Encoder - Global

concept brazil Updated Sep 10, 2026 9 min read EncoderAPI.md
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The Encoder API provides a scriptable object used in Predictive Intelligence stores. This object converts input data into vectors of numbers, based on encoder-specific goals and configurations. Encoders can be used independently to run encodings or can be configured as part of solutions to encode text columns.

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

Encoders are text processing objects that are either pre-trained or trained based on the language datasets you provide. You can train encoders that determine how the system interprets and processes text fields. For ML solutions that include text, you can train an encoder to specify how to process text and use the trained encoder in a solution.

Encoders have configuration and versions, and can be trained independently with their own retraining frequency. API-defined encoders are different from UI-defined encoders, because the retraining of UI-defined encoders is controlled by the solutions using them.

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

  1. Create one or more datasets using the DatasetDefinition API.
  2. Use the constructor to create an encoder object.
  3. Add the encoder object to the encoder store using the EncoderStore - add() method.
  4. Train the encoder using the submitTrainingJob() method. This creates a version of the object that you can manage using the EncoderVersion API.

Once you have trained an encoder, you can use it in a solution object:

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

Encoder - Encoder(Object config)

Creates an encoder.

To get an encoder for one or more datasets, use this constructor to create a new encoder object with a unique name.

Name

Type

Description

config

Object

JavaScript object containing configuration properties of theencoder.``` { "algorithmConfig": {Object}, "datasets": [Array], "domainName": "String", "label": "String", "minRowCount": "String", "processingLanguage": "String", "stopwords": [Array], "trainingFrequency": "String" }

Text
</td></tr><tr><td>
config.algorithmConfig
</td><td>
Object
</td><td>
Optional. JavaScript object containing algorithm configuration properties. ```'algorithmConfig' : {  "algorithm": "String"}

config.algorithmConfig.algorithm

String

Name of the algorithm for training this encoder. Possible values:

  • paravec: Paragraph vector word embedding.
  • tf-idf: Term Frequency–Inverse Document Frequency (TF-IDF)-based text.

config.datasets

Array

List of DatasetDefinition object names.

config.domainName

String

Optional. Domain name associated with this dataset. Default: Current domain, for example, "global".

config.label

String

Identifies the prediction task.

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.trainingFrequency

String

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
The following example shows how to create an encoder job and add it to the encoder store.
JavaScript
var myPrbData = new sn_ml.DatasetDefinition({    'tableName' : 'problem',    'fieldNames' : ['short_description'],    'encodedQuery' : 'activeANYTHING'});
var myIncidentData = new sn_ml.DatasetDefinition({    'tableName' : 'incident',    'fieldNames' : ['short_description', 'description'],    'encodedQuery' : 'activeANYTHING'});
var myEncoder = new sn_ml.Encoder({    'label': "encoder",    'datasets' : [myPrbData, myIncidentData],    'algorithmConfig' : {        'algorithm' : 'tf-idf'    }});var myEncoderName = sn_ml.EncoderStore.add(myEncoder);

Encoder - cancelTrainingJob()

Cancels a job for a encoder 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.

JavaScript
var myEncoder = sn_ml.EncoderStore.get('ml_sn_global_global_encoder');
myEncoder.cancelTrainingJob();

Encoder - getActiveVersion()

Gets the active EncoderVersion object.

Name Type Description
None
Type Description
Object Active EncoderVersion object.

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

JavaScript
var mlEncoder = sn_ml.EncoderStore.get('ml_x_snc_global_global_encoder');
gs.print(JSON.stringify(JSON.parse(mlEncoder.getActiveVersion().getStatus()), null, 2));

Output:

JSON
{  "state": "encoder_complete",  "percentComplete": "100",  "hasJobEnded": "true"}

Encoder - getAllVersions()

Gets all versions of an encoder.

Name Type Description
None
Type Description
Array Existing versions of an encoder object. See also EncoderVersion API.

