Skip to content

On-premise users: click in-app to access the full platform documentation for your version of DataRobot.

DataRobot API resources > API reference documentation > DataRobot REST API > Generative AI > Vector Databases

Vector Databases (GenAI)

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

GET /api/v2/genai/vectorDatabases/

List vector databases.

Code samples

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

Parameters

Name In Type Required Description
useCaseId query any false Only retrieve the vector databases linked to these use case IDs.
playgroundId query string false Only retrieve the vector databases linked to this playground ID.
familyId query string false Only retrieve the vector databases linked to this family ID.
parentsOnly query boolean false If true, only retrieve (root) parent vector databases. The default is false.
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 vector databases with names matching the search query.
sort query any false Apply this sort order to the results. Valid options are "name", "creationDate", "creationUserId", "embeddingModel", "datasetId", "chunkingMethod", "chunksCount", "size", "userName", "datasetName", "playgroundsCount", "source". 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 vector databases that have finished building. The default is false.

Example responses

200 Response

{
  "count": 0,
  "data": [
    {
      "addedDatasetIds": [
        "string"
      ],
      "addedDatasetNames": [
        "string"
      ],
      "chunkOverlapPercentage": 0,
      "chunkSize": 0,
      "chunkingMethod": "recursive",
      "chunksCount": 0,
      "creationDate": "2019-08-24T14:15:22Z",
      "creationUserId": "string",
      "datasetId": "string",
      "datasetName": "string",
      "embeddingModel": "intfloat/e5-large-v2",
      "embeddingValidationId": "string",
      "errorMessage": "Unknown vector database error occurred.",
      "errorResolution": "An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance.",
      "executionStatus": "NEW",
      "familyId": "string",
      "id": "string",
      "isSeparatorRegex": true,
      "lastUpdateDate": "2019-08-24T14:15:22Z",
      "metadataColumns": [
        "string"
      ],
      "name": "string",
      "organizationId": "string",
      "parentId": "string",
      "percentage": 0,
      "playgroundsCount": 0,
      "separators": [
        null
      ],
      "size": 0,
      "source": "DataRobot",
      "tenantId": "string",
      "useCaseId": "string",
      "userName": "string",
      "validationId": "string",
      "version": 0
    }
  ],
  "next": "string",
  "previous": "string",
  "totalCount": 0
}

Responses

Status Meaning Description Schema
200 OK Vector databases successfully retrieved. ListVectorDatabasesResponse
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/vectorDatabases/

Create a new vector database.

Code samples

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

Body parameter

{
  "chunkingParameters": {
    "chunkOverlapPercentage": 50,
    "chunkSize": 0,
    "chunkingMethod": "recursive",
    "embeddingModel": "intfloat/e5-large-v2",
    "embeddingValidationId": "string",
    "isSeparatorRegex": false,
    "separators": [
      "string"
    ]
  },
  "datasetId": "string",
  "name": "string",
  "parentVectorDatabaseId": "string",
  "updateDeployments": false,
  "updateLlmBlueprints": false,
  "useCaseId": "string"
}

Parameters

Name In Type Required Description
body body CreateVectorDatabaseRequest true none

Example responses

202 Response

{
  "addedDatasetIds": [
    "string"
  ],
  "addedDatasetNames": [
    "string"
  ],
  "chunkOverlapPercentage": 0,
  "chunkSize": 0,
  "chunkingMethod": "recursive",
  "chunksCount": 0,
  "creationDate": "2019-08-24T14:15:22Z",
  "creationUserId": "string",
  "datasetId": "string",
  "datasetName": "string",
  "embeddingModel": "intfloat/e5-large-v2",
  "embeddingValidationId": "string",
  "errorMessage": "Unknown vector database error occurred.",
  "errorResolution": "An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance.",
  "executionStatus": "NEW",
  "familyId": "string",
  "id": "string",
  "isSeparatorRegex": true,
  "lastUpdateDate": "2019-08-24T14:15:22Z",
  "metadataColumns": [
    "string"
  ],
  "name": "string",
  "organizationId": "string",
  "parentId": "string",
  "percentage": 0,
  "playgroundsCount": 0,
  "separators": [
    null
  ],
  "size": 0,
  "source": "DataRobot",
  "tenantId": "string",
  "useCaseId": "string",
  "userName": "string",
  "validationId": "string",
  "version": 0
}

Responses

Status Meaning Description Schema
202 Accepted Vector database creation job successfully accepted. Follow the Location header to poll for job execution status. VectorDatabaseResponse
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/vectorDatabases/fromCustomModelDeployment/

Create a new vector database from a custom model deployment.

