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Script include - AILensActionService

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Script include - AILensActionService

Use the AILensActionService script include together with Lens actions to leverage ServiceNow AI Lens as a service for extracting information from the provided images and getting answers to your questions.

This script include is part of the ServiceNow AI Lens (sn_ai_lens) store application and is located within the sn_app_lens_core scope.

This script include provides methods that enable the following:

Calls Lens as a back-end service
Analyzes and comprehends data from provided images
Gets response from AI as per provided directions
Does not require ServiceNow AI Lens desktop app

Parent Topic:ServiceNow AI Lens reference

AILensActionService - AILensActionService()

Creates an AILensActionService instance.

|Name|Type|Description| |—-|—-|———–| |None| ||

The following example shows how to initialize AILensActionService.

var lensService = new sn_app_lens_core. AILensActionService()

AILensActionService - invokeLens(String lensActionId, String[] attachmentIds, String userPrompt, Object[] imageArr, Object inputJSON, Object[] additionalContext, Boolean skipACL)

Invokes ServiceNow AI Lens as a service.

Name

</th><th>

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

lensActionId

</td><td>

String

</td><td>

Sys_id of the Lens Actions record created for your use case, or you can select the out-of-the-box option that fits your requirements.Example: 842bfc8e37066210b97528c734924baf

This parameter is mandatory.

</td></tr><tr><td>

attachmentIds

</td><td>

String[]

</td><td>

Array of sys_ids for existing image attachments.Example: ["0067e66f93f022108319f9ed1dba108b", "0000e8a42c9a7110f877137af4eab4b5"]

You must pass either attachmentIds or imageArr parameter.

</td></tr><tr><td>

userPrompt

</td><td>

String

</td><td>

An instruction or question for AI to answer after analyzing the contents of the attachments.Example: Analyze this production issue and create an incident ticket

</td></tr><tr><td>

imageArr

</td><td>

Object[]

</td><td>

Array of objects containing name of the screenshot and base64 encoded image data.Example:

[
    {
        name: "screenshot1.png",
        data: "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
    },
    {
        name: "screenshot2.png",
        data: "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9Qz0AEYAJMgkU1f5kAAAAASUVORK5CYII="
    }
];

You must pass either attachmentIds or imageArr parameter.

</td></tr><tr><td>

inputJSON

</td><td>

Object

</td><td>

Additional JSON input parameters that you want to pass in the pre-processing script of the Lens action.Note: To use Multi-table schema mapping, you must pass at least one table name key and its corresponding value inside the input JSON. For example, { ‘tableName’: ‘sn_ent_model,sn_ent_asset’ },

Example:

{
      "type" : "object",
      "properties" : {
        "short_description" : {
          "type" : "string",
          "label" : "Short description"
        },
        "description" : {
          "type" : "string",
          "label" : "Description"
        },
      },
      "required" : [ "short_description", "comments" ],
   }

</td></tr><tr><td>

additionalContext

</td><td>

Object

</td><td>

An optional parameter that you can use to pass any extra key–value information while you’re performing a Lens service call. The examples show the key–value information that you can pass.- Example:

```

{IsFileUploadEnabled: true} “‘

Example: As part of the Excel mapping services, you can pass key-value information to:
- auto-map the column headers of an Excel file with ServiceNow table fields
- auto-map Excel values to ServiceNow drop-down values
- auto-map Excel values to referenced records in ServiceNow tables For more information on the various key-values of the Excel mapping service, see Key-values of the Excel auto-mapping services

Key-values of the Excel auto-mapping services

-   **Schema mapping** — Maps Excel column headers to ServiceNow table fields and identifies the field type of each mapped field. You can also use this option to map Excel column headers from a single sheet to multiple ServiceNow tables. Key-values for schema mapping are:

    -   `mappingStrategy`: Set to `'schema_mapping'`
    -   `rowContextSize`: Number of sample data rows to use for mapping context. Default: `10`.
    -   `randomizeRowOrder`: If `true`, Lens selects rows randomly from the Excel file. If `false`, Lens uses rows in order from the top.
    -   `sheetName`: Name of the Excel Sheet. If no name is specified, the first sheet is chosen by default.
    -   `headerRowNumber`: The row number from which the header with column names start with.
    ```

Request example: var response = new sn_app_lens_core.AILensActionService().invokeLens(
‘b7fbd8694730c310dbc4964a516d431e’,
[‘0c0b1c6d4730c310dbc4964a516d43f2’],
”, [], {},
true, {“mappingStrategy”:“schema_mapping”, sheetName: “Incident Data”,headerRowNumber: 2,“rowContextSize”:5,“randomizeRowOrder”:false

} );
gs.info(“Lens Response : ” + JSON.stringify(response));

Response example: { “Owner”: { “type”: “string”, “mappings”: { “0.8”: [“assigned_to”, “parent”, “epic”] } }, “Age”: { “type”: “integer”, “mappings”: { “0.8”: [“story_points”] } }, “Status”: { “type”: “choice”, “mappings”: { “0.8”: [“state”, “priority”] }, “choiceValues”: [“Retired”, “On Boarding”, “Working”] }, “Start Date”: { “type”: “glide_date”, “mappings”: { “1.0”: [“expected_start”], “0.8”: [“due_date”] } }, “End Date”: { “type”: “glide_date”, “mappings”: { “1.0”: [“due_date”], “0.8”: [“expected_start”] } }, “Project Assigned”:{ “type”: “string”, “mappings”: { “0.8”: [“short_description”, “task”, “description”] } } } “‘

Multi-table schema mapping

Maps Excel column headers from a single sheet to multiple ServiceNow tables simultaneously. ServiceNow AI Lens identifies the relevant fields in each table for each Excel column header and maps the data accordingly.

Request example