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

JavaScript
var mlEncoder = sn_ml.EncoderStore.get('ml_x_snc_global_global_encoder');
var mlEncoderVersions = mlEncoder.getAllVersions();
for (i = 0; i < mlEncoderVersions.length; i++) {gs.print("Version " + mlEncoderVersions[i].getVersionNumber() + " Status: " + mlEncoderVersions[i].getStatus() +"\n");};

Output:

CSS
Version 3 Status: {"state":"encoder_complete","percentComplete":"100","hasJobEnded":"true"}
Version 2 Status: {"state":"encoder_complete","percentComplete":"100","hasJobEnded":"true"}
Version 1 Status: {"state":"encoder_cancelled","percentComplete":"0","hasJobEnded":"true"}

Encoder - getLatestVersion()

Gets the latest version of an encoder.

Name Type Description
None
Type Description
Object EncoderVersion object corresponding to the latest version of an Encoder().

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

JavaScript
var mlEncoder = sn_ml.EncoderStore.get('ml_x_snc_global_global_encoder');
gs.print(JSON.stringify(JSON.parse(mlEncoder.getLatestVersion().getStatus()), null, 2));

Output:

JSON
{  "state": "encoder_complete",  "percentComplete": "100",  "hasJobEnded": "true"}

Encoder - getName()

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

Name Type Description
None
Type Description
String Name of the encoder object.

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

JavaScript
// Update encodervar myIncidentData = new sn_ml.DatasetDefinition({   'tableName' : 'incident',   'fieldNames' : ['category', 'short_description', 'priority'],   'encodedQuery' : 'activeANYTHING'});
var eligibleFields = JSON.parse(myIncidentData.getEligibleFields(encoder));
var myEncoder = new sn_ml.Encoder({   'label': "my encoder",   'datasets' : [myIncidentData],   'inputFieldNames': eligibleFields['eligibleInputFieldNames'],   'predictedFieldName': 'category'});
// update encodersn_ml.EncoderStore.update('ml_x_snc_global_global_my_definition_4', myEncoder);
// print encoder namegs.print('Encoder Name: '+myEncoder.getName());

Output:

Text
Encoder Name: ml_x_snc_global_global_my_definition_4

Encoder - getProperties()

Gets solution object properties.

Name Type Description
None

Type

Description

Object

Contents of the Dataset and Encoder() object details in the EncoderStore.``` { "algorithmConfig" : {Object}, "datasetsProperties": [Array], "domainName": "String", "label": "String", "name": "String", "processingLanguage": "String", "scope": "String", "stopwords": [Array], "trainingFrequency": "String" }

Text
</td></tr><tr><td>
&lt;Object&gt;.algorithmConfig.algorithm
</td><td>
Name of the algorithm for training this encoder. Possible values:
-   **paravec**: Paragraph vector word embedding.-   **tf-idf**: Term Frequency–Inverse Document Frequency \(TF-IDF\)-based text.
 Data type: String.
</td></tr><tr><td>
&lt;Object&gt;.algorithmConfig
</td><td>
Optional. JavaScript object containing algorithm configuration properties. ```'algorithmConfig' : {  "algorithm": "String"}

Data type: Object.

<Object>.datasetsProperties

List of DatasetDefinition() properties associated with the encoder.

JavaScript
{ "encodedQuery": "String", "fieldDetails": [Array], "fieldNames": [Array], "tableName": "String"}

Data type: Array.

<Object>.datasetsProperties.tableName

Name of the table for the dataset. For example, "tableName" : "Incident". Data type: String.

<Object>.datasetsProperties.fieldNames

List of field names from the specified table as strings. For example, "fieldNames" : ["short_description", "priority"]. Data type: Array.