Code samples

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

Body parameter

{
  "name": "string",
  "useCaseId": "string",
  "validationId": "string"
}

Parameters

Name In Type Required Description
body body any true none

Example responses

201 Response

{
  "addedDatasetIds": [
    "string"
  ],
  "addedDatasetNames": [
    "string"
  ],
  "chunkOverlapPercentage": 0,
  "chunkSize": 0,
  "chunkingMethod": "recursive",
  "chunksCount": 0,
  "creationDate": "2019-08-24T14:15:22Z",
  "creationUserId": "string",
  "datasetId": "string",
  "datasetName": "string",
  "embeddingModel": "intfloat/e5-large-v2",
  "embeddingValidationId": "string",
  "errorMessage": "Unknown vector database error occurred.",
  "errorResolution": "An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance.",
  "executionStatus": "NEW",
  "familyId": "string",
  "id": "string",
  "isSeparatorRegex": true,
  "lastUpdateDate": "2019-08-24T14:15:22Z",
  "metadataColumns": [
    "string"
  ],
  "name": "string",
  "organizationId": "string",
  "parentId": "string",
  "percentage": 0,
  "playgroundsCount": 0,
  "separators": [
    null
  ],
  "size": 0,
  "source": "DataRobot",
  "tenantId": "string",
  "useCaseId": "string",
  "userName": "string",
  "validationId": "string",
  "version": 0
}

Responses

Status Meaning Description Schema
201 Created Custom model hosted vector database successfully added. Full representation is available in the response body. VectorDatabaseResponse
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/vectorDatabases/supportedEmbeddings/

List the supported embedding models for building vector databases.

Code samples

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

Parameters

Name In Type Required Description
datasetId query string false Only retrieve the embedding models compatible with this dataset ID.
useCaseId query string false If specified, include the custom model embeddings available for this use case ID.

Example responses

200 Response

{
  "customModelEmbeddingValidations": [
    {
      "id": "string",
      "name": "string"
    }
  ],
  "defaultEmbeddingModel": "string",
  "embeddingModels": [
    {
      "description": "string",
      "embeddingModel": "intfloat/e5-large-v2",
      "languages": [
        "Afrikaans"
      ],
      "maxSequenceLength": 0
    }
  ]
}

Responses

Status Meaning Description Schema
200 OK Supported embeddings successfully retrieved. SupportedEmbeddingsResponse
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/vectorDatabases/supportedRetrievalSettings/

List all vector database retrieval settings that are supported by LLM blueprints.

Code samples

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

Example responses

200 Response

{
  "settings": [
    {
      "default": null,
      "description": "string",
      "enum": [
        "string"
      ],
      "groupId": "string",
      "maximum": 0,
      "minimum": 0,
      "name": "string",
      "settings": [
        {}
      ],
      "title": "string",
      "type": "string"
    }
  ]
}

Responses

Status Meaning Description Schema
200 OK Supported vector database retrieval settings successfully retrieved. SupportedRetrievalSettingsResponse
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/vectorDatabases/supportedTextChunkings/

List the supported text chunking methods for building vector databases.

Code samples

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

Example responses

200 Response

{
  "textChunkingConfigs": [
    {
      "defaultMethod": "string",
      "embeddingModel": "intfloat/e5-large-v2",
      "methods": [
        {
          "chunkingMethod": "recursive",
          "chunkingParameters": [
            {
              "default": 0,
              "description": "string",
              "max": 0,
              "min": 0,
              "name": "string",
              "type": "int"
            }
          ],
          "description": "string",
          "title": "Recursive"
        }
      ]
    }
  ]
}

Responses

Status Meaning Description Schema
200 OK Supported text chunking methods successfully retrieved. SupportedTextChunkingResponse

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

BearerAuth

DELETE /api/v2/genai/vectorDatabases/{vectorDatabaseId}/

Delete an existing vector database.

Code samples

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

Parameters

Name In Type Required Description
vectorDatabaseId path string true The ID of the vector database to delete.

