AI Assets API · SN Doc Reader

AI Assets API provides endpoints to retrieve, update, and create several types of AI assets, such as systems, data sets, prompts, and models.

The AI Assets API supports actions that you can perform on AI Asset records in the Expanded Model and Asset Classes application. It requires the Asset Classes (sn_ent) plugin to access it. You must have the asset and model_manager roles to call the endpoints provided by the AI Assets API.

Note: The data included in AI Asset records may be spread across several tables like Product Model, Configuration Item, and others. To reduce complexity when calling this API, however, the AI Assets API inserts data into only the Asset, Product Model and Configuration Item tables. Specifics about table updates are provided under each endpoint.

Parent Topic:REST API reference

AI Assets API - GET /sn_ent/asset/ai_dataset/{sys_id}

Retrieves the data of the specified AI dataset asset.

URL format

Versioned URL: /api/sn_ent/{api_version}/asset/ai_dataset/{sys_id}

Default URL: /api/sn_ent/asset/ai_dataset/{sys_id}

Supported request parameters

Name

Description

api_version

Optional. Version of the endpoint to access. For example, v1 or v2. Only specify this value to use an endpoint version other than the latest. Data type: String

sys_id

Sys_id of the asset.Table: AI Dataset Asset [alm_ai_dataset_digital_asset]

Data type: String

|Name|Description| |----|-----------| |None| |

Name	Description
None

Headers

The following request and response headers apply to this HTTP action only, or apply to this action in a distinct way. For a list of general headers used in the REST API, see Supported REST API headers.

Header	Description
Accept	Data format of the response body. Only supports application/json.
Content-Type	Data format of the request body. Only supports application/json.

Header	Description
None

Status codes

The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see REST API HTTP response codes.

Status code	Description
200	Successful. The request was successfully processed.
401	Unauthorized. The user credentials are incorrect or have not been passed.
404	Not found. Failed to fetch the asset with the given sys_id.
500	Internal server error. An unexpected error occurred while processing the request. The response contains additional information about the error.

Response body parameters

Name	Description
result	Details of the newly created AI Dataset asset.Data type: Object JavaScript `"result": { "acceptable_usage": {Object}, "base_datasets": [Array], "created": "String", "dataset_card": "String", "data_type": {Object}, "description": "String", "documentation": "String", "display_name": "String", "managed_by": {Object}, "name": "String", "provider": {Object}, "state": "Development", "source": "String", "sys_id": "String", "updated": "String", "version": "String",}`
result.acceptable_usage	Acceptable usage for the AI Dataset Asset record. Acceptable usage refers to how a dataset or model can be used, typically for training or evaluation purposes.Data type: Object CSS `"acceptable_usage": { "label": "String" "value": "String" }`
result.acceptable_usage.label	Display label of the acceptable usage value.Data type: String
result.acceptable_usage.value	The purpose of the dataset or model, like for training or evaluation.Valid values: `1`: indicates training purpose `2`: indicates an evaluative purpose Data type: String (number)
result.base_datasets	Comma-separated list of base datasets required to build the given dataset. Accepts the name or sys_id of a base data set in the AI Dataset Digital Asset [alm_ai_dataset_digital_asset] table.Data type: Array Text `"base_datasets": ["String", "String"]`
result.created	Date and time that the AI Dataset Asset record was created.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.data_type	The type of data present in the AI Dataset Asset record.Data type: Object CSS `"data_type": { "label": "String", "value": "String"}`
result.data_type.label	The display label for the data type value.Data type: String
result.data_type.value	Value of the dataset asset's data type.Data type: String
result.dataset_card	The data set card. A dataset_card is a metadata document that describes the contents, structure, and context of an AI dataset. It provides details like data sources, features, intended use, and any known limitations to ensure proper understanding and usage.Data type: String
result.description	Description of the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.display_name	Display name of the AI Dataset Asset record.Table: AI Dataset Digital Asset [alm_ai_dataset_digital_asset] Data type: String
result.documentation	Documentation of the associated AI Dataset Product Model [cmdb_ai_dataset_product_model] table record.Data type: String
result.managed_by	Details about the user who manages the AI Dataset Asset record.Data type: Object Text `"managed_by": [ { "name": "String", "sys_id": "String" }]`
result.managed_by.name	Name of the user who manages the AI Dataset Asset record.Table: User [user] Data type: String
result.managed_by.sys_id	Sys_id of the user who manages the AI Dataset Asset record.Table: User [user] Data type: String
result.name	Name of the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.provider	Provider of the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: Object Text `provider: { "name": "String", "sys_id": "String"}`
result.provider.name	Name of the provider.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.provider.sys_id	Sys_id of the provider in the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.source	Details about the source of AI dataset asset.Valid values: Link to the source of the dataset asset. Details (in plain text) of the source of the dataset asset. For example, the name of a product or website. Data type: String Default: empty or null
result.state	State of the AI Dataset Asset record.Possible values: In use Deployed Retired Development Unknown N/A Data type: String
result.sys_id	Sys_id of the AI Dataset Asset record.Table: AI Dataset Digital Asset [alm_ai_dataset_digital_asset] Data type: String
result.updated	Date and time that the AI Dataset Asset record was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.version	Version number of the associated AI Dataset Product Model record. For example, `V2`.Data type: String

### cURL request

The following example.

Shell

curl "https://instance.servicenow.com/api/sn_ent/asset/ai_dataset/ cc419cb2331e92101c9aca989d5c7b4c" \ --request GET \ --header "Accept:application/json" \ --user "username":"password"

Output:

JSON

{   "result": {     "sys_id": "9d60fb5f40d21210f877b00c113d1fea",     "display_name": "ServiceNow Closed Incidents v1",     "name": "Closed Incidents",     "description": "Incidents with resolution",     "documentation": "Sample Documentation",     "source": "incident table on servicenow instance",     "dataset_card": "Sample Dataset Card",     "state": "Development",     "version": "v1",     "data_type": {       "value": "2",       "label": "Image"     },     "provider": {       "sys_id": "93d4ecfac0a8000b6294d71b733977fb",       "name": "ServiceNow"     },     "managed_by": {       "sys_id": "62826bf03710200044e0bfc8bcbe5df1",       "name": "Abel Tuter"     },     "acceptable_usage": {       "value": "1",       "label": "Training"     },     "base_datasets": [],     "created": "2024-12-11 08:50:40",     "updated": "2024-12-11 08:50:40"   } }

AI Assets API - GET /sn_ent/asset/ai_model/{sys_id}

Retrieves the data of the specified AI model asset.

URL format

Versioned URL: /api/sn_ent/{api_version}/asset/ai_model/{sys_id}

Default URL: /api/sn_ent/asset/ai_model/{sys_id}

Supported request parameters

Name

Description

api_version

Optional. Version of the endpoint to access. For example, v1 or v2. Only specify this value to use an endpoint version other than the latest. Data type: String

sys_id

Sys_id of the asset.Table: AI Model Asset [alm_ai_model_digital_asset]

Data type: String

|Name|Description| |----|-----------| |None| |

Name	Description
None

Headers

Header	Description
Accept	Data format of the response body. Only supports application/json.
Content-Type	Data format of the request body. Only supports application/json.

Header	Description
None

Status codes

The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see REST API HTTP response codes.

Status code	Description
200	Successful. The request was successfully processed.
401	Unauthorized. The user credentials are incorrect or have not been passed.
404	Not found. Failed to fetch the asset with the given sys_id.
500	Internal server error. An unexpected error occurred while processing the request. The response contains additional information about the error.

Response body parameters


result	Details of the model asset record.Data type: Object JavaScript result: { "base_model": {Object}, "context_window": "String", "created": "String", "deployment_guideline": "String", "description": "String", "display_name": "String", "documentation": "String", "evaluation_datasets": [Array], "evaluation_metrics_report": "String", "managed_by": {Object}, "model_size_in_mb": "String", "name": "String", "parameters_info": "String", "provider": {Object}, "required_infrastructure": "String", "source": "String", "state": Number, "supported_languages": [Array], "sys_id": "String", "training_datasets": [Array], "training_procedure": "String", "updated": "String", "version": "String"}
result.base_model	AI model that this model version was derived from.Note: Only applicable for models developed within the organization. Data type: Object JSON `{ "name": "String", "sys_id": "String" }` Default:
result.base_model.name	Name of the AI model asset to model this AI model after.Data type: String
result.base_model.sys_id	Sys_id of the AI model asset to model this AI model after.Table: AI Model Asset [alm_ai_model_digital_asset] Data type: String
result.context_window	Size of input sequences (in other words, the number of tokens) that the model can handle.Data type: String represented with a number
result.created	Date and time that the AI Dataset Asset record was created.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.deployment_guideline	Instructions applicable for models developed and deployed within an organization.Data type: String
result.description	Description to give the AI Model Product Model. Table: AI Model Product Model [cmdb_ai_model_product_model] Data type: String
result.documentation	Documentation of the AI Prompt Product Model record.Table: AI Prompt Product Model [cmdb_ai_model_product_model] Data type: String
result.evaluation_datasets	Comma-separated list of sys_ids or display names of AI datasets of the AI Dataset Digital Asset used for evaluating the model. Mostly applicable for models developed within an organization. Data type: Array Text `"evaluation_datasets": [ "name": "String", "sys_id": "String"]`
result.evaluation_metrics_report	Reference to the evaluation results.Possible values: Details (in plain text) outlining results Links to specific results Data type: String
result.managed_by	Details about the user who manages the AI Model Asset record.Data type: Object Text `"managed_by": [ { "name": "String", "sys_id": "String" }]`
result.managed_by.name	Name of the user who manages the AI Dataset Asset record.Table: User [user] Data type: String
result.managed_by.sys_id	Sys_id of the user who manages the AI Dataset Asset record.Table: User [user] Data type: String
result.model_size_in_mb	Size of the model in MB. Mostly applicable for models developed and deployed within an organization.Data type: Number
result.name	Required. Name of the AI Model Product Model. Table: AI Model Product Model [cmdb_ai_model_product_model] Data type: String
result.parameters_info	Properties of the training data that learn during the learning process. For example: `7B` or `30B`.Data type: String
result.provider	Provider of the associated AI Dataset Product Model record.Data type: Object Text `provider: { "name": "String", "sys_id": "String"}`
result.provider.name	Name of the provider.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.provider.sys_id	Sys_id of the provider in the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.required_infrastructure	Documentation of infrastructure needs for the model deployment. For example, details about the infrastructure stack and processing needs. Mostly applicable for models deployed within an organization.Data type: String
result.result.display_name	Display name of the asset record.Data type: String
result.source	Details about the source of the model.Possible values: Link to the source of the model. For example, `https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1` or a link to Github. Details of the source in plain text. For example, `Microsoft Azure` Data type: String
result.state	State to apply to the AI Model Asset.Possible values: In use Deployed Retired Development Unknown N/A Data type: String
result.supported_languages	Languages that are supported by the AI Model.Data type: Array Text `"supported_languages": [ { "name": "String", "sys_id": "String" }]`
result.supported_languages.name	Name of the Language record. For example, `"French", "English"`.Table: Language [sys_language] Data type: String
result.supported_languages.sys_id	Sys_id of the Language record.Table: Language [sys_language] Data type: String
result.sys_id	Sys_id of the AI model asset record.Table: AI Model Asset [alm_ai_model_digital_asset] Data type: String
result.training_datasets	Reference to 1+ associated data sets used for training the model.Data type: Array Text `"training_datasets": [ { "name": "String", "sys_id": "String" } ]`
result.training_procedure	Type of AI training applied to the model.Possible values: 1: Decision Trees 2: Deep Neural Networks 3: Linear Regression 4: Logistic Regression 5: Random Forest 6: Supervised Learning 7: Unsupervised Learning 8: Reinforcement Learning 9: Transfer Learning 10: Semi-Supervised Learning 11: Instruction Finetuning 12: Supervised Finetuning Data type: String
result.updated	Date and time that the AI model asset was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.version	Version for AI Model Product Model. For example, `V2`.Data type: String

### cURL request

The following example.

Shell

curl "https://instance.servicenow.com/api/sn_ent/asset/ai_model/cc419cb2331e92101c9aca989d5c7b4c" \--request GET \--header "Accept:application/json" \--user "username":"password"

Output:

JSON

{  "result": {    "sys_id": "a438d170ff96da10c1fbffffffffffd5",    "display_name": "ServiceNow Now LLM V6",    "name": "Now LLM",    "description": "enables text-to-text like question answering and summarization",    "version": "V6",    "provider": {      "sys_id": "93d4ecfac0a8000b6294d71b733977fb",      "name": "ServiceNow"    },    "documentation": "Now LLM V5 Documentation",    "parameters_info": "7B",    "supported_languages": [      {        "sys_id": "914493a30f320010e96b0e4fef767e90",        "name": "English"      }    ],    "model_size_in_mb": "87",    "deployment_guideline": "Deployed on ServiceNow infrastructure",    "source": null,    "training_procedure": "2",    "context_window": "8000",    "state": "Deployed",    "required_infrastructure": "undefined",    "base_model": {      "sys_id": null,      "name": ""    },    "evaluation_datasets": [      {        "sys_id": "45cb45baff06d610c1fbffffffffffa9",        "name": "ServiceNow Open Incidents"      }    ],    "training_datasets": [      {        "sys_id": "45cb45baff06d610c1fbffffffffffa9",        "name": "ServiceNow Open Incidents"      }    ],    "evaluation_metrics_report": "Testing results: See files attached to this record",    "managed_by": {      "sys_id": "62826bf03710200044e0bfc8bcbe5df1",      "name": "Abel Tuter"    },    "created": "2024-12-03 16:50:53",    "updated": "2024-12-03 16:50:53"  }}

AI Assets API - GET /sn_ent/asset/ai_prompt/{sys_id}

Retrieves the data of the specified AI prompt asset.

URL format

Versioned URL: /api/sn_ent/{api_version}/asset/ai_prompt/{sys_id}

Default URL: /api/sn_ent/asset/ai_prompt/{sys_id}

Supported request parameters

Name

Description

api_version

Optional. Version of the endpoint to access. For example, v1 or v2. Only specify this value to use an endpoint version other than the latest. Data type: String

sys_id

Sys_id of the asset.Table: AI Prompt Asset [alm_ai_prompt_digital_asset]

Data type: String

|Name|Description| |----|-----------| |None| |

Name	Description
None

Headers

Header	Description
Accept	Data format of the response body. Only supports application/json.
Content-Type	Data format of the request body. Only supports application/json.

