DataRobot APIのリソース > APIリファレンスドキュメント > DataRobot REST API > Custom Model Vector Databases (GenAI)

Custom Model Vector Databases (GenAI)¶

This page outlines the operations, endpoints, parameters, and example requests and responses for the Custom Model Vector Databases (GenAI).

GET /api/v2/genai/customModelVectorDatabaseValidations/¶

List custom model vector database validations.

Code samples¶

# You can also use wget
curl -X GET https://app.datarobot.com/api/v2/genai/customModelVectorDatabaseValidations/ \
  -H "Accept: application/json" \
  -H "Authorization: Bearer {access-token}"

Parameters

Name	In	Type	Required	Description
useCaseId	query	any	false	Only retrieve the custom model vector database validations associated with these use case IDs.
playgroundId	query	string	false	Only retrieve the custom model vector database validations associated with this playground ID.
offset	query	integer	false	Skip the specified number of values.
limit	query	integer	false	Retrieve only the specified number of values.
search	query	any	false	Only retrieve the custom model vector database validations matching the search query.
sort	query	any	false	Apply this sort order to the results. Valid options are "name", "deploymentName", "userName", "creationDate". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate.
completedOnly	query	boolean	false	If `true`, only retrieve the completed custom model vector database validations. The default is `false`.
deploymentId	query	string	false	Only retrieve the custom model vector database validations associated with this deployment ID.
modelId	query	string	false	Only retrieve the custom model vector database validations associated with this model ID.
promptColumnName	query	any	false	Only retrieve the custom model vector database validations where the custom model uses this column name for prompt input.
targetColumnName	query	any	false	Only retrieve the custom model vector database validations where the custom model uses this column name for prediction output.

Example responses¶

200 Response

{
  "count": 0,
  "data": [
    {
      "creationDate": "2019-08-24T14:15:22Z",
      "deploymentAccessData": {
        "authorizationHeader": "[REDACTED]",
        "datarobotKey": "string",
        "inputType": "CSV",
        "modelType": "TEXT_GENERATION",
        "predictionApiUrl": "string"
      },
      "deploymentId": "string",
      "deploymentName": "string",
      "errorMessage": "Unknown vector database error occurred",
      "id": "string",
      "modelId": "string",
      "name": "string",
      "predictionTimeout": 0,
      "promptColumnName": "string",
      "targetColumnName": "string",
      "tenantId": "string",
      "useCaseId": "string",
      "userId": "string",
      "userName": "string",
      "validationStatus": "TESTING"
    }
  ],
  "next": "string",
  "previous": "string",
  "totalCount": 0
}

Responses¶

Status	Meaning	Description	Schema
200	OK	Custom model vector database validations successfully retrieved.	ListCustomModelVectorDatabaseValidationsResponse
422	Unprocessable Entity	Validation Error	HTTPValidationErrorResponse

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

POST /api/v2/genai/customModelVectorDatabaseValidations/¶

Validate a vector database hosted in a custom model deployment for use in the playground.

Code samples¶

# You can also use wget
curl -X POST https://app.datarobot.com/api/v2/genai/customModelVectorDatabaseValidations/ \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer {access-token}"

Body parameter¶

{
  "deploymentId": "string",
  "modelId": "string",
  "name": "Untitled",
  "predictionTimeout": 300,
  "promptColumnName": "string",
  "targetColumnName": "string",
  "useCaseId": "string"
}

Parameters

Name	In	Type	Required	Description
body	body	CreateCustomModelValidationRequest	true	none

Example responses¶

202 Response

{
  "creationDate": "2019-08-24T14:15:22Z",
  "deploymentAccessData": {
    "authorizationHeader": "[REDACTED]",
    "datarobotKey": "string",
    "inputType": "CSV",
    "modelType": "TEXT_GENERATION",
    "predictionApiUrl": "string"
  },
  "deploymentId": "string",
  "deploymentName": "string",
  "errorMessage": "Unknown vector database error occurred",
  "id": "string",
  "modelId": "string",
  "name": "string",
  "predictionTimeout": 0,
  "promptColumnName": "string",
  "targetColumnName": "string",
  "tenantId": "string",
  "useCaseId": "string",
  "userId": "string",
  "userName": "string",
  "validationStatus": "TESTING"
}