Example responses

422 Response

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

Responses

Status Meaning Description Schema
204 No Content Vector database successfully deleted. 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/vectorDatabases/{vectorDatabaseId}/

Retrieve an existing vector database.

Code samples

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

Parameters

Name In Type Required Description
vectorDatabaseId path string true The ID of the vector database to retrieve.

Example responses

200 Response

{
  "addedDatasetIds": [
    "string"
  ],
  "addedDatasetNames": [
    "string"
  ],
  "chunkOverlapPercentage": 0,
  "chunkSize": 0,
  "chunkingMethod": "recursive",
  "chunksCount": 0,
  "creationDate": "2019-08-24T14:15:22Z",
  "creationUserId": "string",
  "datasetId": "string",
  "datasetName": "string",
  "embeddingModel": "intfloat/e5-large-v2",
  "embeddingValidationId": "string",
  "errorMessage": "Unknown vector database error occurred.",
  "errorResolution": "An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance.",
  "executionStatus": "NEW",
  "familyId": "string",
  "id": "string",
  "isSeparatorRegex": true,
  "lastUpdateDate": "2019-08-24T14:15:22Z",
  "metadataColumns": [
    "string"
  ],
  "name": "string",
  "organizationId": "string",
  "parentId": "string",
  "percentage": 0,
  "playgroundsCount": 0,
  "separators": [
    null
  ],
  "size": 0,
  "source": "DataRobot",
  "tenantId": "string",
  "useCaseId": "string",
  "userName": "string",
  "validationId": "string",
  "version": 0
}

Responses

Status Meaning Description Schema
200 OK Vector database successfully retrieved. VectorDatabaseResponse
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/vectorDatabases/{vectorDatabaseId}/

Edit an existing vector database.

Code samples

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

Body parameter

{
  "name": "string"
}

Parameters

Name In Type Required Description
vectorDatabaseId path string true The ID of the vector database to edit.
body body EditVectorDatabaseRequest true none

Example responses

200 Response

{
  "addedDatasetIds": [
    "string"
  ],
  "addedDatasetNames": [
    "string"
  ],
  "chunkOverlapPercentage": 0,
  "chunkSize": 0,
  "chunkingMethod": "recursive",
  "chunksCount": 0,
  "creationDate": "2019-08-24T14:15:22Z",
  "creationUserId": "string",
  "datasetId": "string",
  "datasetName": "string",
  "embeddingModel": "intfloat/e5-large-v2",
  "embeddingValidationId": "string",
  "errorMessage": "Unknown vector database error occurred.",
  "errorResolution": "An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance.",
  "executionStatus": "NEW",
  "familyId": "string",
  "id": "string",
  "isSeparatorRegex": true,
  "lastUpdateDate": "2019-08-24T14:15:22Z",
  "metadataColumns": [
    "string"
  ],
  "name": "string",
  "organizationId": "string",
  "parentId": "string",
  "percentage": 0,
  "playgroundsCount": 0,
  "separators": [
    null
  ],
  "size": 0,
  "source": "DataRobot",
  "tenantId": "string",
  "useCaseId": "string",
  "userName": "string",
  "validationId": "string",
  "version": 0
}

Responses

Status Meaning Description Schema
200 OK Vector database successfully updated. VectorDatabaseResponse
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/vectorDatabases/{vectorDatabaseId}/datasetExportJobs/

Export an existing vector database as dataset to AI catalog.

Code samples

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

Parameters

Name In Type Required Description
vectorDatabaseId path string true The ID of the vector database to retrieve.

Example responses

202 Response

{
  "exportDatasetId": "string",
  "jobId": "string",
  "vectorDatabaseId": "string"
}

Responses

Status Meaning Description Schema
202 Accepted Vector database export job successfully accepted.Follow the Location header to poll for job execution status. VectorDatabaseExportResponse
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/vectorDatabases/{vectorDatabaseId}/latestVersion/

Retrieve the latest vector database within the family associated with this vector database.

Code samples

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

Parameters

Name In Type Required Description
vectorDatabaseId path string true The ID of the vector database to retrieve the latest version.
completedOnly query boolean false If true, only retrieve the vector databases that have finished building. The default is false.