Header	Description
None

Status codes

The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see REST API HTTP response codes.

Status code	Description
200	Successful. The request was successfully processed.
401	Unauthorized. The user credentials are incorrect or have not been passed.
404	Not found. Failed to fetch the asset with the given sys_id.
500	Internal server error. An unexpected error occurred while processing the request. The response contains additional information about the error.

Response body parameters

Name	Description
result	Details about the AI Prompt Asset.``` result: { "ai_model": {Object}, "created": "String", "description": "String", "display_name": "String", "documentation": "String", "managed_by": {Object}, "name": "String", "prompt_info": "String", "provider": {Object}, "state": Number, "sys_id": "String", "updated": "String", "version": "String" } Text `</td></tr><tr><td> result.ai\_model </td><td> AI Model of the AI Prompt Asset record.Data type: Object` "ai_model": { "name": "String", "sys_id": "String" } Text </td></tr><tr><td> result.ai\_model.name </td><td> Name of the AI model.Data type: String </td></tr><tr><td> result.ai\_model.sys\_id </td><td> Sys\_id of the AI Prompt Product Model record.Table: AI Prompt Product Model \[alm\_ai\_model\_digital\_asset\] Data type: String </td></tr><tr><td> result.created </td><td> Date and time that the AI Prompt Asset record was created.Format: YYYY-MM-DD HH:mm:ss Data type: String </td></tr><tr><td> result.description </td><td> Description of the AI Prompt Product Model record.Table: AI Prompt Product Model \[cmdb\_ai\_prompt\_product\_model\] Data type: String </td></tr><tr><td> result.display\_name </td><td> Display name of the associated AI Prompt Asset record.Table: AI Prompt Asset \[alm\_ai\_prompt\_digital\_asset\] Data type: String </td></tr><tr><td> result.documentation </td><td> Documentation for the AI Prompt Product Model record.Table: AI Prompt Product Model \[cmdb\_ai\_prompt\_product\_model\] Data type: String </td></tr><tr><td> result.managed\_by </td><td> User who manages the AI Prompt Asset record.Data type: Object "managed_by": { "name": "String", "sys_id": "String" } Text </td></tr><tr><td> result.managed\_by.name </td><td> Name of the user who manages the asset record.Tables: User \[sys\_user\], AI Prompt Asset \[alm\_ai\_model\_digital\_asset\] Data type: String </td></tr><tr><td> result.managed\_by.sys\_id </td><td> Sys\_id of the User record that corresponds to the Managed by user of the asset record.Tables: User \[sys\_user\], AI Prompt Asset \[alm\_ai\_model\_digital\_asset\] Data type: String </td></tr><tr><td> result.name </td><td> Name of the associated AI Prompt Product Model.Table: AI Prompt Product Model \[cmdb\_ai\_prompt\_product\_model\] Data type: String </td></tr><tr><td> result.prompt\_info </td><td> Prompt information of the AI Prompt Asset record.Table: AI Prompt Asset \[alm\_ai\_prompt\_digital\_asset\] Data type: String </td></tr><tr><td> result.provider </td><td> Provider of the AI Prompt Product Model record.Table: AI Prompt Product Model \[cmdb\_ai\_prompt\_product\_model\] "provider": { "name": "String", "sys_id": "String" } Text Data type: Object </td></tr><tr><td> result.provider.name </td><td> Name of the associated AI Prompt Product Model record.Table: AI Prompt Product Model \[cmdb\_ai\_prompt\_product\_model\] Data type: String </td></tr><tr><td> result.provider.sys\_id </td><td> Sys\_id of the Company table record that corresponds to the provider of the associated AI Prompt Product Model record.Tables: Company \[core\_company\], AI Prompt Product Model \[cmdb\_ai\_prompt\_product\_model\] Data type: String </td></tr><tr><td> result.state </td><td> State of the AI Prompt Asset record.Possible values: - In use- Deployed- Retired- Development- Unknown- N/A Data type: String </td></tr><tr><td> result.sys\_id </td><td> Sys\_id of the AI Prompt Asset record.Table: AI Prompt Asset \[alm\_ai\_prompt\_digital\_asset\] Data type: String </td></tr><tr><td> result.updated </td><td> Date and time that the AI Prompt Asset record was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String </td></tr><tr><td> result.version </td><td> Version of the associated AI Prompt Product Model record. For example, `V2`.Data type: String </td></tr></tbody></table>### cURL request The following example shows how to retrieve information about a AI Prompt Asset Model using a specific ID. curl "https://instance.servicenow.com/api/sn_ent/asset/ai_prompt/cc419cb2331e92101c9aca989d5c7b4c" --request GET --header "Accept:application/json" --user "username":"password" Text `The response body returns details about the specific prompt asset model that was retrieved.` { "result": { "sys_id": "cc419cb2331e92101c9aca989d5c7b4c", "display_name": "ServiceNow ServiceNow Incident Summarization Prompt 4 V8", "name": "ServiceNow Incident Summarization Prompt 4", "description": "Prompt for Incident Summarization", "version": "V8", "provider": { "sys_id": "93d4ecfac0a8000b6294d71b733977fb", "name": "ServiceNow" }, "documentation": "Documentation", "state": "Development", "ai_model": { "sys_id": "a57d0be6eb1e5210aa82fab8bad0cd18", "name": "mistral-large" }, "prompt_info": "Provide incident summary using short_decription, state, worknotes", "managed_by": { "sys_id": "62826bf03710200044e0bfc8bcbe5df1", "name": "Abel Tuter" }, "created": "2024-12-09 03:18:46", "updated": "2024-12-09 04:26:08" } } Text ## AI Assets API - GET /sn\_ent/asset/ai\_system/\{sys\_id\} Retrieves the data of the specified AI system asset. ### URL format Versioned URL: `/api/sn_ent/{api_version}/asset/ai_system/{sys_id}` Default URL: `/api/sn_ent/asset/ai_system/{sys_id}` ### Supported request parameters <table id="table_ksp_xkw_5dc" class="rest_api_path_parameters"><thead><tr><th> Name </th><th> Description </th></tr></thead><tbody><tr><td> api\_version </td><td id="version-entry-RESTAPI"> Optional. Version of the endpoint to access. For example, `v1` or `v2`. Only specify this value to use an endpoint version other than the latest. Data type: String </td></tr><tr><td> sys\_id </td><td> Sys\_id of the asset.Table: AI System Digital Asset \[alm\_ai\_system\_digital\_asset\] Data type: String </td></tr></tbody></table>\|Name\|Description\|\|----\|-----------\|\|None\| \| \|Name\|Description\|\|----\|-----------\|\|None\| \| ### Headers The following request and response headers apply to this HTTP action only, or apply to this action in a distinct way. For a list of general headers used in the REST API, see [Supported REST API headers](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/api-reference/rest-api-explorer/c_RESTAPI.md). \|Header\|Description\|\|------\|-----------\|\|Accept\|Data format of the response body. Only supports application/json.\|\|Content-Type\|Data format of the request body. Only supports application/json.\| \|Header\|Description\|\|------\|-----------\|\|None\| \| ### Status codes The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see [REST API HTTP response codes](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/api-reference/rest-api-explorer/c_RESTAPI.md). \|Status code\|Description\|\|-----------\|-----------\|\|200\|Successful. The request was successfully processed.\|\|401\|Unauthorized. The user credentials are incorrect or have not been passed.\|\|404\|Not found. Failed to fetch the asset with the given sys\_id.\|\|500\|Internal server error. An unexpected error occurred while processing the request. The response contains additional information about the error.\| ### Response body parameters <table id="table_ssp_xkw_5dc"><thead><tr><th> Name </th><th> Description </th></tr></thead><tbody><tr><td> result </td><td> Details about the retrieved AI System Asset.```result: { "ai_models": [Array], "ai_prompts": [Array], "created": "String", "description": "String", "display_name": "String", "documentation": "String", "evaluation_datasets": [Array], "evaluation_metrics_report": "String", "managed_by": {Object}, "name": "String", "provider": {Object}, "state": Number, "sys_id": "String", "updated": "String", "version": "String"}
result.ai_models	List of AI models in the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: Array Text `"ai_models": [ { "name": "String", "sys_id": "String" }]`
result.ai_models.name	Name of the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.ai_models.sys_id	Sys_id of the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.ai_prompts	List of AI Prompts in the AI System Asset record.Data type: Array Text `"ai_prompts": [ { "name": "String", "sys_id": "String" }]`
result.ai_prompts.name	Name of the AI prompt.Data type: String
result.ai_prompts.sys_id	Sys_id of the AI Prompt Digital Asset record.Table: AI Prompt Digital Asset [alm_ai_prompt_digital_asset] Data type: String
result.created	Date and time that the AI System Asset was created.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.description	Description of the associated AI System Product Model record. Table: AI System Product Model [cmdb_ai_system_product_model] Data type: String
result.display_name	Display name of the AI System Asset record.Table: AI System Asset [cmdb_ai_ system_asset_model] Data type: String
result.documentation	Documentation for the AI System Product System record.Table: AI System Product System [cmdb_ai_system_product_system] Data type: String
result.evaluation_datasets	List of AI datasets used for evaluating the model in the AI System Digital Asset record. Mostly applicable for models developed within an organization.Tables: AI Dataset Digital Asset [alm_ai_dataset_digital_asset], AI System Asset [alm_ai_system_digital_asset] Data type: Array Text `"evaluation_datasets": [ { "name": "String", "sys_id": "String" }]` Default: empty string
result.evaluation_datasets.name	Name of the AI Dataset Digital Asset.Data type: String
result.evaluation_datasets.sys_id	Sys_id of the AI Dataset Digital Asset record.Table: AI Dataset Digital Asset [alm_ai_dataset_digital_asset] Data type: String
result.evaluation_metrics_report	Evaluation results of the AI system asset record.Possible values: Details (in plain text) outlining results Links to specific results Data type: String
result.managed_by	Details about the user who manages the AI System Asset record.Data type: Object Text `"managed_by": [ { "name": "String", "sys_id": "String" }]`
result.managed_by.name	Sys_id of the user who manages the AI System Asset record.Table: User [user] Data type: String
result.managed_by.sys_id	Name of the user who manages the AI System Asset record.Table: User [user] Data type: String
result.name	Name of the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_system_product_model] Data type: String
result.provider	Value of the Provider field in the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_ system_product_model] Data type: Object Text `provider: { "name": "String", "sys_id": "String"}`
result.provider.name	Name of the provider in the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_system_product_model] Data type: String
result.provider.sys_id	Sys_id of the provider in the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_system_product_model] Data type: String
result.state	State of the AI System Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Possible values: In use Deployed Retired Development Unknown N/A Data type: String
result.sys_id	Sys_id of the AI System Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.updated	Date and time that the AI System Asset was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.version	Version number of the associated AI System Product Model record. For example, `V2`.Data type: String

### cURL request

The following example shows how to retrieve an AI System Model Asset with a given ID.

Shell

curl "https://instance.servicenow.com/api/sn_ent/asset/ai_system/3b140397435a9210a63d00002fb8f2d7" \--request GET \--header "Accept:application/json" \--user "username":"password"

The response body returns details about the given AI System Model Asset.

JSON

{  "result": {      "sys_id": "3b140397435a9210a63d00002fb8f2d7",      "display_name": "ServiceNow Incident Summarization V2",      "name": "Incident Summarization",      "description": "Incident Summarization Skill",      "version": "V2",      "provider": {        "sys_id": "93d4ecfac0a8000b6294d71b733977fb",        "name": "ServiceNow"      },      "documentation": "Sample Documentation",      "state": "Deployed",      "ai_models": [{         "sys_id": "9tgdc7e6eb1e5210aa82fab8bad0cda2",         "name": "llm_generic_small"       },      {         "sys_id": "7efdc7e6eb1e5210aa82fab8bad0cda2",         "name": "mixtral-instruct"       }],      "ai_prompts": [{         "sys_id": "7d7dc7e6eb1e5210aa82fab8bad0cda2",         "name": "LLM Prompt"       }],      "evaluation_datasets": [{         "sys_id": "9d7dc7e6eb1e5210aa82fab8bad0cda2",         "name": "Base dataset"       }],      "evaluation_metrics_report": "Sample Report",      "managed_by": {        "sys_id": "62826bf03710200044e0bfc8bcbe5df1",        "name": "Abel Tuter"      },      "created": "2024-12-11 18:23:09",      "updated": "2024-12-11 18:23:09"  }}

AI Assets API - POST /sn_ent/asset/ai_dataset

Creates a new AI dataset asset entry in the AI Dataset Digital Asset [alm_ai_dataset_digital_asset] and AI Dataset Product Model [cmdb_ai_dataset_product_model] according to details you provide in the request body.

URL format

Versioned URL: /api/sn_ent/{api_version}/asset/ai_dataset

Default URL: /api/sn_ent/asset/ai_dataset

Supported request parameters

Name	Description
api_version	Optional. Version of the endpoint to access. For example, `v1` or `v2`. Only specify this value to use an endpoint version other than the latest. Data type: String

|Name|Description| |----|-----------| |None| |

Name	Description
{object}	JavaScript `{ "acceptable_usage": "String", "base_datasets": [Array], "dataset_card": "String" "data_type": "String", "description": "String", "documentation": "String", "managed_by": "String", "name": "String", "provider": "String", "state": "String" "source": "String", "version": "String"}`
{object}.acceptable_usage	The purpose of the dataset or model, like for training or evaluation.Valid values: `1`: indicates training purpose `2`: indicates an evaluative purpose Data type: String (number)
{object}.base_datasets	Comma-separated list of base datasets needed to build this dataset. Accepts names or sys_ids of datasets present in AI Dataset Digital Asset [alm_ai_dataset_digital_asset] table.Data type: Array Text `“base_datasets”: ["String", “String"]`
{object}.data_type	Type of data present in the dataset. For example, `Text,Video,Image` or `1,2`.Data type: String
{object}.dataset_card	The data set card. A dataset_card is a metadata document that describes the contents, structure, and context of an AI dataset. It provides details like data sources, features, intended use, and any known limitations to ensure proper understanding and usage.Data type: String
{object}.description	Description of the associated AI Dataset Product Model.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
{object}.documentation	Documentation for the AI Dataset Product Model.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
{object}.managed_by	Sys_id or name value of the user in the Managed by field of the AI Dataset Product Model record. Tables: User [sys_user], AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
{object}.name	Required. Name of the associated record in the AI Dataset Product Model [cmdb_ai_dataset_product_model] table.Data type: String
{object}.provider	Required. Sys_id or name value of the Provider field of the Company [core_company] table record.Data type: String
{object}.source	Details about the source of the dataset.Valid values: Link to the source of the dataset. Details of the source in plain text. Data type: String Default: empty or null
{object}.state	State of the AI dataset asset.Valid values: In use Deployed Retired Development Unknown N/A Data type: String
{object}.version	Version number of the associated AI Dataset Product Model record. For example, `V2`.Data type: String

### Headers

Header	Description
Accept	Data format of the response body. Only supports application/json.
Content-Type	Data format of the request body. Only supports application/json.