<Object>.datasetsProperties.fieldNames.fieldDetails

List of JavaScript objects that specify field properties. ``` [ { "name": "String", "type": "String" } ]

Text
 Data type: Array.
</td></tr><tr><td>
&lt;Object&gt;.datasetsProperties.fieldNames.fieldDetails.&lt;object&gt;.name
</td><td>
Name of the field defining the type of information to restrict this dataset to. Data type: String.
</td></tr><tr><td>
&lt;Object&gt;.datasetsProperties.fieldDetails.&lt;object&gt;.type
</td><td>
Machine-learning field type. Data type: String.
</td></tr><tr><td>
&lt;Object&gt;.datasetsProperties.fieldDetails.encodedQuery
</td><td>
Encoded query string in the standard platform format. See [Encoded query strings](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/brazil/markdown/platform-user-interface/c_EncodedQueryStrings.md).Data type: String.
</td></tr><tr><td>
&lt;Object&gt;.domainName
</td><td>
Domain name associated with this dataset. See [Domain separation and Predictive Intelligence](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/brazil/markdown/intelligent-experiences/domain-separation-predictive-intelligence.md).Type: String
</td></tr><tr><td>
&lt;Object&gt;.label
</td><td>
Identifies the prediction task. ```{  "label": "my first prediction"}

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>.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.
The following example gets properties of an encoder object in the store.
JavaScript
var myEncoder = sn_ml.EncoderStore.get('ml_sn_global_global_encoder');
gs.print(JSON.stringify(JSON.parse(myEncoder.getProperties()), null, 2));

Output:

CSS
*** Script: {  "datasetsProperties": [    {      "tableName": "incident",      "fieldNames": [        "assignment_group",        "short_description",        "description"      ],      "encodedQuery": "activeANYTHING"    }  ],  "domainName": "global",  "label": "my encoder definition",  "name": "ml_x_snc_global_global_my_encoder_definition",  "processingLanguage": "en",  "scope": "global",  "stopwords": [    "Default English Stopwords"  ],  "trainingFrequency": "run_once"}

Encoder - getVersion(String version)

Gets an encoder by provided version number.

Name Type Description
version String Existing version number of an encoder.
Type Description
Object Specified version of the Encoder() object on which you can call EncoderVersion API methods.

The following example shows how to get the training status of an encoder by version number.

JavaScript
var mlEncoder = sn_ml.EncoderStore.get('ml_x_snc_global_global_encoder');
gs.print(JSON.stringify(JSON.parse(mlEncoder.getVersion('1').getStatus()), null, 2));

Output:

JSON
{  "state": "encoder_complete",  "percentComplete": "100",  "hasJobEnded": "true"}

Encoder - setActiveVersion(String version)

Activates a specified version of an encoder in the store.

Name

Type

Description

version

String

Name of the Encoder() object version to activate.Activating this version deactivates any other version.
|Type|Description| |----|-----------| |None| |

The following example shows how to activate an encoder version in the store.

JavaScript
sn_ml.Encoder.setActiveVersion("ml_incident_categorization");

Encoder - submitTrainingJob()

Submits a training job.

Note: Before running this method, you must first add an encoder to the store using the EncoderStore - add() method.

Name Type Description
None
Type Description
Object EncoderVersion object corresponding to the Encoder being trained.

The following example shows how to create a dataset, apply it to an encoder, add it to a store, and submit the training job.

JavaScript
// Create a dataset var myData = new sn_ml.DatasetDefinition({
  'tableName' : 'incident',  'fieldNames' : ['assignment_group', 'short_description', 'description'],  'encodedQuery' : 'activeANYTHING'
});
// Create an encoder var myEncoder = new sn_ml.Encoder({
  'label': "my encoder definition",  'datasets' : [myData],  'predictedFieldName' : 'assignment_group',  'inputFieldNames':['short_description']
});
// Add the encoder to the store to later be able to retrieve it.var my_unique_name = sn_ml.EncoderStore.add(myEncoder);
// Train the encoder - this is a long running job var myEncoderVersion = myEncoder.submitTrainingJob();
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