Example responses

200 Response

{
  "addedDatasetIds": [
    "string"
  ],
  "addedDatasetNames": [
    "string"
  ],
  "chunkOverlapPercentage": 0,
  "chunkSize": 0,
  "chunkingMethod": "recursive",
  "chunksCount": 0,
  "creationDate": "2019-08-24T14:15:22Z",
  "creationUserId": "string",
  "datasetId": "string",
  "datasetName": "string",
  "embeddingModel": "intfloat/e5-large-v2",
  "embeddingValidationId": "string",
  "errorMessage": "Unknown vector database error occurred.",
  "errorResolution": "An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance.",
  "executionStatus": "NEW",
  "familyId": "string",
  "id": "string",
  "isSeparatorRegex": true,
  "lastUpdateDate": "2019-08-24T14:15:22Z",
  "metadataColumns": [
    "string"
  ],
  "name": "string",
  "organizationId": "string",
  "parentId": "string",
  "percentage": 0,
  "playgroundsCount": 0,
  "separators": [
    null
  ],
  "size": 0,
  "source": "DataRobot",
  "tenantId": "string",
  "useCaseId": "string",
  "userName": "string",
  "validationId": "string",
  "version": 0
}

Responses

Status Meaning Description Schema
200 OK Latest vector database version successfully retrieved. VectorDatabaseResponse
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/vectorDatabases/{vectorDatabaseId}/supportedSyntheticDatasetGenerationLanguages/

List the languages for synthetic dataset generation that are supported by this vector database.

Code samples

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

Parameters

Name In Type Required Description
vectorDatabaseId path string true The ID of the vector database to retrieve supported languages.

Example responses

200 Response

{
  "recommendedLanguage": "string",
  "supportedLanguages": [
    "string"
  ]
}

Responses

Status Meaning Description Schema
200 OK Supported languages successfully retrieved. SupportedLanguagesResponse
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/vectorDatabases/{vectorDatabaseId}/textAndEmbeddings/

Retrieve the text chunk and embeddings asset for an existing vector database.

Code samples

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

Parameters

Name In Type Required Description
vectorDatabaseId path string true The ID of the vector database to retrieve the assets for.

Example responses

200 Response

null

Responses

Status Meaning Description Schema
200 OK Text and embeddings asset successfully retrieved. Inline
422 Unprocessable Entity Validation Error HTTPValidationErrorResponse

Response Schema

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

BearerAuth

Schemas

ChunkingMethodNames

"recursive"

ChunkingMethodNames

Properties

Name Type Required Restrictions Description
ChunkingMethodNames string false Supported names of text chunking methods.

Enumerated Values

Property Value
ChunkingMethodNames [recursive, semantic]

ChunkingMethodNamesTitle

"Recursive"

ChunkingMethodNamesTitle

Properties

Name Type Required Restrictions Description
ChunkingMethodNamesTitle string false Supported user-facing friendly ames of text chunking methods.

Enumerated Values

Property Value
ChunkingMethodNamesTitle [Recursive, Semantic]

ChunkingParameterTypes

"int"

ChunkingParameterTypes

Properties

Name Type Required Restrictions Description
ChunkingParameterTypes string false Supported parameter data types for text chunking parameters.

Enumerated Values

Property Value
ChunkingParameterTypes [int, list[str], bool]

ChunkingParameters

{
  "chunkOverlapPercentage": 50,
  "chunkSize": 0,
  "chunkingMethod": "recursive",
  "embeddingModel": "intfloat/e5-large-v2",
  "embeddingValidationId": "string",
  "isSeparatorRegex": false,
  "separators": [
    "string"
  ]
}

ChunkingParameters

Properties

Name Type Required Restrictions Description
chunkOverlapPercentage integer¦null false maximum: 50
minimum: 0
 The chunk overlap percentage to use for text chunking.
chunkSize integer¦null false The chunk size to use for text chunking (measured in tokens).
chunkingMethod ChunkingMethodNames¦null false The text chunking method to use.
embeddingModel EmbeddingModelNames¦null false The name of the embedding model to use. If omitted, DataRobot will choose the default embedding model automatically.
embeddingValidationId string¦null false The validation ID of the custom embedding model (in case of using a custom model for embeddings).
isSeparatorRegex boolean false Whether the text chunking separator uses a regular expression.
separators [string]¦null false maxItems: 9
 The list of separators to use for text chunking.

CreateCustomModelVectorDatabaseFromDeploymentRequest

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

CreateCustomModelVectorDatabaseFromDeploymentRequest

Properties

Name Type Required Restrictions Description
deploymentId string true The ID of the custom model deployment.
modelId string true The ID of the model in the custom model deployment.
name string true maxLength: 5000
minLength: 1
minLength: 1
 The name of the vector database.
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 true The ID of the use case to link the vector database to.