Header	Description
None

Status codes

The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see REST API HTTP response codes.

Status code	Description
200	Successful. The request was successfully processed.
400	Bad Request. A bad request type or malformed request was detected.
500	Internal server error. An unexpected error occurred while processing the request. The response contains additional information about the error.

Response body parameters (JSON or XML)

Name	Description
result	Details of the newly created AI Dataset asset.Data type: Object JavaScript `"result": { "acceptable_usage": {Object}, "base_datasets": [Array], "created": "String", "dataset_card": "String", "data_type": {Object}, "description": "String", "documentation": "String", "display_name": "String", "managed_by": {Object}, "name": "String", "provider": {Object}, "state": "Development", "source": "String", "sys_id": "String", "updated": "String", "version": "String",}`
result.acceptable_usage	Acceptable usage for the AI Dataset Asset record. Acceptable usage refers to how a dataset or model can be used, typically for training or evaluation purposes.Data type: Object CSS `"acceptable_usage": { "label": "String" "value": "String" }`
result.acceptable_usage.label	Display label of the acceptable usage value.Data type: String
result.acceptable_usage.value	The purpose of the dataset or model, like for training or evaluation.Valid values: `1`: indicates training purpose `2`: indicates an evaluative purpose Data type: String (number)
result.base_datasets	Comma-separated list of base datasets required to build the given dataset. Accepts the name or sys_id of a base data set in the AI Dataset Digital Asset [alm_ai_dataset_digital_asset] table.Data type: Array Text `"base_datasets": ["String", "String"]`
result.created	Date and time that the AI Dataset Asset record was created.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.data_type	The type of data present in the AI Dataset Asset record.Data type: Object CSS `"data_type": { "label": "String", "value": "String"}`
result.data_type.label	The display label for the data type value.Data type: String
result.data_type.value	Value of the dataset asset's data type.Data type: String
result.dataset_card	The data set card. A dataset_card is a metadata document that describes the contents, structure, and context of an AI dataset. It provides details like data sources, features, intended use, and any known limitations to ensure proper understanding and usage.Data type: String
result.description	Description of the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.display_name	Display name of the AI Dataset Asset record.Table: AI Dataset Digital Asset [alm_ai_dataset_digital_asset] Data type: String
result.documentation	Documentation of the associated AI Dataset Product Model [cmdb_ai_dataset_product_model] table record.Data type: String
result.managed_by	Details about the user who manages the AI Dataset Asset record.Data type: Object Text `"managed_by": [ { "name": "String", "sys_id": "String" }]`
result.managed_by.name	Name of the user who manages the AI Dataset Asset record.Table: User [user] Data type: String
result.managed_by.sys_id	Sys_id of the user who manages the AI Dataset Asset record.Table: User [user] Data type: String
result.name	Name of the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.provider	Provider of the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: Object Text `provider: { "name": "String", "sys_id": "String"}`
result.provider.sys_id	Sys_id of the provider in the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.provider.name	Name of the provider.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.source	Details about the source of AI dataset asset.Valid values: Link to the source of the dataset asset. Details (in plain text) of the source of the dataset asset. For example, the name of a product or website. Data type: String Default: empty or null
result.state	State of the AI Dataset Asset record.Possible values: In use Deployed Retired Development Unknown N/A Data type: String
result.sys_id	Sys_id of the AI Dataset Asset record.Table: AI Dataset Digital Asset [alm_ai_dataset_digital_asset] Data type: String
result.updated	Date and time that the AI Dataset Asset record was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.version	Version number of the associated AI Dataset Product Model record. For example, `V2`.Data type: String
result.warnings	Comma-separated list of warning messages that are present when creating the dataset. These warnings can be validation checks, such as when the sys_id of an optional parameter is invalid.Data type: Array Text `"warnings": ["String"]`

### cURL request

The following example shows how to create a new AI dataset asset record.

Shell

curl -X POST 'https://instance.servicenow.com/api/sn_ent/asset/ai_dataset' \   -H 'Accept: application/json' \   -H 'Content-Type: application/json' \   -u 'username':'password' \   -d ' {   "name": "Dataset One",   "description": "Description for dataset ",   "provider": "servicenow",   "version": "V1",   "state": 31,   "source”: "Source of dataset”,  "documentation": "document",   "dataset_card”: "Dataset Card”,   "base_datasets”: ["Dataset Two”, "Dataset Three”],   "data_type”: "1,2”,   "acceptable_usage”: "1,2”,   "managed_by": "abel.tuter" }'

Response body. The results of the newly created dataset asset record.

JSON

{   "result": {     "asset": {       "sys_id": "da8393eb40d25210f877b00c113d1fc1",       "display_name": "ServiceNow Closed Incidents",       "name": "Closed Incidents",       "description": "Incidents with resolution",       "documentation": "Sample Documentation",       "source": "incident table on servicenow instance",       "dataset_card": "Dataset Card",       "state": "Deployed",       "version": null,       "data_type": {         "value": "1",         "label": "Text"       },       "provider": {         "sys_id": "93d4ecfac0a8000b6294d71b733977fb",         "name": "ServiceNow"       },       "managed_by": {         "sys_id": "undefined",         "name": ""       },       "acceptable_usage": {         "value": "1",         "label": "Training"       },       "base_datasets": [],       "created": "2024-12-12 01:23:03",       "updated": "2024-12-12 01:23:03"     },     "warnings": []        } }

AI Assets API - POST /sn_ent/asset/ai_prompt

Creates a new AI prompt asset entry in the AI Prompt Digital Asset [alm_ai_prompt_digital_asset] and AI Prompt Product Model [cmdb_ai_promt_product_model] tables according to details you provide in the request body.

URL format

Versioned URL: /api/sn_ent/{api_version}/asset/ai_prompt

Default URL: /api/sn_ent/asset/ai_prompt

Supported request parameters

Name	Description
api_version	Optional. Version of the endpoint to access. For example, `v1` or `v2`. Only specify this value to use an endpoint version other than the latest. Data type: String

|Name|Description| |----|-----------| |None| |

Name	Description
object	Required. Content of the AI Prompt asset to update.Data type: Object JavaScript `{ "ai_model": "String", "description": "String", "documentation": "String", "managed_by": "String" "name": "String", "prompt_info": "String", "provider": "String", "state": Number, "version": "String"}`
ai_model	Name of sys_id value of the AI Model field in the AI Model Digital Asset record.Table: AI Model Digital Asset [alm_ai_model_digital_asset] Data type: String Default: empty string
description	Description to give the AI Prompt Product Model.Table: AI Prompt Product Model [cmdb_ai_prompt_product_model] Data type: String Default: empty string
documentation	Documentation of the AI Prompt Product Model.Table: AI Prompt Product Model [cmdb_ai_prompt_product_model] Data type: String Default: empty string
managed_by	Name or sys_id of the user that manages the asset. Located in the Managed by field of the asset record.Tab;e: User [sys_user] Data type: String Default: empty string
name	Required. Name of the associated record in the AI Prompt Product Model [cmdb_ai_prompt_product_model] table.Data type: String Default: empty string
prompt_info	Prompt information for AI Prompt Asset.Data type: String Default: empty string
provider	Required. Provider name or sys_id of the Company table record.Table: Company [core_company] Data type: String
state	State to apply to the AI Model Asset. For example, Valid values: In use Deployed Retired Development Unknown N/A Data type: String
version	Version number to assign the AI Prompt Product Model. For example, `V2`.Data type: String Default: empty string

### Headers

Header	Description
Accept	Data format of the response body. Only supports application/json.
Content-Type	Data format of the request body. Only supports application/json.

Header	Description
None

Status codes

The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see REST API HTTP response codes.

Status code	Description
200	Successful. The request was successfully processed.
400	Bad Request. A bad request type or malformed request was detected.
500	Internal server error. An unexpected error occurred while processing the request. The response contains additional information about the error.

Response body parameters (JSON or XML)

Name	Description
result	Results of the new AI asset prompt.Data type: Object JavaScript `"result": { "asset": Object, "warnings": [Array] }`
result.asset	Details about the newly created entry.Data type: Object JavaScript `"asset": { "ai_model": [Array], "ai_prompts": [Array] "created": "String", "description": "String", "display_name": "String", "documentation": "String", "evaluation_datasets": [Array], "evaluation_metrics_report": String, "managed_by": Object, "name": "String", "provider": Object, "state": "String", "sys_id": "String", "updated": "String", "version": "String", "warnings": [Array]}`
result.asset.ai_model	List of AI models in the AI System Asset table record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: Array Text `"ai_model": [ { "name": "String", "sys_id": "String" }]`
result.asset.ai_models.name	Name of the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.ai_models.sys_id	Sys_id of the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.ai_prompts	List of AI Prompts of the AI System Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: Array Text `"ai_prompts": [ { "name": "String", "sys_id": "String" }]`
result.asset.ai_prompts.name	Name of the AI prompt.Data type: String
result.asset.ai_prompts.sys_id	Sys_id of the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.created	Date and time that the AI model asset was created.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.asset.description	Description of the associated AI System Product Model record. Table: AI System Product Model [cmdb_ai_system_product_model] Data type: String
result.asset.display_name	Display name of the AI System Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.documentation	Documentation of the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_ system_product_model] Data type: String
result.asset.evaluation_datasets	List of sys_ids or display names of the AI datasets used to evaluate the AI system asset model. Mostly applicable for models developed within an organization.Tables: AI Dataset Digital Asset [alm_ai_dataset_digital_asset], AI System Digital Asset [alm_ai_system_digital_asset] Data type: Array Text `"evaluation_datasets": [ { "name": "String", "sys_id": "String" }]`
result.asset.evaluation_datasets.name	Name of the AI Dataset Digital Asset.Data type: String
result.asset.evaluation_datasets.sys_id	Sys_id of the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.evaluation_metrics_report	Evaluation results of the AI system asset.Possible values: Details (in plain text) outlining results Links to specific results Data type: String
result.asset.managed_by	Details about the user who manages the asset.Data type: Object Text `"managed_by": [ { "name": "String", "sys_id": "String" }]`
result.asset.managed_by.name	Name of the user who manages the AI model asset record.Table: User [user] Data type: String
result.asset.managed_by.sys_id	Sys_id of the user who manages the AI model asset record.Table: User [user] Data type: String
result.asset.name	Name of the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_system_product_model] Data type: String
result.asset.provider	Value of the provider field in the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_ system_product_model] Data type: Object Text `provider: { "name": "String", "sys_id": "String"}`
result.asset.provider.name	Name of the provider in the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_ system_product_model] Data type: String
result.asset.provider.sys_id	Sys_id of the provider in the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_ system_product_model] Data type: String
result.asset.state	State of the AI Model Asset. Possible values: In use Deployed Retired Development Unknown N/A Data type: String
result.asset.sys_id	Sys_id of the AI System Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.updated	Date and time that the AI model asset was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.warning	Comma-separated list of warning messages. These warnings can be validation checks, such as when the sys_id of an optional parameter is invalid.Data type: Array Text `"warnings": ["String"]`

### cURL request

The following example shows how to use the POST method to create a new AI prompt according to details provided in the request body.

Shell

curl -X POST 'https://instance.servicenow.com/api/sn_ent/asset/ai_prompt' \   -H 'Accept: application/json' \   -H 'Content-Type: application/json' \   -u 'username':'password' \   -d '{   "name": "Incident Summarization prompt1",   "description": "Prompt for Incident Summarization",   "provider": "servicenow",   "version": "V1",   "state": 31,   "documentation": "Document",   "ai_model": "mixtral-instruct",   "prompt_info": "Provide incident summary using short_decription, state, worknotes",   "managed_by": "abel.tuter"  }'

Response body.

JSON

{   "result": {     "asset": {       "sys_id": "9833721b331e92101c9aca989d5c7bf0",       "display_name": "ServiceNow Incident Summarization prompt1 V1",       "name": "Incident Summarization prompt1",       "description": "Prompt for Incident Summarization",       "version": "V1",       "provider": {         "sys_id": "93d4ecfac0a8000b6294d71b733977fb",         "name": "ServiceNow"       },       "documentation": "Document",       "state": "Deployed",       "ai_model": {         "sys_id": "9d7dc7e6eb1e5210aa82fab8bad0cda2",         "name": "mixtral-instruct"       },       "prompt_info": "Provide incident summary using short_decription, state, worknotes",       "managed_by": {         "sys_id": "62826bf03710200044e0bfc8bcbe5df1",         "name": "Abel Tuter"       },       "created": "2024-12-11 04:23:17",       "updated": "2024-12-11 04:23:17"     },     "warnings": []   } }

AI Assets API - POST /sn_ent/asset/ai_model

Creates a new AI model asset entry in the AI Model Digital Asset [alm_ai_model_digital_asset] and AI Model Product Model [cmdb_ai_model_product_model] tables according to details you provide in the request body.