```

    ```

-   **Choice mapping** — For fields identified as Choice type, maps the unique values from the Excel column to the valid choice values in the ServiceNow table field. Key-values for Choice mapping are:

    -   `mappingStrategy`: Set to `'choice_mapping'`.
    -   `columnConfigs`: An array of objects that specifies the ServiceNow choice field and the corresponding Excel column to map, including the field name, column header, and optionally the cell values to use instead of reading from the file.
    ```

Request example: { mappingStrategy: ‘choice_mapping’,

SheetName:’Incident Data’, 

SheetNameheaderRowNumber:2, 
columnConfigs: [ 
    { field: 'priority', name: 'Priority' }, 
    { field: 'state',    name: 'Ticket Status' } 
]

} Response example: [ { “name”: “Priority”, “field”: “priority”, “mappings”: [ { “key”: “High”, “value”: “1”, “label”: “1 - Critical”, “score”: 1.0 }, { “key”: “Medium”, “value”: “3”, “label”: “3 - Moderate”, “score”: 0.9 } ] } ] “‘

Multi-column to single field choice mapping

Maps multiple Excel column headers and their values to a single ServiceNow field. This is useful when related data from separate columns should be consolidated into one field.

Request example

```

var response = new sn_app_lens_core.AILensActionService().invokeLens( “4b50a72c2b694710c18df2086e91bf84”, [“e782e364936103105357f1cfb903d682”], “”, {}, {}, true, { “mappingStrategy”: “choice_mapping”, “columnConfigs”: [ { “field”: “location”, “name”: “city” }, { “field”: “location”, “name”: “zip” }, { “field”: “location”, “name”: “state” } ] } ); gs.info(“response ” + JSON.stringify(response, null, ‘ ’));

```

Note: Multi-column to single field mapping also works with Reference mapping.

-   **Reference mapping** — Maps Excel cell values to the corresponding ServiceNow reference field records. Key-values for Reference mapping are:

    -   `mappingStrategy`: Set to `'reference_mapping'`.
    -   `columnConfigs`: Configuration for the specific column being mapped, including the source column name and the target reference table details.
    ```

SheetName:’Incident Data’,

SheetNameheaderRowNumber:2,

columnConfigs: [{ field: ‘assignment_group’, name: ‘Assignment Group’, }] } );

Response example: [ { “name”: “Assignment Group”, “field”: “assignment_group”, “mappings”: [ { “key”: “IT Help Desk”, “value”: “abc123…”, “label”: “IT Help Desk”, “score”: 1.0 } ] } ] “‘

</td></tr></tbody> </table><table id=“table_w4d_qzt_lgc” class=“returns”><thead><tr><th>

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

</td><td>

Returned success object { "status": "success", "lensResponse": "{\"short_description\":\"Service Degradation Error in Order Processing System\",\"description\":\"The Order Processing API v2.1 encountered a service degradation issue in the Production environment.\" }" }

</td></tr><tr><td>

error

</td><td>

Returned error object { "status": "error", "error": { "errorType": "Execution Error", "message": "Detailed error message here" } }

</td></tr></tbody> </table>This example shows how to call Lens service from a script block.

var lensActionId = "cd6570cdf36a2210b9751f09f6968c42";
var attachmentIds = ["3fe930093b626210aba1fadc73e45a38", "0000e8a42c9a7110f877137af4eab4b5"];
var userPrompt = "Analyze this production issue and create an incident ticket";
var imageArr = [
    {
        name: "screenshot1.png",
        data: "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
    },
    {
        name: "screenshot2.png",
        data: "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9Qz0AEYAJMgkU1f5kAAAAASUVORK5CYII="
    }
];
var inputJSON = {
      "type" : "object",
      "properties" : {
        "short_description" : {
          "type" : "string",
          "label" : "Short description"
        },
        "description" : {
          "type" : "string",
          "label" : "Description"
        },
      },
      "required" : [ "short_description", "comments" ],
   }
var additionalContext = {
      IsFileUploadEnabled: true};

 // Call the method
var result = new sn_app_lens_core. AILensActionService().invokeLens(lensActionId, attachmentIds, userPrompt, imageArr, inputJSON, skipACL, additionalContext);
 
// Handle the response
if (result.status === 'success') {
    var response = JSON.parse(result.lensResponse);
    gs.info("AI Lens Analysis Complete:");
    gs.info("Title:", response.short_description);
    gs.info("Description:", response.description);
} else {
    gs.error("Error occurred:", result.error.message);
}

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