CreateCustomModelVectorDatabaseFromValidationIdPayload

{
  "name": "string",
  "useCaseId": "string",
  "validationId": "string"
}

CreateCustomModelVectorDatabaseFromValidationIdPayload

Properties

Name Type Required Restrictions Description
name string true maxLength: 5000
minLength: 1
minLength: 1
 The name of the vector database.
useCaseId string true The ID of the use case to link the vector database to.
validationId string true The validation ID of the custom model validation.

CreateVectorDatabaseRequest

{
  "chunkingParameters": {
    "chunkOverlapPercentage": 50,
    "chunkSize": 0,
    "chunkingMethod": "recursive",
    "embeddingModel": "intfloat/e5-large-v2",
    "embeddingValidationId": "string",
    "isSeparatorRegex": false,
    "separators": [
      "string"
    ]
  },
  "datasetId": "string",
  "name": "string",
  "parentVectorDatabaseId": "string",
  "updateDeployments": false,
  "updateLlmBlueprints": false,
  "useCaseId": "string"
}

CreateVectorDatabaseRequest

Properties

Name Type Required Restrictions Description
chunkingParameters ChunkingParameters¦null false The text chunking parameters to use for building the vector database.
datasetId string true The ID of the dataset to use for building the vector database.
name string¦null false maxLength: 5000
minLength: 1
minLength: 1
 The name of the vector database.
parentVectorDatabaseId string¦null false The ID of the parent vector database used as base for the re-building.
updateDeployments boolean false Whether to update the deployments that use the parent vector database.Can only be set to True if parent_vector_database_id is provided.
updateLlmBlueprints boolean false Whether to update the LLM blueprints that use the parent vector database.Can only be set to True if parent_vector_database_id is provided.
useCaseId string true The ID of the use case to link the vector database to.

CustomEmbeddingModelNames

"custom-embeddings/default"

CustomEmbeddingModelNames

Properties

Name Type Required Restrictions Description
CustomEmbeddingModelNames string false Model names for custom embedding models.

Enumerated Values

Property Value
CustomEmbeddingModelNames custom-embeddings/default

DatasetLanguages

"Afrikaans"

DatasetLanguages

Properties

Name Type Required Restrictions Description
DatasetLanguages string false The names of dataset languages.

Enumerated Values

Property Value
DatasetLanguages [Afrikaans, Amharic, Arabic, Assamese, Azerbaijani, Belarusian, Bulgarian, Bengali, Breton, Bosnian, Catalan, Czech, Welsh, Danish, German, Greek, English, Esperanto, Spanish, Estonian, Basque, Persian, Finnish, French, Western Frisian, Irish, Scottish Gaelic, Galician, Gujarati, Hausa, Hebrew, Hindi, Croatian, Hungarian, Armenian, Indonesian, Icelandic, Italian, Japanese, Javanese, Georgian, Kazakh, Khmer, Kannada, Korean, Kurdish, Kyrgyz, Latin, Lao, Lithuanian, Latvian, Malagasy, Macedonian, Malayalam, Mongolian, Marathi, Malay, Burmese, Nepali, Dutch, Norwegian, Oromo, Oriya, Panjabi, Polish, Pashto, Portuguese, Romanian, Russian, Sanskrit, Sindhi, Sinhala, Slovak, Slovenian, Somali, Albanian, Serbian, Sundanese, Swedish, Swahili, Tamil, Telugu, Thai, Tagalog, Turkish, Uyghur, Ukrainian, Urdu, Uzbek, Vietnamese, Xhosa, Yiddish, Chinese]

EditVectorDatabaseRequest

{
  "name": "string"
}

EditVectorDatabaseRequest

Properties

Name Type Required Restrictions Description
name string true maxLength: 5000
minLength: 1
minLength: 1
 The new name of the vector database.

EmbeddingModel

{
  "description": "string",
  "embeddingModel": "intfloat/e5-large-v2",
  "languages": [
    "Afrikaans"
  ],
  "maxSequenceLength": 0
}

EmbeddingModel

Properties

Name Type Required Restrictions Description
description string true The description of the embedding model.
embeddingModel EmbeddingModelNames true The name of the embedding model.
languages [DatasetLanguages] true The list of languages the embedding models supports.
maxSequenceLength integer true The maximum input token sequence length that the embedding model can accept.