URL format

Versioned URL: /api/sn_ent/{api_version}/asset/ai_model

Default URL: /api/sn_ent/asset/ai_model

Supported request parameters

Name	Description
api_version	Optional. Version of the endpoint to access. For example, `v1` or `v2`. Only specify this value to use an endpoint version other than the latest. Data type: String

|Name|Description| |----|-----------| |None| |

Name	Description
{object}	Required. Details to apply to the new asset model record.Data type: Object Shell "object": { "base_model": {Object}, "context_window:" "String", "deployment_guideline": "String", "description": "String", "documentation": "String", "evaluation_datasets": [Array], "evaluation_metrics_report": "String", "managed_by": "String", "model_size_in_mb": "String", "model_weights_info": "String", "name": "String", "parameters_info": "String", "provider": "String", "required_infrastructure": "String", "source": "String", "state": Number, "supported_languages: [Array], "training_datasets": [Array], "training_procedure": "String", "version": "String"}
{object}.base_model	AI model that this model version was derived from.Note: Only applicable for models developed within the organization. Data type: Object JSON `{ "name": "String", "sys_id": "String" }`
{object}.base_model.name	Name of the AI model asset to model this AI model after.Data type: String
{object}.base_model.sys_id	Sys_id of the AI model asset to model this AI model after.Table: AI Model Digital Asset [alm_ai_dataset_digital_asset] Data type: String Default: empty string
{object}.context_window	Size of input sequences (in other words, the number of tokens) that the model can handle.Data type: Integer Default: empty value
{object}.deployment_guideline	Instructions applicable for models developed and deployed within an organization.Data type: String Default: empty string
{object}.description	Description to give the AI Model Product Model. Updated table: AI Model Product Model [cmdb_ai_model_product_model] Data type: String Default: empty string
{object}.documentation	Documentation of the AI Model Product Model.Data type: String Default: empty string
{object}.evaluation_datasets	Comma-separated list of sys_ids or display names of AI datasets of the AI datasets used for evaluating the model. Mostly applicable for models developed within an organization.Tables: AI Dataset Digital Asset [alm_ai_dataset_digital_asset], AI Model Asset [alm_ai_model_digital_asset] Data type: Array Text `"evaluation_datasets": ["String", "String"]` Valid values: Name of the AI Dataset Digital Asset record. Sys_id of the AI Dataset Digital Asset record. Default: empty string
{object}.evaluation_metrics_report	Reference to the evaluation results located within the text field of the AI model digital asset record. For example:``` Testing results: link to the result document Details: Accuracy: 85% Hallucination: 10% Eval Run 1: link Eval Run 2: link Text Table: AI model digital asset \[alm\_ai\_model\_digital\_asset\] Data type: String Default: empty string </td></tr><tr><td> \{object\}.managed\_by </td><td> Value of the 'Managed by' field of an existing record in the User \[sys\_user\] table. Valid values: - Name of the User \[sys\_user\] record- Sys\_id of the User \[sys\_user\] record Data type: String Default: empty string </td></tr><tr><td> \{object\}.model\_size\_in\_mb </td><td> Size of the model in MB. Mostly applicable for models developed and deployed within an organization.Data type: Number Default: null or empty </td></tr><tr><td> \{object\}.model\_weights\_info </td><td> Additional model information, if available. Mostly applicable for models developed within an organization.Data type: String Default: null or empty </td></tr><tr><td> \{object\}.name </td><td> Required. Name of the AI Model Product Model. Table: AI Model Product Model \[cmdb\_ai\_model\_product\_model\] Data type: String Default: null or empty </td></tr><tr><td> \{object\}.parameters\_info </td><td> Number of parameters given to for the model.Data type: String Default: empty string </td></tr><tr><td> \{object\}.provider </td><td> Required. Sys\_id or name of the user to assign the Provider field in the AI Model Product Model record.Table: Company \[core\_company\] Data type: String Default: empty string </td></tr><tr><td> \{object\}.required\_infrastructure </td><td> Documentation of infrastructure needs for the model deployment. For example, details about the infrastructure stack and processing needs. Mostly applicable for models deployed within an organization.Data type: String Default: empty or null </td></tr><tr><td> \{object\}.source </td><td> Details about the source of who or what created the model.Valid values: - Link to the source of the model. For example, `https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1` or a link to Github.- Details of the source in plain text. For example, `Microsoft Azure` Data type: String Default: empty or null </td></tr><tr><td> \{object\}.state </td><td> State to apply to the AI Model Asset.Valid values: - In use- Deployed- Retired- Development- Unknown- N/A Data type: String </td></tr><tr><td> \{object\}.supported\_languages </td><td> Comma-separated list of languages that are supported by the AI Model.Data type: Array "supported_languages": [ "String", "String" ] Text Valid values: - Sys\_id of the Language \[sys\_language\] record.- Name of the Language \[sys\_language\] record. For example, `"French", "English"`. Default: empty string </td></tr><tr><td> \{object\}.training\_datasets </td><td> Datasets used to train the AI Model. Comma-separated list of sys\_ids or display names of the AI Dataset Asset \[alm\_ai\_dataset\_digital\_asset\] table. Mostly applicable for models developed within an organization.Data type: Array "training_datasets": [ "String", "String" ] Text Default: empty string </td></tr><tr><td> \{object\}.training\_procedure </td><td> Number indicating the type of training to apply to the AI model.Valid values: - `1`: Decision Trees- `2`: Deep Neural Networks- `3`: Linear Regression- `4`: Logistic Regression- `5`: Random Forest- `6`: Supervised Learning- `7`: Unsupervised Learning- `8`: Reinforcement Learning- `9`: Transfer Learning- `10`: Semi-Supervised Learning- `11`: Instruction Finetuning- `12`: Supervised Finetuning Data type: String represented with a number \(for example, `"3"`\) Default: 1: Decision Trees </td></tr><tr><td> \{object\}.version </td><td> Version for AI Model Product Model. For example, `V2`.Data type: String Default: empty string </td></tr></tbody></table>### Response body parameters \(JSON or XML\) ### Headers The following request and response headers apply to this HTTP action only, or apply to this action in a distinct way. For a list of general headers used in the REST API, see [Supported REST API headers](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/api-reference/rest-api-explorer/c_RESTAPI.md). \|Header\|Description\|\|------\|-----------\|\|Accept\|Data format of the response body. Only supports application/json.\|\|Content-Type\|Data format of the request body. Only supports application/json.\| \|Header\|Description\|\|------\|-----------\|\|None\| \| ### Status codes The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see [REST API HTTP response codes](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/api-reference/rest-api-explorer/c_RESTAPI.md). \|Status code\|Description\|\|-----------\|-----------\|\|201\|Successful. The request was successfully processed.\|\|400\|Bad Request. A bad request type or malformed request was detected.\|\|500\|Internal server error. An unexpected error occurred while processing the request. The response contains additional information about the error.\| ### Response body parameters \(JSON or XML\) <table id="id_itg_lz3_12c"><thead><tr><th> Name </th><th> Description </th></tr></thead><tbody><tr><td> result </td><td> Results of the AI model asset creation.Data type: Object "result": { "asset": Object, "warnings": [Array], } Text `</td></tr><tr><td> result.asset </td><td> Details about the created AI model asset.Data type: Object` "asset": { "base_model": Object, "context_window": String, "created": "String", "deployment_guideline": String, "description": "String", "display_name": "String", "documentation": "String", "evaluation_datasets": Array, "evaluation_metrics_report": String, "managed_by": Object, "model_size_in_mb": String, "name": "String", "parameters_info": "String", "provider": Object, "required_infrastructure": String, "state": "String", "source": String, "supported_languages": Array, "sys_id": "String", "training_datasets": Array, "training_procedure": String, "updated": "String", "version": "String" } Text `</td></tr><tr><td> result.asset.base\_model </td><td> Information about the AI model asset from which this version derived from.Data type: Object` "base_model": { "name": "String", "sys_id": "String" } Text </td></tr><tr><td> result.asset.base\_model.name </td><td> Display name of AI model asset.Table: AI Model Digital Asset \[alm\_ai\_model\_digital\_asset\] Data type: String </td></tr><tr><td> result.asset.base\_model.sys\_id </td><td> Sys\_id of AI model asset.Table: AI Model Digital Asset \[alm\_ai\_model\_digital\_asset\] Data type: String </td></tr><tr><td> result.asset.context\_window </td><td> Size of input sequences that the model can handle. In other words, the number of tokens.Data type: String represented with a number. For example, `"6000"`. </td></tr><tr><td> result.asset.created </td><td> Date and time that the AI model asset was created.Format: YYYY-MM-DD HH:mm:ss Data type: String </td></tr><tr><td> result.asset.deployment\_guideline </td><td> Instructions applicable for models developed and deployed within an organization.Data type: String </td></tr><tr><td> result.asset.description </td><td> Description of the associated AI Model Product Model record.Table: AI Model Product Model \[cmdb\_ai\_model\_product\_model\] Data type: String </td></tr><tr><td> result.asset.display\_name </td><td> Display name of the AI Model Asset record.Table: AI Model Digital Asset \[alm\_ai\_model\_digital\_asset\] \(display\_name field\) Data type: String </td></tr><tr><td> result.asset.documentation </td><td> Documentation of the associated AI Model Product Model record.Table: AI Model Product Model \[cmdb\_ai\_model\_product\_model\] Data type: String </td></tr><tr><td> result.asset.evaluation\_datasets </td><td> AI datasets used for evaluating the model. Mostly applicable for models developed within an organization.Tables: AI Dataset Digital Asset \[alm\_ai\_dataset\_digital\_asset\], AI Model Digital Asset \[alm\_ai\_model\_digital\_asset\] Data type: Array "evaluation_datasets": [ { "name": "String", "sys_id": "String" } ] Text </td></tr><tr><td> result.evaluation\_datasets.name </td><td> Name of the AI Dataset Digital Asset.Table: AI Model Digital Asset \[alm\_ai\_model\_digital\_asset\] Data type: String </td></tr><tr><td> result.evaluation\_datasets.sys\_id </td><td> Sys\_id of the AI Model Digital Asset record.Table: AI Model Digital Asset \[alm\_ai\_model\_digital\_asset\] Data type: String </td></tr><tr><td> result.asset.evaluation\_metrics\_report </td><td> Reference to the evaluation results.Possible values: - Details \(in plain text\) outlining results- Links to specific results Data type: String </td></tr><tr><td> result.asset.managed\_by </td><td> User that manages the AI model asset record.Data type: Object " managed_by": { "name": "String", "sys_id": "String" } Text </td></tr><tr><td> result.asset.managed\_by.name </td><td> Name of the user who manages the AI model asset record.Table: User \[user\] Data type: String </td></tr><tr><td> result.asset.managed\_by.sys\_id </td><td> Sys\_id of the user who manages the AI model asset record.Table: User \[user\] Data type: String </td></tr><tr><td> result.asset.model\_size\_in\_mb </td><td> Size of the model in MB. Usually applicable for models developed and deployed within an organization.Data type: Number </td></tr><tr><td> result.asset.name </td><td> Name of the associated AI Model Product Model record.Table: AI Model Product Model \[cmdb\_ai\_model\_product\_model\] Data type: String </td></tr><tr><td> result.asset.provider </td><td> Provider of the associated AI Model Product Model record.Table: AI Model Product Model \[cmdb\_ai\_model\_product\_model\] Data type: Object "provider": { "name": "String", "sys_id": "String" } Text </td></tr><tr><td> result.asset.provider.name </td><td> Name of the the Company record that corresponds to the provider of the associated AI Model Product Model record.Tables: Company \[core\_company\], AI Model Product Model \[cmdb\_ai\_model\_product\_model\] Data type: String </td></tr><tr><td> result.asset.provider.sys\_id </td><td> Sys\_id of the Company record that corresponds to the provider of the associated AI Model Product Model record.Tables: Company \[core\_company\], AI Model Product Model \[cmdb\_ai\_model\_product\_model\] Data type: String </td></tr><tr><td> result.asset.source </td><td> Details about the source of the asset.Valid values: - Link to the source of the model. For example, `https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1` or a link to Github.- Details of the source in plain text. For example, `Microsoft Azure` Data type: String </td></tr><tr><td> result.asset.state </td><td> State of the AI Model Asset record.Possible values: - In use- Deployed- Retired- Development- Unknown- N/A Data type: String </td></tr><tr><td> result.asset.supported\_languages.name </td><td> Name of the supported language.Table: Language \[sys\_language\] Data type: String </td></tr><tr><td> result.asset.supported\_languages.sys\_id </td><td> Sys\_id of the supported language.Table: Language \[sys\_language\] Data type: String </td></tr><tr><td> result.asset.sys\_id </td><td> Sys\_id of the AI Model Asset record Table: AI Model Digital Asset \[alm\_ai\_model\_digital\_asset\] Data type: String </td></tr><tr><td> result.asset.training\_datasets </td><td> Reference to 1+ associated data sets used for training the model.Data type: Array "training_datasets": [ { "name": "String", "sys_id": "String" } ] Text </td></tr><tr><td> result.asset.training\_procedure </td><td> A numeric value indicating the type of AI training applied to the model.Possible values: - `1`: Decision Trees- `2`: Deep Neural Networks- `3`: Linear Regression- `4`: Logistic Regression- `5`: Random Forest- `6`: Supervised Learning- `7`: Unsupervised Learning- `8`: Reinforcement Learning- `9`: Transfer Learning- `10`: Semi-Supervised Learning- `11`: Instruction Finetuning- `12`: Supervised Finetuning Data type: String represented by a number </td></tr><tr><td> result.asset.updated </td><td> Date and time that the AI model asset was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String </td></tr><tr><td> result.asset.version </td><td> Version of the associated AI Model Product Model record.Table: AI Model Product Model \[cmdb\_ai\_model\_product\_model table\] Data type: String </td></tr><tr><td> result.required\_infrastructure </td><td> Documentation of infrastructure needs for the model's deployment. For example, details about the infrastructure stack and processing needs.Data type: String </td></tr><tr><td> result.asset.parameters\_info </td><td> Properties of the training data that learn during the learning process. For example: `7B` or `30B`.Data type: String </td></tr><tr><td> result.asset.supported\_languages </td><td> Details about the languages supported by the AI model asset.Table: Language \[sys\_language\] Data type: Object "supported_languages": [ { "name": "String", "sys_id": "String" } ] Text </td></tr><tr><td> result.asset.supported\_languages.name </td><td> Name of the language that is supported by the asset.Table: Language \[sys\_language\] Data type: String </td></tr><tr><td> result.asset.supported\_languages.sys\_id </td><td> Sys\_id of the language that is supported by the asset.Table: Language \[sys\_language\] Data type: String </td></tr><tr><td> result.warnings </td><td> Comma-separated list of warning messages. These warnings can be validation checks, such as when the sysId of an optional parameter is invalid.Data type: Array "warnings": ["String"] Text `</td></tr></tbody></table>### cURL request The following example creates an AI model asset according to details provided in the request body.` curl -X POST 'https://instance.servicenow.com/api/sn_ent/asset/ai_prompt' \ -H 'Accept: application/json' \ -H 'Content-Type: application/json' \ -u 'username':'password' \ -d ' { "name": "Now LLM", "description": "enables text-to-text like question answering and summarization", "provider": "servicenow", "documentation": "Now LLM V5 Documentation", "version": "V8", "parameters_info": "7B", "supported_languages": [ "English", "French" ], "model_size_in_mb": "87", "deployment_guideline": "Deployed on ServiceNow infrastructure", "source": "https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1", "training_procedure": "2", "context_window": "8000", "state": "31", "base_model": "Servicenow Now LLM V6", "model_weights_info": "refer to weights and biases project", "required_infrastructre": "GPUs needed: 1, GPU Type: A100", "training_datasets": [ "Servicenow Open Incidents" ], "evaluation_datasets": [ "Servicenow Open Incidents" ], "evaluation_metrics_report": "Testing results: See files attached to this record", "managed_by": "abel.tuter" }' Text `The response body shows details about the newly created AI model asset, including the resultant sys\_id.` { "result": { "asset": { "sys_id": "a438d170ff96da10c1fbffffffffffd5", "display_name": "ServiceNow Now LLM V6", "name": "Now LLM", "description": "enables text-to-text like question answering and summarization", "version": "V8", "provider": { "sys_id": "93d4ecfac0a8000b6294d71b733977fb", "name": "ServiceNow" }, "documentation": "Now LLM V5 Documentation", "parameters_info": "7B", "supported_languages": [ { "sys_id": "914493a30f320010e96b0e4fef767e90", "name": "English" } ], "model_size_in_mb": "87", "deployment_guideline": "Deployed on ServiceNow infrastructure", "source": "https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1", "training_procedure": "2", "context_window": "8000", "state": "Deployed", "required_infrastructure": "Servicenow Instance", "base_model": { "sys_id": "a438d170ff96da10c1fbffffffffffd5", "name": "ServiceNow Now LLM V8" }, "evaluation_datasets": [ { "sys_id": "45cb45baff06d610c1fbffffffffffa9", "name": "ServiceNow Open Incidents" } ], "training_datasets": [ { "sys_id": "45cb45baff06d610c1fbffffffffffa9", "name": "ServiceNow Open Incidents" } ], "evaluation_metrics_report": "Testing results: See files attached to this record", "managed_by": { "sys_id": "62826bf03710200044e0bfc8bcbe5df1", "name": "Abel Tuter" }, "created": "2024-12-03 16:50:53", "updated": "2024-12-12 15:56:28" }, "warnings": [ "Reference record 'French' not found for supported_languages in table sys_language" ] } } Text ## AI Assets API - POST /sn\_ent/asset/ai\_system Creates a new AI system asset entry in the AI System Digital Asset \[alm\_ai\_system\_digital\_asset\] and AI System Product Model \[cmdb\_ai\_system\_product\_model\] tables according to details you provide in the request body. ### URL format Versioned URL: `/api/sn_ent/{api_version}/asset/ai_system` Default URL: `/api/sn_ent/asset/ai_system` ### Supported request parameters <table class="rest_api_path_parameters"><thead><tr><th> Name </th><th> Description </th></tr></thead><tbody><tr><td> api\_version </td><td id="version-entry-RESTAPI"> Optional. Version of the endpoint to access. For example, `v1` or `v2`. Only specify this value to use an endpoint version other than the latest. Data type: String </td></tr></tbody></table>\|Name\|Description\|\|----\|-----------\|\|None\| \| <table id="id_ack_2dh_zdc" class="rest_api_request_parameters"><thead><tr><th> Name </th><th> Description </th></tr></thead><tbody><tr><td> object </td><td> Required. Details to update in the AI system.Data type: Object { "name": "String", "description": "String", "provider": "String", "version": "String", "state": Number, "documentation": "String", "ai_models": "String", "ai_prompts": "Strings", "evaluation_datasets": [Array], "evaluation_metrics_report": "String", "managed_by": "String" } Text </td></tr><tr><td> object.name </td><td> Required. Name for the AI System Product Model.Data type: String </td></tr><tr><td> object.description </td><td> Description for the AI System Product Model.Data type: String </td></tr><tr><td> object.provider </td><td> Required. Sys\_id or name value of the Provider field of the Company \[core\_company\] table record.Data type: String </td></tr><tr><td> object.state </td><td> State to apply to the AI System Asset. Valid values: - In use- Deployed- Retired- Development- Unknown- N/A Data type: String Default: empty string </td></tr><tr><td> object.version </td><td> Version for AI System Product Model. For example, `V2`.Data type: String Default: empty string </td></tr><tr><td> object.documentation </td><td> Documentation of the AI System Asset.Data type: String Default: </td></tr><tr><td> object.ai\_models </td><td> Comma-separated list of names or sys\_ids of the AI Model Digital Asset record, where each string represents an AI Model field value of an existing AI Model Digital Asset record.Table: AI Model Digital Asset \[alm\_ai\_model\_digital\_asset\] Data type: Array of Strings "ai_models": [{String", "String"}] Text `Default: empty string </td></tr><tr><td> object.evaluation\_datasets </td><td> Comma-separated list of names or sys\_ids of the AI datasets used for evaluating the AI System Asset record. Mostly applicable for models developed within an organization.Tables: AI Dataset Digital Asset \[alm\_ai\_dataset\_digital\_asset\], AI System Asset \[AI System Digital Asset alm\_ai\_system\_digital\_asset\] Data type: Array of Strings` "evaluation_datasets": [{String", "String"}] Text Default: empty string </td></tr><tr><td> object.evaluation\_metrics\_report </td><td> Reference to the evaluation results located within the text field of the AI system asset record. For example:```Testing results: link to the result document Details:Accuracy: 85%Hallucination: 10%Eval Run 1: linkEval Run 2: link Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String Default: empty string
object.managed_by	Sys_id or name of the 'Managed by' field of an existing User [sys_user] table record.Data type: String Default: empty string

