snc_global_global_similarity_solution';
var mlSolution = sn_ml.SimilaritySolutionStore.get(solutionName);
var trainingStatus = JSON.parse(mlSolution.getLatestVersion().getStatus());

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

    Output:

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

    SimilaritySolutionVersion - getVersionNumber()

    Gets the version number of a solution object.

    Table 5. Parameters
    Name Type Description
    None
    Table 6. Returns
    Type Description
    String Version number.

    The following example shows how to get a version number.

    // Get version number
var mlSolution = sn_ml.SimilaritySolutionStore.get('ml_x_snc_global_similarity_solution');

gs.print("Version number: "+JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getVersionNumber()), null, 2));

    Output:

    Version number: 1

    SimilaritySolutionVersion - predict(Object input, Object options)

    Gets the input data for a prediction.

    Table 7. Parameters
    Name Type Description
    input Object GlideRecord or array of JSON objects containing field names and values as key-value pairs.
    options Object Optional values for filtering prediction results.
    {
  "apply_threshold": Boolean,
  "top_n": Number
}
    options.apply_threshold Boolean Flag that indicates whether to check the threshold value for the solution and apply it to the result set.
    Valid values:
    • true: Return results in which confidence is greater than threshold.
    • false: Return all results.

    Default: True
    options.top_n Number If provided, returns the top results, up to the specified number of predictions.
    Table 8. Returns
    Type Description
    Object JSON object containing the prediction results sorted by sys_id or record_number. 
    {
    <identifier>: [Array]
}
    <Object>.<identifier> List of objects with details for each prediction result.

    Data type: Array of Objects

    <identifier>: [
  {
    "confidence": Number,
    "predictedSysId": "String",
    "predictedValue": "String", 
    "threshold": Number
  }
]
    <Object>.<identifier>.<object>.confidence Value of the confidence associated with the prediction. For example, 53.84.

    Data type: Number
    <Object>.<identifier>.<object>.predictedSysId The sys_id of the predicted value. Results can be from any table on which information is being predicted.

    Data type: String
    <Object>.<identifier>.<object>.predictedValue Value representing the prediction result.

    Data type: String
    <Object>.<identifier>.<object>.threshold Value of the configured threshold associated with the prediction.

    Data type: Number

    The following example shows how to display prediction results for a predict() method that takes a GlideRecord by sys_id for input and includes optional parameters to restrict to top three results and exclude the threshold value.

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

// single GlideRecord input
var input = new GlideRecord("incident");
input.get("<sys_id>");

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

var results = mlSolution.getVersion(1).predict(input, options);
// pretty print JSON results
gs.print(JSON.stringify(JSON.parse(results), null, 2));
    {
        "<sys_id/now_GR>": [
    {
      "confidence": 62.10782320780268,
      "threshold": 20.36,
      "predictedValue": "Clone Issues",
      "predictedSysId": ""
    },
    {
      "confidence": 6.945237375770391,
      "threshold": 16.63,
      "predictedValue": "Instance Administration",
      "predictedSysId": ""
    },
    {
      "confidence": 5.321061076300759,
      "threshold": 23.7,
      "predictedValue": "Administration",
      "predictedSysId": ""
    }
  ]
}

    The following example shows how to display prediction results for a predict() method that takes an array of field names as key-value pairs for input and includes optional parameters to restrict to top three results and exclude the threshold value.

    var mlSolution = sn_ml.SimilaritySolutionStore.get("ml_x_snc_global_similarity_solution");
// 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));
    {
  "1": [
    {
      "confidence": 37.5023032262591,
      "threshold": 10.72,
      "predictedValue": "Authentication",
      "predictedSysId": ""
    },
    {
      "confidence": 24.439964862166583,
      "threshold": 23.7,
      "predictedValue": "Administration",
      "predictedSysId": ""
    },
    {
      "confidence": 11.736320486031047,
      "threshold": 100,
      "predictedValue": "Security",
      "predictedSysId": ""
    }
  ],
  "2": [
    {
      "confidence": 99,
      "threshold": 17.77,
      "predictedValue": "Email",
      "predictedSysId": ""
    },
    {
      "confidence": 3.182137005157543,
      "threshold": 10.72,
      "predictedValue": "Authentication",
      "predictedSysId": ""
    },
    {
      "confidence": 2.8773826570713514,
      "threshold": -1,
      "predictedValue": "Email (I/f)",
      "predictedSysId": ""
    }
  ]
}