EmbeddingModelNames

"intfloat/e5-large-v2"

EmbeddingModelNames

Properties

Name Type Required Restrictions Description
EmbeddingModelNames string false Embedding model names (matching the format of HuggingFace repositories).

Enumerated Values

Property Value
EmbeddingModelNames [intfloat/e5-large-v2, intfloat/e5-base-v2, intfloat/multilingual-e5-base, intfloat/multilingual-e5-small, sentence-transformers/all-MiniLM-L6-v2, jinaai/jina-embedding-t-en-v1, jinaai/jina-embedding-s-en-v2, cl-nagoya/sup-simcse-ja-base]

ExecutionStatus

"NEW"

ExecutionStatus

Properties

Name Type Required Restrictions Description
ExecutionStatus string false Job and entity execution status.

Enumerated Values

Property Value
ExecutionStatus [NEW, RUNNING, COMPLETED, REQUIRES_USER_INPUT, SKIPPED, ERROR]

HTTPValidationErrorResponse

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

HTTPValidationErrorResponse

Properties

Name Type Required Restrictions Description
detail [ValidationError] false none

ListVectorDatabaseSortQueryParam

"name"

ListVectorDatabaseSortQueryParam

Properties

Name Type Required Restrictions Description
ListVectorDatabaseSortQueryParam string false Sort order values for listing vector databases.

Enumerated Values

Property Value
ListVectorDatabaseSortQueryParam [name, -name, creationUserId, -creationUserId, creationDate, -creationDate, embeddingModel, -embeddingModel, datasetId, -datasetId, chunkingMethod, -chunkingMethod, chunksCount, -chunksCount, size, -size, userName, -userName, datasetName, -datasetName, playgroundsCount, -playgroundsCount, source, -source]

ListVectorDatabasesResponse

{
  "count": 0,
  "data": [
    {
      "addedDatasetIds": [
        "string"
      ],
      "addedDatasetNames": [
        "string"
      ],
      "chunkOverlapPercentage": 0,
      "chunkSize": 0,
      "chunkingMethod": "recursive",
      "chunksCount": 0,
      "creationDate": "2019-08-24T14:15:22Z",
      "creationUserId": "string",
      "datasetId": "string",
      "datasetName": "string",
      "embeddingModel": "intfloat/e5-large-v2",
      "embeddingValidationId": "string",
      "errorMessage": "Unknown vector database error occurred.",
      "errorResolution": "An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance.",
      "executionStatus": "NEW",
      "familyId": "string",
      "id": "string",
      "isSeparatorRegex": true,
      "lastUpdateDate": "2019-08-24T14:15:22Z",
      "metadataColumns": [
        "string"
      ],
      "name": "string",
      "organizationId": "string",
      "parentId": "string",
      "percentage": 0,
      "playgroundsCount": 0,
      "separators": [
        null
      ],
      "size": 0,
      "source": "DataRobot",
      "tenantId": "string",
      "useCaseId": "string",
      "userName": "string",
      "validationId": "string",
      "version": 0
    }
  ],
  "next": "string",
  "previous": "string",
  "totalCount": 0
}

ListVectorDatabasesResponse

Properties

Name Type Required Restrictions Description
count integer true The number of records on this page.
data [VectorDatabaseResponse] 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.

SupportedCustomModelEmbeddings

{
  "id": "string",
  "name": "string"
}

SupportedCustomModelEmbeddings

Properties

Name Type Required Restrictions Description
id string true The validation ID of the custom embedding model.
name string true The name of the custom embedding model.

SupportedEmbeddingsResponse

{
  "customModelEmbeddingValidations": [
    {
      "id": "string",
      "name": "string"
    }
  ],
  "defaultEmbeddingModel": "string",
  "embeddingModels": [
    {
      "description": "string",
      "embeddingModel": "intfloat/e5-large-v2",
      "languages": [
        "Afrikaans"
      ],
      "maxSequenceLength": 0
    }
  ]
}

SupportedEmbeddingsResponse

Properties

Name Type Required Restrictions Description
customModelEmbeddingValidations [SupportedCustomModelEmbeddings] false The list of validated custom embedding models.
defaultEmbeddingModel string true The name of the default embedding model.
embeddingModels [EmbeddingModel] true The list of embeddings models.