### Headers

Header	Description
Accept	Data format of the response body. Only supports application/json.
Content-Type	Data format of the request body. Only supports application/json.

Header	Description
None

Status codes

The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see REST API HTTP response codes.

Status code	Description
200	Successful. The request was successfully processed.
400	Bad Request. A bad request type or malformed request was detected.
500	Internal server error. An unexpected error occurred while processing the request. The response contains additional information about the error.

Response body parameters (JSON or XML)

Name	Description
result	Results of the new AI system asset.Data type: Object JavaScript `"result": { "asset": Object, "warnings": [Array]}`
result.asset	Details about the newly created entry.Data type: Object JavaScript `"asset": { "ai_model": [Array], "ai_prompts": [Array] "created": "String", "description": "String", "display_name": "String", "documentation": "String", "evaluation_datasets": [Array], "evaluation_metrics_report": String, "managed_by": Object, "name": "String", "provider": Object, "state": "String", "sys_id": "String", "updated": "String", "version": "String", "warnings": [Array]}`
result.asset.ai_models	List of AI models in the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: Array Text `"ai_models": [ { "name": "String", "sys_id": "String" }]`
result.asset.ai_models.name	Name of the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.ai_models.sys_id	Sys_id of the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.ai_prompts	List of AI Prompts of the AI System Digital Asset [alm_ai_system_digital_asset] record.Data type: Array Text `"ai_prompts": [ { "name": "String", "sys_id": "String" }]`
result.asset.ai_prompts.name	Name of the AI prompt.Data type: String
result.asset.ai_prompts.sys_id	Sys_id of the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.created	Date and time that the AI model asset was created.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.asset.description	Description of the associated AI System Product Model record. Table: AI System Product Model [cmdb_ai_system_product_model] Data type: String
result.asset.display_name	Display name of the AI System Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.documentation	Documentation for the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_ system_product_model] Data type: String
result.asset.evaluation_datasets	List of AI datasets used for evaluating the AI System Asset record. Mostly applicable for models developed within an organization.Tables: AI Dataset Digital Asset [alm_ai_dataset_digital_asset], AI System Asset [alm_ai_system_digital_asset] Data type: Array Text `"evaluation_datasets": [ { "name": "String", "sys_id": "String" }]`
result.asset.evaluation_datasets.name	Name of the AI Dataset Digital Asset.Table: AI Dataset Digital Asset [alm_ai_dataset_digital_asset] Data type: String
result.asset.evaluation_datasets.sys_id	Sys_id of the AI Dataset Digital Asset record.Table: AI Dataset Digital Asset [alm_ai_dataset_digital_asset] Data type: String
result.asset.evaluation_metrics_report	Evaluation results of the AI system asset.Possible values: Details (in plain text) outlining results Links to specific results Data type: String
result.asset.managed_by	Comma-separated list of sys_ids or display names of AI datasets of the AI System Asset used for evaluating the model. Mostly applicable for models developed within an organization.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: Object Text `"managed_by": [ { "name": "String", "sys_id": "String" }]`
result.asset.managed_by.name	Sys_id of the user who manages the AI model asset record.Table: User [user] Data type: String
result.asset.managed_by.sys_id	Name of the user who manages the AI model asset record.Table: User [user] Data type: String
result.asset.name	Name of the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_system_product_model] Data type: String
result.asset.provider	Value of the Provider field in the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_ system_product_model] Data type: Object Text `provider: { "name": "String", "sys_id": "String"}`
result.asset.provider.name	Name of the provider in the associated AI System Product Model record.Table: System Product Model [cmdb_ai_ system_product_model] Data type: String
result.asset.provider.sys_id	Sys_id of the provider in the associated AI System Product Model record.Table: System Product Model [cmdb_ai_ system_product_model] Data type: String
result.asset.state	State of the AI Model Asset. Possible values: In use Deployed Retired Development Unknown N/A Data type: String
result.asset.sys_id	Sys_id of the AI System Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.updated	Date and time that the AI model asset was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.asset.version	Version number of the associated AI System Product Model record. For example, `V2`.Data type: String
result.asset.warnings	Comma-separated list of warning messages. These warnings can be validation checks, such as when the sys_id of an optional parameter is invalid.Data type: Array Text `"warnings": ["String"]`

### cURL request

The following example inserts a new entry into the AI System Digital Asset and AI System Product Model tables using details included in the request body.

Shell

curl -X POST 'https://instance.servicenow.com/api/sn_ent/asset/ai_prompt' \   -H 'Accept: application/json' \   -H 'Content-Type: application/json' \   -u 'username':'password' \   -d '{   "name": "Incident Summarization",   "description": "Incident Summarization Skill",   "provider": "servicenow",   "documentation": "Sample Documentation",   "version": "V2",   "state": 31,   "ai_models": [     "llm_generic_small",     "mixtral-instruct"   ],   "ai_prompts": [     "LLM Prompt"     ],   "evaluation_datasets": [     "Base dataset"   ],   "evaluation_metrics_report": "Sample Report",   "managed_by": "abel.tuter" }'

Response body.

JSON

{   "result": {     "asset": {       "sys_id": "3b140397435a9210a63d00002fb8f2d7",       "display_name": "ServiceNow Incident Summarization V2",       "name": "Incident Summarization",       "description": "Incident Summarization Skill",       "version": "V2",       "provider": {         "sys_id": "93d4ecfac0a8000b6294d71b733977fb",         "name": "ServiceNow"       },       "documentation": "Sample Documentation",       "state": "Deployed",       "ai_models": [{          "sys_id": "9tgdc7e6eb1e5210aa82fab8bad0cda2",          "name": "llm_generic_small"        },       {          "sys_id": "7efdc7e6eb1e5210aa82fab8bad0cda2",          "name": "mixtral-instruct"        }],       "ai_prompts": [{          "sys_id": "7d7dc7e6eb1e5210aa82fab8bad0cda2",          "name": "LLM Prompt"        }],       "evaluation_datasets": [{          "sys_id": "9d7dc7e6eb1e5210aa82fab8bad0cda2",          "name": "Base dataset"        }],       "evaluation_metrics_report": "Sample Report",       "managed_by": {         "sys_id": "62826bf03710200044e0bfc8bcbe5df1",         "name": "Abel Tuter"       },       "created": "2024-12-11 18:23:09",       "updated": "2024-12-11 18:23:09"     },     "warnings": []   } }

AI Assets API - PUT /sn_ent/asset/ai_dataset/{sys_id}

Updates the data of a specific AI dataset asset record according to information that you provide in the request body.

Note: Provide only the parameter-value pairs for the specific data you want to update. This endpoint overwrites the data for any parameters that are sent in the request.

Use the AI Assets API - GET /sn_ent/asset/ai_dataset/{sys_id} method to retrieve an existing AI dataset record with a given ID. You can then use this PUT method to update values in the dataset using the same ID information.

URL format

Versioned URL: /api/sn_ent/{api_version}/asset/ai_dataset/{sys_id}

Default URL: /api/sn_ent/asset/ai_dataset/{sys_id}

Supported request parameters

Name

Description

api_version

Optional. Version of the endpoint to access. For example, v1 or v2. Only specify this value to use an endpoint version other than the latest. Data type: String

sys_id

Sys_id of the asset.Table: AI Dataset Asset [alm_ai_dataset_digital_asset]

Data type: String

|Name|Description| |----|-----------| |None| |

Name	Description
{object}	JavaScript `{ "acceptable_usage": "String", "base_datasets": [Array], "dataset_card": "String" "data_type": "String", "description": "String", "documentation": "String", "managed_by": "String", "name": "String", "provider": "String", "state": "String" "source": "String", "version": "String"}`
{object}.acceptable_usage	The purpose of the dataset or model, like for training or evaluation.Valid values: `1`: indicates training purpose `2`: indicates an evaluative purpose Data type: String (number)
{object}.base_datasets	Comma-separated list of base datasets needed to build this dataset. Accepts names or sys_ids of datasets present in AI Dataset Digital Asset [alm_ai_dataset_digital_asset] table.Data type: Array Text `“base_datasets”: ["String", “String"]`
{object}.data_type	Type of data present in the dataset. For example, `Text,Video,Image` or `1,2`.Data type: String
{object}.dataset_card	The data set card. A dataset_card is a metadata document that describes the contents, structure, and context of an AI dataset. It provides details like data sources, features, intended use, and any known limitations to ensure proper understanding and usage.Data type: String
{object}.description	Description of the associated AI Dataset Product Model.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
{object}.documentation	Documentation for the AI Dataset Product Model.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
{object}.managed_by	Sys_id or name value of the user in the Managed by field of the AI Dataset Product Model record. Tables: User [sys_user], AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
{object}.name	Required. Name of the associated record in the AI Dataset Product Model [cmdb_ai_dataset_product_model] table.Data type: String
{object}.provider	Required. Sys_id or name value of the Provider field of the Company [core_company] table record.Data type: String
{object}.source	Details about the source of the dataset.Valid values: Link to the source of the dataset. Details of the source in plain text. Data type: String Default: empty or null
{object}.state	State of the AI dataset asset.Valid values: In use Deployed Retired Development Unknown N/A Data type: String
{object}.version	Version number of the associated AI Dataset Product Model record. For example, `V2`.Data type: String

### Headers

Header	Description
Accept	Data format of the response body. Only supports application/json.
Content-Type	Data format of the request body. Only supports application/json.