Responses¶

Status	Meaning	Description	Schema
202	Accepted	Custom model vector database validation job successfully accepted. Follow the `Location` header to poll for job execution status.	CustomModelVectorDatabaseValidationResponse
422	Unprocessable Entity	Validation Error	HTTPValidationErrorResponse

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

DELETE /api/v2/genai/customModelVectorDatabaseValidations/{validationId}/¶

Delete an existing custom model vector database validation.

Code samples¶

# You can also use wget
curl -X DELETE https://app.datarobot.com/api/v2/genai/customModelVectorDatabaseValidations/{validationId}/ \
  -H "Accept: application/json" \
  -H "Authorization: Bearer {access-token}"

Parameters

Name	In	Type	Required	Description
validationId	path	string	true	The ID of the custom model vector database validation to delete.

Example responses¶

422 Response

{
  "detail": [
    {
      "loc": [
        "string"
      ],
      "msg": "string",
      "type": "string"
    }
  ]
}

Responses¶

Status	Meaning	Description	Schema
204	No Content	Successfully deleted custom model vector database validation	None
422	Unprocessable Entity	Validation Error	HTTPValidationErrorResponse

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

GET /api/v2/genai/customModelVectorDatabaseValidations/{validationId}/¶

Retrieve the status of validating a custom model vector database.

Code samples¶

# You can also use wget
curl -X GET https://app.datarobot.com/api/v2/genai/customModelVectorDatabaseValidations/{validationId}/ \
  -H "Accept: application/json" \
  -H "Authorization: Bearer {access-token}"

Parameters

Name	In	Type	Required	Description
validationId	path	string	true	The ID of the custom model vector database validation to retrieve.

Example responses¶

200 Response

{
  "creationDate": "2019-08-24T14:15:22Z",
  "deploymentAccessData": {
    "authorizationHeader": "[REDACTED]",
    "datarobotKey": "string",
    "inputType": "CSV",
    "modelType": "TEXT_GENERATION",
    "predictionApiUrl": "string"
  },
  "deploymentId": "string",
  "deploymentName": "string",
  "errorMessage": "Unknown vector database error occurred",
  "id": "string",
  "modelId": "string",
  "name": "string",
  "predictionTimeout": 0,
  "promptColumnName": "string",
  "targetColumnName": "string",
  "tenantId": "string",
  "useCaseId": "string",
  "userId": "string",
  "userName": "string",
  "validationStatus": "TESTING"
}

Responses¶

Status	Meaning	Description	Schema
200	OK	Custom model vector database validation status successfully retrieved.	CustomModelVectorDatabaseValidationResponse
422	Unprocessable Entity	Validation Error	HTTPValidationErrorResponse

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

PATCH /api/v2/genai/customModelVectorDatabaseValidations/{validationId}/¶

Edit an existing custom model vector database validation.

Code samples¶

# You can also use wget
curl -X PATCH https://app.datarobot.com/api/v2/genai/customModelVectorDatabaseValidations/{validationId}/ \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer {access-token}"

Body parameter¶

{
  "deploymentId": "string",
  "modelId": "string",
  "name": "string",
  "predictionTimeout": 1,
  "promptColumnName": "string",
  "targetColumnName": "string"
}

Parameters

Name	In	Type	Required	Description
validationId	path	string	true	The ID of the custom model vector database validation to edit.
body	body	EditCustomModelValidationRequest	true	none

Example responses¶

200 Response

{
  "creationDate": "2019-08-24T14:15:22Z",
  "deploymentAccessData": {
    "authorizationHeader": "[REDACTED]",
    "datarobotKey": "string",
    "inputType": "CSV",
    "modelType": "TEXT_GENERATION",
    "predictionApiUrl": "string"
  },
  "deploymentId": "string",
  "deploymentName": "string",
  "errorMessage": "Unknown vector database error occurred",
  "id": "string",
  "modelId": "string",
  "name": "string",
  "predictionTimeout": 0,
  "promptColumnName": "string",
  "targetColumnName": "string",
  "tenantId": "string",
  "useCaseId": "string",
  "userId": "string",
  "userName": "string",
  "validationStatus": "TESTING"
}

