Update AI Space Settings
This documentation is for Kyvos 2026.6.x. To view documentation for other Kyvos versions, click here. |
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Overview
This API updates the AI settings in an AI Space.
Method: POST
Request URL: {kyvos-url}/rest/v2/ai-spaces/{name-or-id}/ai-settings
Authorization
API Key: sessionid
This method authenticates requests using a session ID which acts like an API key.
BasicAuth: HTTP Authorization Scheme
This method authenticates requests using a username and password sent in the HTTP Authorization header.
Headers
Header Name | Type | Description | Possible Values |
|---|---|---|---|
Content-Type (Required) | String | Specifies media type of request body. |
|
sessionid | String | The session ID is the token received from the login API. Note: Session ID is required for token-based authentication. | – |
Path Parameters
Parameter Name | Type | Description | Possible Values |
|---|---|---|---|
name-or-id (Required) | String | Name or ID of the AI Space whose AI settings need to be updated. Note: If the AI Space name is provided, then either folderName or folderId must also be provided. | – |
Query Parameters
Parameter Name | Type | Description | Possible Values |
|---|---|---|---|
by | String | Specifies whether the name-or-id parameter should be interpreted as a AI Space name or AI Space ID. If by=name, provide the AI Space name in the name-or-id parameter. If by=id, provide the AI Space ID in the name-or-id parameter. The default value is name. | – |
Request Body
Parameter Name | Type | Description | Possible Values |
|---|---|---|---|
folderName | String | Name of the folder containing the AI Space. If the AI Space is present in the root directory, specify [Root]. Note: If neither folderName nor folderId is provided, the AI Space will be treated as a root-level entity by default. | – |
folderId | String | ID of the folder containing the AI Space. | – |
aiSettingsJson (Required) | String | JSON string which is used for updating the AI settings in an AI Space. Note: An example JSON is provided under the Resources section below. | – |
Responses
200 Success
{
"response": {
"success": "Successfully updated Entity AI settings."
}
}Status Code | Description |
|---|---|
401 | Authorization information is missing or invalid. |
404 | No matching record found. |
412 | Precondition failed. Required parameter missing. |
500 | Internal Server Error |
Resources
Below is an example of JSON structure which can be used as a reference for the aiSettingsJson parameter in the request body.
{
"mandatoryRAGColumns": [],
"AI_SpaceObject": {
"businessContext": ""
},
"isElasticIndexExits": false,
"isDataSharingAllowed": true,
"columnMetadata": {
"userPrompt": ""
},
"defaultProperties": {
"VERTEX_AI_LLM": [
{
"semanticModelSection": "NLSummary",
"applicableForSemanticModel": true,
"defaultValue": "true",
"dataType": "TEXT",
"options": [
{
"display": "genai.lblYes",
"value": "true"
},
{
"display": "genai.lblNo",
"value": "false"
}
],
"description": "genai.descAllowSendingDatatoLLM",
"inputType": "SELECTLIST",
"label": "genai.lblAllowSendingDatatoLLM",
"inheritedValue": {
"Gemini-3-Flash": "true",
"Germini-3-Flash-Low": "true",
"Gemini-Pro-2-5": "true",
"Automation-Gemini-Pro2-5": "true"
},
"fullyQualifiedName": "allow_values_for_nlp_generation",
"key": "allow_values_for_nlp_generation"
},
{
"semanticModelSection": "NLSummary",
"applicableForSemanticModel": true,
"defaultValue": "5000",
"dataType": "TEXT",
"description": "genai.descMaxRowsSummary",
"inputType": "TEXTBOX",
"label": "genai.lblMaxRowsSummary",
"inheritedValue": {
"Gemini-3-Flash": "5000",
"Germini-3-Flash-Low": "5000",
"Gemini-Pro-2-5": "5000"
},
"fullyQualifiedName": "max_rows_summary",
"key": "max_rows_summary"
}
],
"AWS_BEDROCK_EMBEDDING": [
{
"semanticModelSection": "NLQuery",
"applicableForSemanticModel": true,
"defaultValue": "0.03",
"dataType": "TEXT",
"description": "genai.descSimilarityUpperThreshold",
"inputType": "TEXTBOX",
"label": "genai.lblSimilarityUpperThreshold",
"inheritedValue": {
"wd-snowflake": "0.03",
"AWS-Bedrock-Embedding": "0.03"
},
"fullyQualifiedName": "upper_threshold",
"key": "upper_threshold"
}
],
"OPENAI_EMBEDDING": [
{
"semanticModelSection": "NLQuery",
"applicableForSemanticModel": true,
"defaultValue": "0.6",
"dataType": "TEXT",
"description": "genai.descSimilarityUpperThreshold",
"inputType": "TEXTBOX",
"label": "genai.lblSimilarityUpperThreshold",
"inheritedValue": {
"OpenAI-Embedding": "0.6"
},
"fullyQualifiedName": "upper_threshold",
"key": "upper_threshold"
}
],
"OTHER_EMBEDDING": [
{
"semanticModelSection": "NLQuery",
"applicableForSemanticModel": true,
"defaultValue": "0.6",
"dataType": "TEXT",
"description": "genai.descSimilarityUpperThreshold",
"inputType": "TEXTBOX",
"label": "genai.lblSimilarityUpperThreshold",
"fullyQualifiedName": "upper_threshold",
"key": "upper_threshold"
}
],
"OPENAI_LLM": [
{
"semanticModelSection": "NLSummary",
"applicableForSemanticModel": true,
"defaultValue": "true",
"dataType": "TEXT",
"options": [
{
"display": "genai.lblYes",
"value": "true"
},
{
"display": "genai.lblNo",
"value": "false"
}
],
"description": "genai.descAllowSendingDatatoLLM",
"inputType": "SELECTLIST",
"label": "genai.lblAllowSendingDatatoLLM",
"inheritedValue": {
"OpenAI-5-Mini": "true",
"Open-AI": "true"
},
"fullyQualifiedName": "allow_values_for_nlp_generation",
"key": "allow_values_for_nlp_generation"
},
{
"semanticModelSection": "NLSummary",
"applicableForSemanticModel": true,
"defaultValue": "5000",
"dataType": "TEXT",
"description": "genai.descMaxRowsSummary",
"inputType": "TEXTBOX",
"label": "genai.lblMaxRowsSummary",
"inheritedValue": {
"OpenAI-5-Mini": "5000",
"Open-AI": "5000"
},
"fullyQualifiedName": "max_rows_summary",
"key": "max_rows_summary"
}
]
},
"Calculations": {
"connection": {
"mode": "",
"name": ""
}
},
"NLQuerying": {
"queryPrompt": " Generate accurate SQL queries by following business rules, SQL syntax, joins, aggregations, and date-handling guidelines. Ensure the query is optimized and compatible with the semantic model.",
"userPrompt": "",
"connection": {
"mode": "",
"name": ""
},
"embedding": {
"mode": "MANUAL",
"name": "AWS-Bedrock-Embedding"
}
},
"NLSummary": {
"connection": {
"mode": "",
"name": ""
},
"summaryPrompt": "Generate a clear and concise summary of the query results while preserving Markdown formatting. Follow cumulative data rules and accurately describe trends, comparisons, and insights."
},
"properties": [],
"metadataGenConfig": {
"connection": {
"mode": "",
"name": ""
}
},
"SMObject": {
"userPrompt": "",
"businessContext": ""
}
}