Header	Description
None

Status codes

The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see REST API HTTP response codes.

Status code	Description
200	Successful. The request was successfully processed.
400	Bad Request. A bad request type or malformed request was detected.
404	Not found. The requested item wasn't found.
500	Internal server error. An unexpected error occurred while processing the request. The response contains additional information about the error.

Response body parameters (JSON or XML)

Name	Description
result	Details of the newly created AI Dataset asset.Data type: Object JavaScript `"result": { "acceptable_usage": {Object}, "base_datasets": [Array], "created": "String", "dataset_card": "String", "data_type": {Object}, "description": "String", "documentation": "String", "display_name": "String", "managed_by": {Object}, "name": "String", "provider": {Object}, "state": "Development", "source": "String", "sys_id": "String", "updated": "String", "version": "String",}`
result.acceptable_usage	Acceptable usage for the AI Dataset Asset record. Acceptable usage refers to how a dataset or model can be used, typically for training or evaluation purposes.Data type: Object CSS `"acceptable_usage": { "label": "String" "value": "String" }`
result.acceptable_usage.label	Display label of the acceptable usage value.Data type: String
result.acceptable_usage.value	The purpose of the dataset or model, like for training or evaluation.Valid values: `1`: indicates training purpose `2`: indicates an evaluative purpose Data type: String (number)
result.base_datasets	Comma-separated list of base datasets required to build the given dataset. Accepts the name or sys_id of a base data set in the AI Dataset Digital Asset [alm_ai_dataset_digital_asset] table.Data type: Array Text `"base_datasets": ["String", "String"]`
result.created	Date and time that the AI Dataset Asset record was created.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.data_type	The type of data present in the AI Dataset Asset record.Data type: Object CSS `"data_type": { "label": "String", "value": "String"}`
result.data_type.label	The display label for the data type value.Data type: String
result.data_type.value	Value of the dataset asset's data type.Data type: String
result.dataset_card	The data set card. A dataset_card is a metadata document that describes the contents, structure, and context of an AI dataset. It provides details like data sources, features, intended use, and any known limitations to ensure proper understanding and usage.Data type: String
result.description	Description of the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.display_name	Display name of the AI Dataset Asset record.Table: AI Dataset Digital Asset [alm_ai_dataset_digital_asset] Data type: String
result.documentation	Documentation of the associated AI Dataset Product Model [cmdb_ai_dataset_product_model] table record.Data type: String
result.managed_by	Details about the user who manages the AI Dataset Asset record.Data type: Object Text `"managed_by": [ { "name": "String", "sys_id": "String" }]`
result.managed_by.name	Name of the user who manages the AI Dataset Asset record.Table: User [user] Data type: String
result.managed_by.sys_id	Sys_id of the user who manages the AI Dataset Asset record.Table: User [user] Data type: String
result.name	Name of the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.provider	Provider of the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: Object Text `provider: { "name": "String", "sys_id": "String"}`
result.provider.name	Name of the provider.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.provider.sys_id	Sys_id of the provider in the associated AI Dataset Product Model record.Table: AI Dataset Product Model [cmdb_ai_dataset_product_model] Data type: String
result.source	Details about the source of AI dataset asset.Valid values: Link to the source of the dataset asset. Details (in plain text) of the source of the dataset asset. For example, the name of a product or website. Data type: String Default: empty or null
result.state	State of the AI Dataset Asset record.Possible values: In use Deployed Retired Development Unknown N/A Data type: String
result.sys_id	Sys_id of the AI Dataset Asset record.Table: AI Dataset Digital Asset [alm_ai_dataset_digital_asset] Data type: String
result.updated	Date and time that the AI Dataset Asset record was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.version	Version number of the associated AI Dataset Product Model record. For example, `V2`.Data type: String
result.warnings	Comma-separated list of warning messages that are present when creating the dataset. These warnings can be validation checks, such as when the sys_id of an optional parameter is invalid.Data type: Array Text `"warnings": ["String"]`

### cURL request

The following example shows how to update details in an AI dataset record with a given ID. The request body contains the parameter values to update.

Shell

curl -X PUT 'https://instance.servicenow.com/api/sn_ent/asset/ai_dataset/9833721b331e92101c9aca989d5c7bf0' \   -H 'Accept: application/json' \   -H 'Content-Type: application/json' \   -u 'username':'password' \   -d '{   "name": "Dataset One",   "description": "Description for dataset ",   "provider": "servicenow",   "version": "V1",   "state": 31,   “source”: “Source of dataset”   "documentation": "document",   “dataset_card”: “Dataset Card”,   “base_datasets”: [ “Dataset Two”, “Dataset Three”],   “data_type”: “1,2”,   “acceptable_usage”: “1,2”,   "managed_by": "abel.tuter" }'

Response body.

JSON

{   "result": {     "asset": {       "sys_id": "da8393eb40d25210f877b00c113d1fc1",       "display_name": "ServiceNow Closed Incidents",       "name": "Closed Incidents",       "description": "Incidents with resolution",       "documentation": "Sample Documentation",       "source": "incident table on servicenow instance",       "dataset_card": "Dataset Card",       "state": "Deployed",       "version": null,       "data_type": {         "value": "1",         "label": "Text"       },       "provider": {         "sys_id": "93d4ecfac0a8000b6294d71b733977fb",         "name": "ServiceNow"       },       "managed_by": {         "sys_id": "undefined",         "name": ""       },       "acceptable_usage": {         "value": "1",         "label": "Training"       },       "base_datasets": [],       "created": "2024-12-12 01:23:03",       "updated": "2024-12-12 01:23:03"     },     "warnings": []        } }

AI Assets API - PUT /sn_ent/asset/ai_prompt/{sys_id}

Updates the data of a specific AI prompt asset record according to information you provide in the request body.

URL format

Versioned URL: /api/sn_ent/{api_version}/asset/ai_prompt/{sys_id}

Default URL: /api/sn_ent/asset/ai_prompt/{sys_id}

Supported request parameters

Name

Description

api_version

Optional. Version of the endpoint to access. For example, v1 or v2. Only specify this value to use an endpoint version other than the latest. Data type: String

sys_id

Sys_id of the asset.Table: AI Prompt Asset [alm_ai_prompt_digital_asset]

Data type: String

|Name|Description| |----|-----------| |None| |

Name	Description
object	Required. Content of the AI Prompt asset to update.Data type: Object JavaScript `{ "ai_model": "String", "description": "String", "documentation": "String", "managed_by": "String" "name": "String", "prompt_info": "String", "provider": "String", "state": Number, "version": "String"}`
ai_model	Name of sys_id value of the AI Model field in the AI Model Digital Asset record.Table: AI Model Digital Asset [alm_ai_model_digital_asset] Data type: String Default: empty string
description	Description to give the AI Prompt Product Model.Table: AI Prompt Product Model [cmdb_ai_prompt_product_model] Data type: String Default: empty string
documentation	Documentation of the AI Prompt Product Model.Table: AI Prompt Product Model [cmdb_ai_prompt_product_model] Data type: String Default: empty string
managed_by	Name or sys_id of the user that manages the asset. Located in the Managed by field of the asset record.Tab;e: User [sys_user] Data type: String Default: empty string
name	Required. Name of the associated record in the AI Prompt Product Model [cmdb_ai_prompt_product_model] table.Data type: String Default: empty string
prompt_info	Prompt information for AI Prompt Asset.Data type: String Default: empty string
provider	Required. Provider name or sys_id of the Company table record.Table: Company [core_company] Data type: String
state	State to apply to the AI Model Asset. For example, Valid values: In use Deployed Retired Development Unknown N/A Data type: String
version	Version number to assign the AI Prompt Product Model. For example, `V2`.Data type: String Default: empty string

### Headers

Header	Description
Accept	Data format of the response body. Only supports application/json.
Content-Type	Data format of the request body. Only supports application/json.

Header	Description
None

Status codes

The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see REST API HTTP response codes.

Status code	Description
200	Successful. The request was successfully processed.
401	Unauthorized. The user credentials are incorrect or have not been passed.
404	Not found. Failed to fetch the asset with the given sys_id.
500	Internal server error. An unexpected error occurred while processing the request. The response contains additional information about the error.

Response body parameters

Name	Description
result	Text `result: { "asset": {Object}, "warnings": [Array]}`
result.asset	Details about the newly created entry.Data type: Object JavaScript `"asset": { "ai_model": Object, "created": "String", "description": "String", "display_name": "String", "documentation": "String", "managed_by": Object, "name": "String", "prompt_info": "String", "provider": Object, "state": "String", "sys_id": "String", "updated": "String", "version": "String", "warnings": [Array]}`
result.asset.ai_model	List of AI models in the AI System Asset table record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: Array Text `"ai_model": [ { "name": "String", "sys_id": "String" }]`
result.asset.ai_models.name	Name of the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.ai_models.sys_id	Sys_id of the AI System Digital Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.created	Date and time that the AI model asset was created.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.asset.description	Description of the associated AI System Product Model record. Table: AI System Product Model [cmdb_ai_system_product_model] Data type: String
result.asset.display_name	Display name of the AI System Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.documentation	Documentation of the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_ system_product_model] Data type: String
result.asset.managed_by	Details about the user who manages the asset.Data type: Object Text `"managed_by": [ { "name": "String", "sys_id": "String" }]`
result.asset.managed_by.name	Name of the user who manages the AI model asset record.Table: User [user] Data type: String
result.asset.managed_by.sys_id	Sys_id of the user who manages the AI model asset record.Table: User [user] Data type: String
result.asset.name	Name of the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_system_product_model] Data type: String
result.asset.prompt_info	Prompt Information of the AI Prompt Asset record.Data type: String
result.asset.provider	Value of the provider field in the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_ system_product_model] Data type: Object Text `provider: { "name": "String", "sys_id": "String"}`
result.asset.provider.name	Name of the provider in the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_ system_product_model] Data type: String
result.asset.provider.sys_id	Sys_id of the provider in the associated AI System Product Model record.Table: AI System Product Model [cmdb_ai_ system_product_model] Data type: String
result.asset.state	State of the AI Model Asset. Possible values: In use Deployed Retired Development Unknown N/A Data type: String
result.asset.sys_id	Sys_id of the AI System Asset record.Table: AI System Digital Asset [alm_ai_system_digital_asset] Data type: String
result.asset.updated	Date and time that the AI model asset was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.asset.version	Version number of the associated AI System Product Model record. For example, `V2`.Data type: String
result.warning	Comma-separated list of warning messages. These warnings can be validation checks, such as when the sys_id of an optional parameter is invalid.Data type: Array Text `"warnings": ["String"]`

### cURL request

The following example updates the data of the AI Prompt asset with the given sys_id according to the provided parameter values in the request body.

Shell

curl -X PUT 'https://instance.servicenow.com/api/sn_ent/asset/ai_prompt/9833721b331e92101c9aca989d5c7bf0' \  -H 'Accept: application/json' \  -H 'Content-Type: application/json' \  -u 'username':'password' \  -d '{  "name": "Incident Summarization prompt1",  "description": "Prompt for Incident Summarization",  "provider": "servicenow",  "version": "V1",  "state": 31,  "documentation": "Docuuu",  "ai_model": "mixtral-instruct",  "prompt_info": "Provide incident summary using short_decription, state, worknotes",  "managed_by": "abel.tuter" }'

Response body:

JSON

{  "result": {    "asset": {      "sys_id": "9833721b331e92101c9aca989d5c7bf0",      "display_name": "ServiceNow Incident Summarization prompt1 V1",      "name": "Incident Summarization prompt1",      "description": "Prompt for Incident Summarization",      "version": "V1",      "provider": {        "sys_id": "93d4ecfac0a8000b6294d71b733977fb",        "name": "ServiceNow"      },      "documentation": "Docuuu",      "state": "Deployed",      "ai_model": {        "sys_id": "9d7dc7e6eb1e5210aa82fab8bad0cda2",        "name": "mixtral-instruct"      },      "prompt_info": "Provide incident summary using short_decription, state, worknotes",      "managed_by": {        "sys_id": "62826bf03710200044e0bfc8bcbe5df1",        "name": "Abel Tuter"      },      "created": "2024-12-11 04:23:17",      "updated": "2024-12-11 04:23:17"    },    "warnings": []  }}

AI Assets API - PUT /sn_ent/asset/ai_system/{sys_id}

Updates the data of a specific AI system record according to information you provide in the request body.

Note: Provide only the parameter-value pairs for the specific data you want to update. This endpoint overwrites the data for any parameters that are sent in the request.