Responses¶

Status	Meaning	Description	Schema
200	OK	Custom model vector database validation successfully updated.	CustomModelVectorDatabaseValidationResponse
422	Unprocessable Entity	Validation Error	HTTPValidationErrorResponse

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

POST /api/v2/genai/customModelVectorDatabaseValidations/{validationId}/revalidate/¶

Revalidate an existing custom model vector database.

Code samples¶

# You can also use wget
curl -X POST https://app.datarobot.com/api/v2/genai/customModelVectorDatabaseValidations/{validationId}/revalidate/ \
  -H "Accept: application/json" \
  -H "Authorization: Bearer {access-token}"

Parameters

Name	In	Type	Required	Description
validationId	path	string	true	The ID of the custom model vector database validation to revalidate.

Example responses¶

200 Response

{
  "creationDate": "2019-08-24T14:15:22Z",
  "deploymentAccessData": {
    "authorizationHeader": "[REDACTED]",
    "datarobotKey": "string",
    "inputType": "CSV",
    "modelType": "TEXT_GENERATION",
    "predictionApiUrl": "string"
  },
  "deploymentId": "string",
  "deploymentName": "string",
  "errorMessage": "Unknown vector database error occurred",
  "id": "string",
  "modelId": "string",
  "name": "string",
  "predictionTimeout": 0,
  "promptColumnName": "string",
  "targetColumnName": "string",
  "tenantId": "string",
  "useCaseId": "string",
  "userId": "string",
  "userName": "string",
  "validationStatus": "TESTING"
}

Responses¶

Status	Meaning	Description	Schema
200	OK	Custom model vector database successfully revalidated.	CustomModelVectorDatabaseValidationResponse
422	Unprocessable Entity	Validation Error	HTTPValidationErrorResponse

To perform this operation, you must be authenticated by means of one of the following methods:

BearerAuth

Schemas¶

CreateCustomModelValidationRequest

{
  "deploymentId": "string",
  "modelId": "string",
  "name": "Untitled",
  "predictionTimeout": 300,
  "promptColumnName": "string",
  "targetColumnName": "string",
  "useCaseId": "string"
}

CreateCustomModelValidationRequest

Properties¶

Name	Type	Required	Restrictions	Description
deploymentId	string	true		The ID of the custom model deployment.
modelId	string¦null	false		The ID of the model used in the deployment.
name	string	false	maxLength: 5000	The name to use for the validated custom model.
predictionTimeout	integer	false	maximum: 1200 minimum: 1	The timeout in seconds for the prediction when validating a custom model. Defaults to 300.
promptColumnName	string	true	maxLength: 5000	The name of the column the custom model uses for prompt text input.
targetColumnName	string	true	maxLength: 5000	The name of the column the custom model uses for prediction output.
useCaseId	string¦null	false		The ID of the use case to associate with the validated custom model.

CustomModelValidationStatus

"TESTING"

CustomModelValidationStatus

Properties¶

Name	Type	Required	Restrictions	Description
CustomModelValidationStatus	string	false		Status of custom model validation.

Enumerated Values¶

Property	Value
CustomModelValidationStatus	[`TESTING`, `PASSED`, `FAILED`]

CustomModelVectorDatabaseValidationResponse

{
  "creationDate": "2019-08-24T14:15:22Z",
  "deploymentAccessData": {
    "authorizationHeader": "[REDACTED]",
    "datarobotKey": "string",
    "inputType": "CSV",
    "modelType": "TEXT_GENERATION",
    "predictionApiUrl": "string"
  },
  "deploymentId": "string",
  "deploymentName": "string",
  "errorMessage": "Unknown vector database error occurred",
  "id": "string",
  "modelId": "string",
  "name": "string",
  "predictionTimeout": 0,
  "promptColumnName": "string",
  "targetColumnName": "string",
  "tenantId": "string",
  "useCaseId": "string",
  "userId": "string",
  "userName": "string",
  "validationStatus": "TESTING"
}

