GeniusResultContext - Scoped

  • Release version: Australia
  • Updated March 12, 2026
  • 7 minutes to read

    • The GeniusResultContext API provides methods for retrieving search query information from the context of a Genius Result configuration.

    You can use search query details retrieved with this API to populate Genius Result answer objects created with the GeniusResultAnswer API.

    Use this API in Genius Result server-side scripts with the sn_ais namespace identifier. For more information on scripting logic for Genius Results, see Create a new Genius Result configuration.

    GeniusResultContext - getMatchingDocuments()

    Retrieves search result documents from the search query that triggers your Genius Result configuration.

    Use this method in a Genius Result configuration's response processor script to retrieve field values from search result documents returned by the search query that triggers your Genius Result. You can populate Genius Result answers with fields from the search result documents using GeniusResultAnswer API methods.
    Note:
    This method returns no results when invoked in an AI Search request processor script, since the script is executed before AI Search computes results for the triggering search query. Don't use this method in your request processor scripts.
    Table 1. Parameters
    Name Type Description
    None
    Table 2. Returns
    Type Description
    [Array]

    Array of objects in which each object represents a search result document that matched the search query.

    Data type: Array

    [
  {
    ".score": Number,
    ".table": "String",
    ".text": "String",
    ".title": "String",
    ".url": "String",
    "class_name": "String",
    "sys_id": "String",
    "<field_name>": <field_value>
  }
]
    Note:
    Depending on the search query, some of the search result document's AI Search index fields may appear in the object in addition to the listed name-value pairs.
    [Array]..score

    Relevancy score computed for the search result document in the context of the search query.

    Data type: Number

    Note:
    The name for this name-value pair begins with a period to distinguish it from the AI Search index's score common field.
    [Array]..table

    Name of the ServiceNow AI Platform table that the search result document was indexed from.

    Data type: String

    Note:
    The name for this name-value pair begins with a period to distinguish it from the AI Search index's table common field.
    [Array]..text

    Indexed text from the search result document.

    Data type: String

    Note:
    The name for this name-value pair begins with a period to distinguish it from the AI Search index's text common field.
    [Array]..title

    Indexed title for the search result document.

    Data type: String

    Note:
    The name for this name-value pair begins with a period to distinguish it from the AI Search index's title common field.
    [Array]..url

    URL for the search result document.

    Data type: String

    Note:
    The name for this name-value pair begins with a period to distinguish it from the AI Search index's url common field.
    [Array].class_name

    The child class that the search result document belongs to. For more information on table child classes, see Table extension and classes.

    Data type: String
    [Array].sys_id

    Sys_id for the search result document.

    Data type: String
    [Array].<field_name>

    AI Search index field from the search result document.

    Data type: The data type of the AI Search index field.

    This AI Search response processor script retrieves up to three search result documents matched by the search query and checks whether any of them is from the Knowledge [kb_knowledge] table. If it finds a search result document from the Knowledge table, the function adds that document's fields to the GeniusResultAnswer object so the UI can display them to the user.

    function process(context) {
  var answer = new sn_ais.GeniusResultAnswer();
  var matchingDocuments = context.getMatchingDocuments();
  if (matchingDocuments.length > 1) {
    for (var i = 0; i < 3; i++) {
      var currentDocument = matchingDocuments[i];
      if (currentDocument['.table'] == 'kb_knowledge') {
        var gr = new GlideRecord("kb_knowledge");
        if (gr.get(currentDocument['sys_id'])) {
          answer.addDataMap({
            "number": currentDocument['number'],
            "url": currentDocument['.url'],
            "title": currentDocument['.title']
          });
        }
      }
      break;
    }
  }
  return answer;
}

    GeniusResultContext – getOriginalSearchPhrase()

    Retrieves the original search terms from the search query that triggers your Genius Result configuration.

    You can use retrieved search terms to populate Genius Result answers using GeniusResultAnswer API methods.

    Table 3. Parameters
    Name Type Description
    None
    Table 4. Returns
    Type Description
    String

    Original terms from the search query.

    Data type: String

    This AI Search request processor script gets the search query's original terms and uses them as the query terms for a new snippet search.

    function process(context) {
  var answer = new sn_ais.GeniusResultAnswer();

  var searchPhrase = context.getOriginalSearchPhrase();
  answer.setSearchPhrase(searchPhrase);
  answer.snippetSearch(true);

  return answer;
}

    GeniusResultContext – getPredictionResult()

    Retrieves NLU model prediction results for the search query that triggers your Genius Result configuration.

    Use this method in a Genius Result configuration's request or response processor script to retrieve the intent detected for the triggering search query. You can populate Genius Result answers with details from the detected intent using GeniusResultAnswer API methods.

    Table 5. Parameters
    Name Type Description
    None
    Table 6. Returns
    Type Description
    [Array]

    Array of objects in which each object represents an NLU model intent prediction result for the search query.

    Data type: Array

    [
  {
    "intentName": "String",
    "result": {Object},
    "solutionName": "String"
  }
]
    [Array].intentName

    Name of the NLU model intent detected in the search query.

    Data type: String
    [Array].result

    Object representing the NLU model intent prediction result for the search query.

    Data type: Object

    "result": {
  "application": {Object},
  "result": {Object},
  "solutionLabel": "String",
  "solutionName": "String",
  "solutionType": "String",
  "type": "String",
  "version": Number
}
    [Array].result.application

    Object containing metadata for the search application that produced the prediction result.