URL format

Versioned URL: /api/sn_ent/{api_version}/asset/ai_system/{sys_id}

Default URL: /api/sn_ent/asset/ai_system/{sys_id}

Supported request parameters

Name

Description

api_version

Optional. Version of the endpoint to access. For example, v1 or v2. Only specify this value to use an endpoint version other than the latest. Data type: String

sys_id

Sys_id of the asset.Table: AI System Digital Asset [alm_ai_system_digital_asset]

Data type: String

|Name|Description| |----|-----------| |None| |

Name	Description
object	Required. Details to update in the AI system.Data type: Object JavaScript `{ "name": "String", "description": "String", "provider": "String", "version": "String", "state": Number, "documentation": "String", "ai_models": "String", "ai_prompts": "Strings", "evaluation_datasets": [Array], "evaluation_metrics_report": "String", "managed_by": "String" }`
object.name	Required. Name for the AI System Product Model.Data type: String
object.description	Description for the AI System Product Model.Data type: String
object.provider	Required. Sys_id or name value of the Provider field of the Company [core_company] table record.Data type: String
object.state	State to apply to the AI System Asset. Valid values: In use Deployed Retired Development Unknown N/A Data type: String Default: empty string
object.version	Version for AI System Product Model. For example, `V2`.Data type: String Default: empty string
object.documentation	Documentation of the AI System Asset.Data type: String Default:
object.ai_models	Comma-separated list of names or sys_ids of the AI Model Digital Asset record, where each string represents an AI Model field value of an existing AI Model Digital Asset record.Table: AI Model Digital Asset [alm_ai_model_digital_asset] Data type: Array of Strings JavaScript `"ai_models": [{String", "String"}]` Default: empty string
object.evaluation_datasets	Comma-separated list of names or sys_ids of the AI datasets used for evaluating the AI System Asset record. Mostly applicable for models developed within an organization.Tables: AI Dataset Digital Asset [alm_ai_dataset_digital_asset], AI System Asset [AI System Digital Asset alm_ai_system_digital_asset] Data type: Array of Strings JavaScript `"evaluation_datasets": [{String", "String"}]` Default: empty string
object.evaluation_metrics_report	Reference to the evaluation results located within the text field of the AI system asset record. For example:``` Testing results: link to the result document Details: Accuracy: 85% Hallucination: 10% Eval Run 1: link Eval Run 2: link Text Table: AI System Digital Asset \[alm\_ai\_system\_digital\_asset\] Data type: String Default: empty string </td></tr><tr><td> object.managed\_by </td><td> Sys\_id or name of the 'Managed by' field of an existing User \[sys\_user\] table record.Data type: String Default: empty string </td></tr></tbody></table>### Headers The following request and response headers apply to this HTTP action only, or apply to this action in a distinct way. For a list of general headers used in the REST API, see [Supported REST API headers](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/api-reference/rest-api-explorer/c_RESTAPI.md). \|Header\|Description\|\|------\|-----------\|\|Accept\|Data format of the response body. Only supports application/json.\|\|Content-Type\|Data format of the request body. Only supports application/json.\| \|Header\|Description\|\|------\|-----------\|\|None\| \| ### Status codes The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see [REST API HTTP response codes](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/api-reference/rest-api-explorer/c_RESTAPI.md). \|Status code\|Description\|\|-----------\|-----------\|\|200\|Successful. The request was successfully processed.\|\|400\|Bad Request. A bad request type or malformed request was detected.\|\|404\|Not found. Failed to fetch the asset with the given sys\_id.\|\|500\|Internal server error. An unexpected error occurred while processing the request. The response contains additional information about the error.\| ### Response body parameters \(JSON or XML\) <table id="table_gdt_wkm_ydc"><thead><tr><th> Name </th><th> Description </th></tr></thead><tbody><tr><td> result </td><td> Results of the updated AI System Asset.Data type: Object "result": { "asset": Object, "warnings": [Array] } Text `</td></tr><tr><td> result.asset </td><td> Details about the newly created asset.Data type: Object` "asset": { "ai_model": [Array], "ai_prompts": [Array] "created": "String", "description": "String", "display_name": "String", "documentation": "String", "evaluation_datasets": Array, "evaluation_metrics_report": String, "managed_by": Object, "name": "String", "provider": Object, "state": "String", "sys_id": "String", "updated": "String", "version": "String", "warnings": [Array] } Text `</td></tr><tr><td> result.ai\_models </td><td> List of AI models in the AI System Digital Asset record.Table: AI System Digital Asset \[alm\_ai\_system\_digital\_asset\] Data type: Array` "ai_models": [ { "name": "String", "sys_id": "String" } ] Text `</td></tr><tr><td> result.ai\_models.name </td><td> Name of the AI System Digital Asset record.Table: AI System Digital Asset \[alm\_ai\_system\_digital\_asset\] Data type: String </td></tr><tr><td> result.ai\_models.sys\_id </td><td> Sys\_id of the AI System Digital Asset record.Table: AI System Digital Asset \[alm\_ai\_system\_digital\_asset\] Data type: String </td></tr><tr><td> result.ai\_prompts </td><td> List of AI Prompts in the AI System Asset record.Data type: Array` "ai_prompts": [ { "name": "String", "sys_id": "String" } ] Text </td></tr><tr><td> result.ai\_prompts.name </td><td> Name of the AI prompt.Data type: String </td></tr><tr><td> result.ai\_prompts.sys\_id </td><td> Sys\_id of the AI Prompt Digital Asset record.Table: AI Prompt Digital Asset \[alm\_ai\_prompt\_digital\_asset\] Data type: String </td></tr><tr><td> result.asset.created </td><td> Date and time that the AI model asset was created.Format: YYYY-MM-DD HH:mm:ss Data type: String </td></tr><tr><td> result.asset.description </td><td> Description of the associated AI System Product Model record. Table: AI System Product Model \[cmdb\_ai\_system\_product\_model\] Data type: String </td></tr><tr><td> result.asset.display\_name </td><td> Display name of the AI System Asset record.Table: AI System Digital Asset \[alm\_ai\_system\_digital\_asset\] Data type: String </td></tr><tr><td> result.asset.documentation </td><td> Documentation for the associated AI System Product Model record.Table: AI System Product Model \[cmdb\_ai\_ system\_product\_model\] Data type: String </td></tr><tr><td> result.asset.evaluation\_datasets </td><td> List of AI datasets used for evaluating the AI System Asset record. Mostly applicable for models developed within an organization.Tables: AI Dataset Digital Asset \[alm\_ai\_dataset\_digital\_asset\], AI System Asset \[alm\_ai\_system\_digital\_asset\] Data type: Array "evaluation_datasets": [ { "name": "String", "sys_id": "String" } ] Text </td></tr><tr><td> result.asset.evaluation\_datasets.name </td><td> Name of the AI Dataset Digital Asset.Table: AI Dataset Digital Asset \[alm\_ai\_dataset\_digital\_asset\] Data type: String </td></tr><tr><td> result.asset.evaluation\_datasets.sys\_id </td><td> Sys\_id of the AI Dataset Digital Asset record.Table: AI Dataset Digital Asset \[alm\_ai\_dataset\_digital\_asset\] Data type: String </td></tr><tr><td> result.asset.evaluation\_metrics\_report </td><td> Evaluation results of the AI system asset.Possible values: - Details \(in plain text\) outlining results- Links to specific results Data type: String </td></tr><tr><td> result.asset.managed\_by </td><td> Comma-separated list of sys\_ids or display names of AI datasets of the AI System Asset used for evaluating the model. Mostly applicable for models developed within an organization.Table: AI System Digital Asset \[alm\_ai\_system\_digital\_asset\] Data type: Object "managed_by": [ { "name": "String", "sys_id": "String" } ] Text </td></tr><tr><td> result.asset.managed\_by.name </td><td> Sys\_id of the user who manages the AI model asset record.Table: User \[user\] Data type: String </td></tr><tr><td> result.asset.managed\_by.sys\_id </td><td> Name of the user who manages the AI model asset record.Table: User \[user\] Data type: String </td></tr><tr><td> result.asset.name </td><td> Name of the associated AI System Product Model record.Table: AI System Product Model \[cmdb\_ai\_system\_product\_model\] Data type: String </td></tr><tr><td> result.asset.provider </td><td> Value of the Provider field in the associated AI System Product Model record.Table: AI System Product Model \[cmdb\_ai\_ system\_product\_model\] Data type: Object provider: { "name": "String", "sys_id": "String" } Text </td></tr><tr><td> result.asset.provider.name </td><td> Name of the provider in the associated AI System Product Model record.Table: System Product Model \[cmdb\_ai\_ system\_product\_model\] Data type: String </td></tr><tr><td> result.asset.provider.sys\_id </td><td> Sys\_id of the provider in the associated AI System Product Model record.Table: System Product Model \[cmdb\_ai\_ system\_product\_model\] Data type: String </td></tr><tr><td> result.asset.state </td><td> State of the AI Model Asset. Possible values: - In use- Deployed- Retired- Development- Unknown- N/A Data type: String </td></tr><tr><td> result.asset.sys\_id </td><td> Sys\_id of the AI System Asset record.Table: AI System Digital Asset \[alm\_ai\_system\_digital\_asset\] Data type: String </td></tr><tr><td> result.asset.updated </td><td> Date and time that the AI model asset was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String </td></tr><tr><td> result.asset.version </td><td> Version number of the associated AI System Product Model record. For example, `V2`.Data type: String </td></tr><tr><td> result.asset.warnings </td><td> Comma-separated list of warning messages. These warnings can be validation checks, such as when the sys\_id of an optional parameter is invalid.Data type: Array "warnings": ["String"] Text `</td></tr></tbody></table>### cURL request The following example updates the AI System with details to update in the request body.` curl -X PUT 'https://instance.servicenow.com/api/sn_ent/asset/ai_prompt/3b140397435a9210a63d00002fb8f2d7' \ -H 'Accept: application/json' \ -H 'Content-Type: application/json' \ -u 'username':'password' \ -d '{ "name": "Incident Summarization", "description": "Skill to summarize incident", "provider": "servicenow", "documentation": "Sample Documentation", "version": "V3", "state": 31, "ai_models": [ "llm_generic_small" ], "ai_prompts": [ "LLM Prompt" ], "evaluation_datasets": [ "Base dataset" ], "evaluation_metrics_report": "Sample Report", "managed_by": "abel.tutor" }' Text `Response body shows the results of the update that was applied to the given AI system sys\_id.` { "result": { "asset": { "sys_id": "3b140397435a9210a63d00002fb8f2d7", "display_name": "ServiceNow Incident Summarization V2", "name": "Incident Summarization", "description": "Skill to summarize incident", "version": "V3", "provider": { "sys_id": "93d4ecfac0a8000b6294d71b733977fb", "name": "ServiceNow" }, "documentation": "Sample Documentation", "state": "Deployed", "ai_models": [{ "sys_id": "9tgdc7e6eb1e5210aa82fab8bad0cda2", "name": "llm_generic_small" }], "ai_prompts": [{ "sys_id": "7d7dc7e6eb1e5210aa82fab8bad0cda2", "name": "LLM Prompt" }], "evaluation_datasets": [{ "sys_id": "9d7dc7e6eb1e5210aa82fab8bad0cda2", "name": "Base dataset" }], "evaluation_metrics_report": "Sample Report", "managed_by": { "sys_id": "a8f98bb0eb32010045e1a5115206fe3a", "name": "Abraham Lincoln" }, "created": "2024-12-11 19:07:13", "updated": "2024-12-11 19:07:42" }, "warnings": [] } } Text ## AI Assets API - PUT /sn\_ent/asset/ai\_model/\{sys\_id\} Updates the data of a specific AI model asset record according to information you provide in the request body. Note: Provide only the parameter-value pairs for specific data you want to update. This endpoint overwrites the data for all parameters that are sent in the request. ### URL format Versioned URL: `/api/sn_ent/{api_version}/asset/ai_model/{sys_id}` Default URL: `/api/sn_ent/asset/ai_model/{sys_id}` ### Supported request parameters <table class="rest_api_path_parameters"><thead><tr><th> Name </th><th> Description </th></tr></thead><tbody><tr><td> api\_version </td><td id="version-entry-RESTAPI"> Optional. Version of the endpoint to access. For example, `v1` or `v2`. Only specify this value to use an endpoint version other than the latest. Data type: String </td></tr><tr><td> sys\_id </td><td id="entry-ai-assetapi-promptID"> Sys\_id of the asset.Table: AI Prompt Asset \[alm\_ai\_prompt\_digital\_asset\] Data type: String </td></tr></tbody></table>\|Name\|Description\|\|----\|-----------\|\|None\| \| <table class="rest_api_request_body"><thead><tr><th> Name </th><th> Description </th></tr></thead><tbody><tr><td> object </td><td> Required. Details to update in the given AI model.Data type: Object { "base_model": "String", "context_window": "String", "deployment_guideline": "String", "description": "String", "documentation": "String", "evaluation_datasets": "String", "evaluation_metrics_report": "String", "managed_by": "String", "model_size_in_mb": "String", "model_weights_info": "String", "name": "String", "parameters_info": "String", "provider": "String", "required_infrastructure": "String", "state": Number, "supported_languages": "String", "training_datasets": "String", "training_procedure": "String", "version": "String" } Text `</td></tr><tr><td> base\_model </td><td> AI model that this model version was derived from.Note: Only applicable for models developed within the organization. Data type: Object` { "name": "String", "sys_id": "String" } Text Default: empty object </td></tr><tr><td> base\_model.name </td><td> Name of the AI model asset to model this AI model after.Table: AI Model Digital Asset alm\_ai\_model\_digital\_asset Data type: String </td></tr><tr><td> base\_model.sys\_id </td><td> Sys\_id of the AI model asset to model this AI model after.Table: AI Model Digital Asset alm\_ai\_model\_digital\_asset Data type: String </td></tr><tr><td> context\_window </td><td> Size of input sequences \(in other words, the number of tokens\) that the model can handle.Data type: Integer Default: 0 </td></tr><tr><td> deployment\_guideline </td><td> Instructions applicable for models developed and deployed within an organization.Data type: String Default: empty </td></tr><tr><td> description </td><td> Description to give the AI Model Product Model. Updated in table: AI Model Product Model \[cmdb\_ai\_model\_product\_model\] Data type: String Default: empty </td></tr><tr><td> documentation </td><td> Documentation of the AI Prompt Product Model record.Table: AI Prompt Product Model \[cmdb\_ai\_model\_product\_model\] Data type: String Default: empty </td></tr><tr><td> evaluation\_datasets </td><td> Comma-separated list of sys\_ids or display names of AI datasets of the AI Prompt Digital Asset used for evaluating the model. Mostly applicable for models developed within an organization.Tables: AI Dataset Digital Asset \[alm\_ai\_dataset\_digital\_asset\], AI Prompt Digital Asset \[alm\_ai\_prompt\_digital\_asset\] Data type: Array "evaluation_datasets": [ "String", "String" ] Text Valid values: - Name of the AI Dataset Digital Asset record.- Sys\_id of the AI Dataset Digital Asset record. Default: empty string </td></tr><tr><td> evaluation\_metrics\_report </td><td> Reference to the evaluation results located within the text field of the AI Dataset Asset record. For example:```Testing results: link to the result document Details:Accuracy: 85%Hallucination: 10%Eval Run 1: linkEval Run 2: link Table: AI Dataset Asset [alm_ai_dataset_digital_asset] Data type: String Default: empty string
managed_by	Sys_id or name of the user in the 'Managed by' field of the AI Dataset Asset record. Table: User [sys_user] Data type: String Default: empty
model_size_in_mb	Size of the model in MB. Mostly applicable for models developed and deployed within an organization.Data type: Number Default: empty
model_weights_info	Additional model information, if available. Mostly applicable for models developed within an organization.Data type: String Default: empty
name	Required. Name of the AI Model Product Model. Updated in table: AI Model Product Model [cmdb_ai_model_product_model] Data type: String
parameters_info	Number of parameters to give to the model.Data type: String Default: empty string
provider	Required. Sys_id or name value of the 'Provider' field of an existing Company [core_company] table record. Assigns this provider to the new AI model.Data type: String
required_infrastructure	Documentation of infrastructure needs for the model deployment. For example, details about the infrastructure stack and processing needs. Mostly applicable for models deployed within an organization.Data type: String Default: empty
source	Details about the source of who or what created the model.Valid values: Link to the source of the model. For example, `https://huggingface.co/mistralai/model1` or a link to Github. Details of the source in plain text. For example, `Microsoft Azure` Data type: String Default: empty or null
state	State to apply to the AI Model Asset.Valid values: In use Deployed Retired Development Unknown N/A Data type: String
supported_languages	Comma-separated list of languages that are supported by the AI Model.Data type: Array Text `"supported_languages": [ "String", "String"]` Valid values: Sys_id of the Language [sys_language] record Name of the Language [sys_language] record. For example, `"French", "English"` Default: empty string
training_datasets	Reference to 1+ associated datasets used for training the model. Comma-separated list of sys_ids or display names of the AI Dataset Asset [alm_ai_dataset_digital_asset] table. Mostly applicable for models developed within an organization.Data type: Array Text `"training_datasets": [ "String", "String"]` Default: empty string
training_procedure	Type of training to apply to the AI model.Valid values: `1`: Decision Trees `2`: Deep Neural Networks `3`: Linear Regression `4`: Logistic Regression `5`: Random Forest `6`: Supervised Learning `7`: Unsupervised Learning `8`: Reinforcement Learning `9`: Transfer Learning `10`: Semi-Supervised Learning `11`: Instruction Finetuning `12`: Supervised Finetuning Data type: String represented with a number (for example, `"3"`) Default: 1: Decision Trees
version	Version for AI Model Product Model. For example, `V2`.Data type: String Default: empty