CustomModelVectorDatabaseValidationResponse

Properties¶

Name	Type	Required	Description
creationDate	string(date-time)	true	The creation date of the custom model validation (ISO 8601 formatted).
deploymentAccessData	DeploymentAccessDataApiFormatted¦null	true	The parameters used for accessing the deployment.
deploymentId	string	true	The ID of the custom model deployment.
deploymentName	string¦null	false	The name of the custom model deployment.
errorMessage	VectorDatabaseErrorMessages¦null	true	The error message associated with the validation error (if the validation failed).
id	string	true	The ID of the custom model validation.
modelId	string	true	The ID of the model used in the deployment.
name	string	true	The name of the validated custom model.
predictionTimeout	integer	true	The timeout in seconds for the prediction API used in this custom model validation.
promptColumnName	string	true	The name of the column the custom model uses for prompt text input.
targetColumnName	string	true	The name of the column the custom model uses for prediction output.
tenantId	string(uuid4)	true	The ID of the tenant the custom model validation belongs to.
useCaseId	string¦null	true	The ID of the use case associated with the validated custom model.
userId	string	true	The ID of the user that created this custom model validation.
userName	string¦null	false	The name of the user that created this custom model validation.
validationStatus	CustomModelValidationStatus	true	The status of the custom model validation.

DeploymentAccessDataApiFormatted

{
  "authorizationHeader": "[REDACTED]",
  "datarobotKey": "string",
  "inputType": "CSV",
  "modelType": "TEXT_GENERATION",
  "predictionApiUrl": "string"
}

DeploymentAccessDataApiFormatted

Properties¶

Name	Type	Required	Description
authorizationHeader	string	false	The `Authorization` header to use for the deployment.
datarobotKey	string¦null	true	The server key associated with the prediction API.
inputType	DeploymentInputType	true	The format of the input data.
modelType	SupportedDeploymentType	true	The type of the target output the deployment produces.
predictionApiUrl	string	true	The URL of the deployment's prediction API.

DeploymentInputType

"CSV"

DeploymentInputType

Properties¶

Name	Type	Required	Restrictions	Description
DeploymentInputType	string	false		The format of the input data submitted to a DataRobot deployment.

Enumerated Values¶

Property	Value
DeploymentInputType	[`CSV`, `JSON`]

EditCustomModelValidationRequest

{
  "deploymentId": "string",
  "modelId": "string",
  "name": "string",
  "predictionTimeout": 1,
  "promptColumnName": "string",
  "targetColumnName": "string"
}

EditCustomModelValidationRequest

Properties¶

Name	Type	Required	Restrictions	Description
deploymentId	string¦null	false		If specified, changes the ID of the deployment associated with this custom model validation.
modelId	string¦null	false		If specified, changes the ID of the model associated with this custom model validation.
name	string¦null	false	maxLength: 5000 minLength: 1 minLength: 1	If specified, renames the custom model validation to this value.
predictionTimeout	integer¦null	false	maximum: 1200 minimum: 1	If specified, sets the timeout in seconds for the prediction when validating a custom model.
promptColumnName	string¦null	false	maxLength: 5000	If specified, changes the name of the column that will be used to format the prompt text input for the custom model deployment.
targetColumnName	string¦null	false	maxLength: 5000	If specified, changes the name of the column that will be used to extract the prediction response from the custom model deployment.

HTTPValidationErrorResponse

{
  "detail": [
    {
      "loc": [
        "string"
      ],
      "msg": "string",
      "type": "string"
    }
  ]
}

HTTPValidationErrorResponse

Properties¶

Name	Type	Required	Restrictions	Description
detail	[ValidationError]	false		none

ListCustomModelValidationSortQueryParam

"name"

ListCustomModelValidationSortQueryParam

Properties¶

Name	Type	Required	Restrictions	Description
ListCustomModelValidationSortQueryParam	string	false		Sort order values for listing custom model validations.