SupportedLanguagesResponse

{
  "recommendedLanguage": "string",
  "supportedLanguages": [
    "string"
  ]
}

SupportedLanguagesResponse

Properties

Name Type Required Restrictions Description
recommendedLanguage string true The recommended language.
supportedLanguages [string] true The list of supported languages.

SupportedRetrievalSettingsResponse

{
  "settings": [
    {
      "default": null,
      "description": "string",
      "enum": [
        "string"
      ],
      "groupId": "string",
      "maximum": 0,
      "minimum": 0,
      "name": "string",
      "settings": [
        {}
      ],
      "title": "string",
      "type": "string"
    }
  ]
}

SupportedRetrievalSettingsResponse

Properties

Name Type Required Restrictions Description
settings [VectorDatabaseSettingParameter] true The list of retrieval settings.

SupportedTextChunkingResponse

{
  "textChunkingConfigs": [
    {
      "defaultMethod": "string",
      "embeddingModel": "intfloat/e5-large-v2",
      "methods": [
        {
          "chunkingMethod": "recursive",
          "chunkingParameters": [
            {
              "default": 0,
              "description": "string",
              "max": 0,
              "min": 0,
              "name": "string",
              "type": "int"
            }
          ],
          "description": "string",
          "title": "Recursive"
        }
      ]
    }
  ]
}

SupportedTextChunkingResponse

Properties

Name Type Required Restrictions Description
textChunkingConfigs [TextChunkingConfig] true The list of text chunking configurations.

TextChunkingConfig

{
  "defaultMethod": "string",
  "embeddingModel": "intfloat/e5-large-v2",
  "methods": [
    {
      "chunkingMethod": "recursive",
      "chunkingParameters": [
        {
          "default": 0,
          "description": "string",
          "max": 0,
          "min": 0,
          "name": "string",
          "type": "int"
        }
      ],
      "description": "string",
      "title": "Recursive"
    }
  ]
}

TextChunkingConfig

Properties

Name Type Required Restrictions Description
defaultMethod string true The name of the default text chunking method.
embeddingModel any true The name of the embedding model.

anyOf

Name Type Required Restrictions Description
» anonymous EmbeddingModelNames false Embedding model names (matching the format of HuggingFace repositories).

or

Name Type Required Restrictions Description
» anonymous CustomEmbeddingModelNames false Model names for custom embedding models.

continued

Name Type Required Restrictions Description
methods [TextChunkingMethod] true The list of text chunking methods.

TextChunkingMethod

{
  "chunkingMethod": "recursive",
  "chunkingParameters": [
    {
      "default": 0,
      "description": "string",
      "max": 0,
      "min": 0,
      "name": "string",
      "type": "int"
    }
  ],
  "description": "string",
  "title": "Recursive"
}

TextChunkingMethod

Properties

Name Type Required Restrictions Description
chunkingMethod ChunkingMethodNames true The name of the text chunking method.
chunkingParameters [TextChunkingParameterFields] true The list of text chunking parameters.
description string true The description of the text chunking method.
title ChunkingMethodNamesTitle true User-friendly label for the text chunking method.

TextChunkingParameterFields

{
  "default": 0,
  "description": "string",
  "max": 0,
  "min": 0,
  "name": "string",
  "type": "int"
}

TextChunkingParameterFields

Properties

Name Type Required Restrictions Description
default any true The default value of the parameter.

anyOf

Name Type Required Restrictions Description
» anonymous integer false none

or

Name Type Required Restrictions Description
» anonymous [string] false none

or

Name Type Required Restrictions Description
» anonymous boolean false none

continued

Name Type Required Restrictions Description
description string true The description of the parameter.
max integer¦null true The maximum value of the parameter (inclusive).
min integer¦null true The minimum value of the parameter (inclusive).
name string true The name of the parameter.
type ChunkingParameterTypes true The data type of the parameter.

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., Inference server is not running., 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., Vector database size exceeds the maximum custom model image size., 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 failed to process the chat completion 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., Deployment prediction does not contain the specified target column name., Worker process was unexpectedly terminated., Only one of responseColumnName or promptColumnName may be specified., Either responseColumnName or promptColumnName may be specified.]

VectorDatabaseErrorResolutions

"An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance."