    Data type: Object

    "application": {
  "language": "String",
  "type": "String"
}
    [Array].result.application.language

    Language context for the ServiceNow AI Platform application that produced the prediction result.

    Data type: String
    [Array].result.application.type

    Type of the ServiceNow AI Platform application that produced the prediction result.

    Data type: String
    [Array].result.result

    Object including details for the NLU model intent prediction result.

    Data type: Object

    "result": {
  "entities": [Array],
  "intentName": "String",
  "intents": [Array],
  "nluModelName": "String",
  "score": Number
}
    [Array].result.result.entities

    Array of objects in which each object represents an NLU model intent entity detected in the search query.

    Data type: Array

    "entities": [
  {
    "matchingSegments": [Array],
    "name": "String",
    "parts": [Array],
    "score": Number,
    "startingPosition": Number,
    "value": "String"
  }
]
    [Array].result.result.entities.matchingSegments

    Array of objects in which each object represents a matching segment of the detected NLU entity.

    Data type: Array

    "matchingSegments": [
  {
    "directMatch": Boolean
    "meta": {Object},
    "value": "String"
  }
]
    [Array].result.result.entities.matchingSegments.directMatch

    Flag that indicates whether the NLU entity matching segment represents an exact match for the entity value.
    [Array].result.result.entities.matchingSegments.meta

    Object containing key-value pair fields representing metadata for the NLU entity matching segment.

    Data type: Object

    "meta": {
  "augmentedBy": "String",
  "colMeta:first_name": "String",
  "colMeta:last_name": "String",
  "compositionStrategy": "String",
  "fieldName": "String",
  "searchPreProcessors": "String",
  "SUB_PHRASE.penalty": "String",
  "tableName": "String"
}
    [Array].result.result.entities.matchingSegments.value

    String value of the NLU entity matching segment.

    Data type: String
    [Array].result.result.entities.name

    Name for the detected NLU entity, in entity:[NLU_model].[intent].[entity_type] format.

    Data type: String
    [Array].result.result.entities.parts

    Array of objects in which each object represents an NLU entity part found in the prediction result.

    Data type: Array

    "parts": [
  {
    "meta": {Object},
    "value": "String"
  }
]
    [Array].result.result.entities.parts.meta

    Object containing key-value pair fields representing metadata for the NLU entity part.

    Data type: Object

    "meta": {
  "augmentedBy": "String",
  "colMeta:name": "String",
  "compositionStrategy": "String",
  "fieldName": "String",
  "searchPreProcessors": "String",
  "SUB_PHRASE.penalty": "String",
  "tableName": "String"
}
    [Array].result.result.entities.parts.value

    String value of the NLU entity part.

    Data type: String
    [Array].result.result.entities.score

    Numeric score from 0 through 1 indicating confidence for the detected NLU entity.

    Data type: Number
    [Array].result.result.entities.startingPosition

    Numeric index for the character position in the search query at which the detected NLU entity begins.

    Data type: Number
    [Array].result.result.entities.value

    String value of the NLU detected in the search query.

    Data type: String
    [Array].result.result.intentName

    Name of the NLU model intent detected in the search query.

    Data type: String
    [Array].result.result.intents

    Array of objects in which each object represents a nested NLU model intent detected in the search query.

    Nested intent objects include the same parameter names and data types as the parent NLU model intent object.

    Data type: Array

    "intents": [
  {
    "entities": [Array],
    "intentName": "String",
    "intents": [Array],
    "nluModelName": "String",
    "score": Number
  }
]
    [Array].result.result.nluModelName

    Name of the NLU model that produced the prediction result.

    Data type: String
    [Array].result.result.score

    Numeric score from 0 through 1 indicating confidence for the NLU model prediction result.

    Data type: Number
    [Array].result.solutionLabel

    Label for the NLU model that produced the prediction result.

    Data type: String
    [Array].result.solutionName

    Name for the NLU model that produced the prediction result.

    Data type: String
    [Array].result.solutionType

    Type for the NLU model that produced the prediction result.

    Data type: String
    [Array].result.type

    Type of the prediction result.

    Data type: String
    [Array].result.version

    Version of the NLU model that produced the prediction result.

    Data type: Number
    [Array].solutionName

    Name of the NLU model that produced the prediction result.

    Data type: String

    This AI Search request processor script checks whether the search query includes an NLU model prediction result. When it finds a prediction result, it adds the matching segments from all detected catalogItem entities as search terms.

    function process(context) {
  var answer = new sn_ais.GeniusResultAnswer();

  answer.setTable('sc_cat_item');
  answer.setSearchLimit(2);
  var predictionResult = context.getPredictionResult();
  if (predictionResult && predictionResult.length == 1) {
    var detail = predictionResult[0];
    if (detail['result'] && detail['result']['entities']) {
      var entities = detail['result']['entities'];
      for (var i = 0; i < entities.length; i++) {
        if (entities[i]['name'].endsWith('catalogItem')) {
          var matchingSegments = entities[i]['matchingSegments'];
          for (var j = 0; j < matchingSegments.length; j++)
            answer.addSearchPhrases([matchingSegments[j]['value']]);
        }
      }
    }
  }

  return answer;
}