Enumerated Values¶

Property	Value
ListCustomModelValidationSortQueryParam	[`name`, `-name`, `deploymentName`, `-deploymentName`, `userName`, `-userName`, `creationDate`, `-creationDate`]

ListCustomModelVectorDatabaseValidationsResponse

{
  "count": 0,
  "data": [
    {
      "creationDate": "2019-08-24T14:15:22Z",
      "deploymentAccessData": {
        "authorizationHeader": "[REDACTED]",
        "datarobotKey": "string",
        "inputType": "CSV",
        "modelType": "TEXT_GENERATION",
        "predictionApiUrl": "string"
      },
      "deploymentId": "string",
      "deploymentName": "string",
      "errorMessage": "Unknown vector database error occurred",
      "id": "string",
      "modelId": "string",
      "name": "string",
      "predictionTimeout": 0,
      "promptColumnName": "string",
      "targetColumnName": "string",
      "tenantId": "string",
      "useCaseId": "string",
      "userId": "string",
      "userName": "string",
      "validationStatus": "TESTING"
    }
  ],
  "next": "string",
  "previous": "string",
  "totalCount": 0
}

ListCustomModelVectorDatabaseValidationsResponse

Properties¶

Name	Type	Required	Description
count	integer	true	The number of records on this page.
data	[CustomModelVectorDatabaseValidationResponse]	true	The list of records.
next	string¦null	true	The URL to the next page, or `null` if there is no such page.
previous	string¦null	true	The URL to the previous page, or `null` if there is no such page.
totalCount	integer	true	The total number of records.

SupportedDeploymentType

"TEXT_GENERATION"

SupportedDeploymentType

Properties¶

Name	Type	Required	Restrictions	Description
SupportedDeploymentType	string	false		The type of the target output a DataRobot deployment produces.

Enumerated Values¶

Property	Value
SupportedDeploymentType	[`TEXT_GENERATION`, `UNSTRUCTURED`, `REGRESSION`, `MULTICLASS`, `BINARY`, `NOT_SUPPORTED`]

ValidationError

{
  "loc": [
    "string"
  ],
  "msg": "string",
  "type": "string"
}

ValidationError

Properties¶

Name	Type	Required	Restrictions	Description
loc	[anyOf]	true		none

anyOf

Name	Type	Required	Restrictions	Description
» anonymous	string	false		none

or

Name	Type	Required	Restrictions	Description
» anonymous	integer	false		none

continued

Name	Type	Required	Restrictions	Description
msg	string	true		none
type	string	true		none

VectorDatabaseErrorMessages

"Unknown vector database error occurred"

VectorDatabaseErrorMessages

Properties¶

Name	Type	Required	Restrictions	Description
VectorDatabaseErrorMessages	string	false		Error messages for vector database errors.

Enumerated Values¶

Property Value

VectorDatabaseErrorMessages [Unknown vector database error occurred, A vector database prediction timeout error occurred., Downloading or finding embedding model weights failed, Loading dataset failed, All loaded documents are empty or contain no text, Raw loaded text dataset size exceeds the limit for the chosen embedding model, Splitting documents into text chunks failed, Generating embeddings from text chunks failed, Creating an index from embeddings failed, Storing vector database assets failed, Calcluating the size of stored vector database assets failed, API token was deleted or revoked, Document retrieval failed, Vector database was deleted, Loading the embedding model failed, External vector database connection is not available, External vector database deployment has been deleted, No access to external vector database deployment, Only unstructured deployment types are supported, An API token is required but was not provided in the request, Deployment prediction server did not accept the request, Deployment prediction does not comply with the expected format, API token was deleted or revoked, External embedding model connection is not available, External embedding deployment has been deleted, No access to external embedding deployment, Only unstructured deployment types are supported, An API token is required but was not provided in the request, Deployment prediction server did not accept the request, Deployment prediction does not comply with the expected format, Worker process was unexpectedly terminated]

更新しました May 14, 2024

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