### Headers

Header	Description
Accept	Data format of the response body. Supported types: application/json or application/xml. Default: application/json

|Header|Description| |------|-----------| |None| |

Status codes

The following status codes apply to this HTTP action. For a list of possible status codes used in the REST API, see REST API HTTP response codes.

Status code	Description
200	Successful. The request was successfully processed.

Response body parameters (JSON or XML)

Name	Description
result	Results of the AI model asset creation.Data type: Object JavaScript `"result": { "asset": Object, "warnings": [Array], }`
result.asset	Details about the created AI model asset.Data type: Object JavaScript "asset": { "base_model": Object, "context_window": String, "created": "String", "deployment_guideline": String, "description": "String", "display_name": "String", "documentation": "String", "evaluation_datasets": Array, "evaluation_metrics_report": String, "managed_by": Object, "model_size_in_mb": String, "name": "String", "parameters_info": "String", "provider": Object, "required_infrastructure": String, "state": "String", "source": String, "supported_languages": Array, "sys_id": "String", "training_datasets": Array, "training_procedure": String, "updated": "String", "version": "String"}
result.asset.base_model	Information about the AI model asset from which this version derived from.Data type: Object Text `"base_model": { "name": "String", "sys_id": "String"}`
result.asset.base_model.name	Display name of AI model asset.Table: AI Model Digital Asset [alm_ai_model_digital_asset] Data type: String
result.asset.base_model.sys_id	Sys_id of AI model asset.Table: AI Model Digital Asset [alm_ai_model_digital_asset] Data type: String
result.asset.context_window	Size of input sequences that the model can handle. In other words, the number of tokens.Data type: String represented with a number. For example, `"6000"`.
result.asset.created	Date and time that the AI model asset was created.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.asset.deployment_guideline	Instructions applicable for models developed and deployed within an organization.Data type: String
result.asset.description	Description of the associated AI Model Product Model record.Table: AI Model Product Model [cmdb_ai_model_product_model] Data type: String
result.asset.display_name	Display name of the AI Model Asset record.Table: AI Model Digital Asset [alm_ai_model_digital_asset] (display_name field) Data type: String
result.asset.documentation	Documentation of the associated AI Model Product Model record.Table: AI Model Product Model [cmdb_ai_model_product_model] Data type: String
result.asset.evaluation_datasets	AI datasets used for evaluating the model. Mostly applicable for models developed within an organization.Tables: AI Dataset Digital Asset [alm_ai_dataset_digital_asset], AI Model Digital Asset [alm_ai_model_digital_asset] Data type: Array Text `"evaluation_datasets": [ { "name": "String", "sys_id": "String" } ]`
result.evaluation_datasets.name	Name of the AI Dataset Digital Asset.Table: AI Model Digital Asset [alm_ai_model_digital_asset] Data type: String
result.evaluation_datasets.sys_id	Sys_id of the AI Model Digital Asset record.Table: AI Model Digital Asset [alm_ai_model_digital_asset] Data type: String
result.asset.evaluation_metrics_report	Reference to the evaluation results.Possible values: Details (in plain text) outlining results Links to specific results Data type: String
result.asset.managed_by	User that manages the AI model asset record.Data type: Object Text `" managed_by": { "name": "String", "sys_id": "String"}`
result.asset.managed_by.name	Name of the user who manages the AI model asset record.Table: User [user] Data type: String
result.asset.managed_by.sys_id	Sys_id of the user who manages the AI model asset record.Table: User [user] Data type: String
result.asset.model_size_in_mb	Size of the model in MB. Usually applicable for models developed and deployed within an organization.Data type: Number
result.asset.name	Name of the associated AI Model Product Model record.Table: AI Model Product Model [cmdb_ai_model_product_model] Data type: String
result.asset.provider	Provider of the associated AI Model Product Model record.Table: AI Model Product Model [cmdb_ai_model_product_model] Data type: Object Text `"provider": { "name": "String", "sys_id": "String"}`
result.asset.provider.name	Name of the the Company record that corresponds to the provider of the associated AI Model Product Model record.Tables: Company [core_company], AI Model Product Model [cmdb_ai_model_product_model] Data type: String
result.asset.provider.sys_id	Sys_id of the Company record that corresponds to the provider of the associated AI Model Product Model record.Tables: Company [core_company], AI Model Product Model [cmdb_ai_model_product_model] Data type: String
result.asset.source	Details about the source of the asset.Valid values: Link to the source of the model. For example, `https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1` or a link to Github. Details of the source in plain text. For example, `Microsoft Azure` Data type: String
result.asset.state	State of the AI Model Asset record.Possible values: In use Deployed Retired Development Unknown N/A Data type: String
result.asset.supported_languages.name	Name of the supported language.Table: Language [sys_language] Data type: String
result.asset.supported_languages.sys_id	Sys_id of the supported language.Table: Language [sys_language] Data type: String
result.asset.sys_id	Sys_id of the AI Model Asset record Table: AI Model Digital Asset [alm_ai_model_digital_asset] Data type: String
result.asset.training_datasets	Reference to 1+ associated data sets used for training the model.Data type: Array Text `"training_datasets": [ { "name": "String", "sys_id": "String" } ]`
result.asset.training_procedure	A numeric value indicating the type of AI training applied to the model.Possible values: `1`: Decision Trees `2`: Deep Neural Networks `3`: Linear Regression `4`: Logistic Regression `5`: Random Forest `6`: Supervised Learning `7`: Unsupervised Learning `8`: Reinforcement Learning `9`: Transfer Learning `10`: Semi-Supervised Learning `11`: Instruction Finetuning `12`: Supervised Finetuning Data type: String represented by a number
result.asset.updated	Date and time that the AI model asset was last updated.Format: YYYY-MM-DD HH:mm:ss Data type: String
result.asset.version	Version of the associated AI Model Product Model record.Table: AI Model Product Model [cmdb_ai_model_product_model table] Data type: String
result.required_infrastructure	Documentation of infrastructure needs for the model's deployment. For example, details about the infrastructure stack and processing needs.Data type: String
result.asset.parameters_info	Properties of the training data that learn during the learning process. For example: `7B` or `30B`.Data type: String
result.asset.supported_languages	Details about the languages supported by the AI model asset.Table: Language [sys_language] Data type: Object Text `"supported_languages": [ { "name": "String", "sys_id": "String" }]`
result.asset.supported_languages.name	Name of the language that is supported by the asset.Table: Language [sys_language] Data type: String
result.asset.supported_languages.sys_id	Sys_id of the language that is supported by the asset.Table: Language [sys_language] Data type: String
result.warnings	Comma-separated list of warning messages. These warnings can be validation checks, such as when the sysId of an optional parameter is invalid.Data type: Array Text `"warnings": ["String"]`

### cURL request

The following example shows how to update a given AI asset model using the PUT method.

Shell

curl -X PUT 'https://instance.servicenow.com/api/sn_ent/asset/ai_model/9833721b331e92101c9aca989d5c7bf0' \   -H 'Accept: application/json' \   -H 'Content-Type: application/json' \   -u 'username':'password' \ -d '{     "name": "Now LLM",     "description": "enables text-to-text like question answering and summarization",     "provider": "servicenow",     "documentation": "Now LLM V5 Documentation",     "version": "V8",     "parameters_info": "7B",     "supported_languages": [         "English",         "French"     ],     "model_size_in_mb": "87",     "deployment_guideline": "Deployed on ServiceNow infrastructure",     "source": "huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1",     "training_procedure": "2",     "context_window": "8000",     "state": "31",     "base_model": "Servicenow Now LLM V6",     "model_weights_info": "refer to weights and biases project",     "required_infrastructre": "GPUs needed: 1, GPU Type: A100",     "training_datasets": [         "Servicenow Open Incidents"     ],     "evaluation_datasets": [         "Servicenow Open Incidents"     ],     "evaluation_metrics_report": "Testing results: See files attached to this record",     "managed_by": "abel.tuter" }'

Response body.

JSON

 {   "result": {     "asset": {       "sys_id": "a438d170ff96da10c1fbffffffffffd5",       "display_name": "ServiceNow Now LLM V6",       "name": "Now LLM",       "description": "enables text-to-text like question answering and summarization",       "version": "V8",       "provider": {         "sys_id": "93d4ecfac0a8000b6294d71b733977fb",         "name": "ServiceNow"       },       "documentation": "Now LLM V5 Documentation",       "parameters_info": "7B",       "supported_languages": [         {           "sys_id": "914493a30f320010e96b0e4fef767e90",           "name": "English"         }       ],       "model_size_in_mb": "87",       "deployment_guideline": "Deployed on ServiceNow infrastructure",       "source": "https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1",       "training_procedure": "2",       "context_window": "8000",       "state": "Deployed",       "required_infrastructure": "undefined",       "base_model": {         "sys_id": "a438d170ff96da10c1fbffffffffffd5",         "name": "ServiceNow Now LLM V8"       },       "evaluation_datasets": [         {           "sys_id": "45cb45baff06d610c1fbffffffffffa9",           "name": "ServiceNow Open Incidents"         }       ],       "training_datasets": [         {           "sys_id": "45cb45baff06d610c1fbffffffffffa9",           "name": "ServiceNow Open Incidents"         }       ],       "evaluation_metrics_report": "Testing results: See files attached to this record",       "managed_by": {         "sys_id": "62826bf03710200044e0bfc8bcbe5df1",         "name": "Abel Tuter"       },       "created": "2024-12-03 16:50:53",       "updated": "2024-12-12 15:56:28"     },     "warnings": [       "Reference record 'French' not found for supported_languages in table sys_language"     ]   } }

title	AI Assets API
description	AI Assets API provides endpoints to retrieve, update, and create several types of AI assets, such as systems, data sets, prompts, and models.Retrieves the data of the specified AI dataset asset.Retrieves the data of the specified AI model asset.Retrieves the data of the specified AI prompt asset.Retrieves the data of the specified AI system asset.Creates a new AI dataset asset entry in the AI Dataset Digital Asset \[alm\_ai\_dataset\_digital\_asset\] and AI Dataset Product Model \[cmdb\_ai\_dataset\_product\_model\] according to details you provide in the request body.Creates a new AI prompt asset entry in the AI Prompt Digital Asset \[alm\_ai\_prompt\_digital\_asset\] and AI Prompt Product Model \[cmdb\_ai\_promt\_product\_model\] tables according to details you provide in the request body.Creates a new AI model asset entry in the AI Model Digital Asset \[alm\_ai\_model\_digital\_asset\] and AI Model Product Model \[cmdb\_ai\_model\_product\_model\] tables according to details you provide in the request body.Creates a new AI system asset entry in the AI System Digital Asset \[alm\_ai\_system\_digital\_asset\] and AI System Product Model \[cmdb\_ai\_system\_product\_model\] tables according to details you provide in the request body.Updates the data of a specific AI dataset asset record according to information that you provide in the request body.Updates the data of a specific AI prompt asset record according to information you provide in the request body.Updates the data of a specific AI system record according to information you provide in the request body.Updates the data of a specific AI model asset record according to information you provide in the request body.
locale	en-US
canonical_url	https://www.servicenow.com/docs/r/australia/api-reference/rest-apis/ai-assets-api.html
release	australia
product	REST APIs
classification	rest-apis
topic_type	concept
last_updated	2026-09-10
reading_time_minutes	83
breadcrumb	REST API referenceAPI referenceAPI implementation and reference