VectorDatabaseErrorResolutions

Properties

Name Type Required Restrictions Description
VectorDatabaseErrorResolutions string false Vector database resolutions are not part of the vector database API response,
but added to llm-blueprint and chat- and comparison-prompt responses
to provide the user verbose information on how to resolve existing issues.

Enumerated Values

Property Value
VectorDatabaseErrorResolutions [An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance., A prediction timeout error occured for your vector database. Please increase your prediction timeout for your custom model validation. And then try revalidating the custom model again. Otherwise contact the DataRobot team for assistance., A prediction error occurred because inference server is not running. Try revalidating the custom model again after some time. Otherwise contact the DataRobot team for assistance., An unknown error occurred when loading the embedding weights. Choose another embedding model or contact DataRobot for assistance., An unknown error occurred when loading the dataset. Validate the dataset or contact DataRobot for assistance., Dataset must contain UTF-8 formatted text. PDFs that contain only images are not supported., Reduce the size of raw text in the dataset or choose a smaller embedding model., An unknown error occurred when splitting the dataset into chunks. Validate the dataset, try a different chunking configuration, or contact DataRobot for assistance., An unknown error occurred during embedding generation. Validate the dataset, choose a different embedding model, or contact DataRobot for assistance., An unknown error occurred during index creation. Contact DataRobot for assistance., An unknown error occurred when storing the vector database.Reduce the dataset size or choose a smaller embedding model. Otherwise, contact DataRobot for assistance., An unknown error occurred when reading the vector database settings. Contact DataRobot for assistance., The vector database size exceeds the maximum custom model image size of 13.0 GB and therefore can not be used in production.Reduce the dataset size or choose a smaller embedding model., An unknown error occurred during document retrieval. Contact DataRobot for assistance., This LLM blueprint's vector database was deleted, disabling the blueprint. To use the same blueprint configuration, copy it to a draft and select a new database., The embedding model cannot be loaded. Contact DataRobot for assistance., The API token of the deployed external vector database was deleted or revoked. Using a new token that provides API access, validate and recreate the external vector database., This LLM blueprint's external vector database is no longer available. Either restore the database connection or copy the saved LLM blueprint to a draft and select a new database., The deployment providing access to the configured external vector database was deleted. Create a new vector database from an active deployment., Access to the external vector database deployment was revoked. Create a new vector database from an authorized deployment., The deployment type of the external vector database is not supported. Ensure the target type for your deployment is Unstructured or TextGeneration., You are attempting to access external vector database validation through non-token authentication methods. Only token-based authentication is allowed., The prediction request to the deployed external vector database failed. Ensure the deployment accepts JSON or CSV input and expects the prompt column to have the specified name., The chat completion request to the deployed external vector database failed. To disable the chat API in custom model deployments, delete thechatfunction from the model and redeploy it., The prediction response of the deployment has an invalid format. Ensure that the unstructured deployment responds with valid JSON that includes the specified target column name., Make sure the target column name exist in the deployment prediction response., The API token of the deployed external embedding model was deleted or revoked. Using a new token that provides API access, validate and recreate the external embedding model., The external embedding model used with this LLM blueprint's vector database is no longer available. Either restore the external embedding connection or select a different vector database., The deployment providing access to the configured external embedding model was deleted. Create a new external embedding model from an active deployment., Access to the external embedding deployment was revoked. Create a new embedding model from an authorized deployment., The deployment type of the external embedding model is not supported. Ensure the target type for your deployment is Unstructured., You are attempting to access external embedding validation through non-token authentication methods. Only token-based authentication is allowed., The prediction request to the deployed external embedding model failed. Make sure the deployment accepts JSON or CSV input with specified prompt column name., The prediction response of the deployment has an invalid format. Ensure the unstructured deployment responds with valid JSON that includes the specified target column name., The worker process was unexpectedly terminated. This could have been caused by excessive memory usage or an internal system error. Try re-creating the vector database with a smaller dataset and a smaller embedding model., Make sure only one of responseColumnName or promptColumnName must be specified., Make sure that either one of responseColumnName or promptColumnName is specified.]

VectorDatabaseErrorResolutionsForDrafts

"An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance."

VectorDatabaseErrorResolutionsForDrafts

Properties

Name Type Required Restrictions Description
VectorDatabaseErrorResolutionsForDrafts string false Error resolutions for vector databases used in blueprint drafts.

Enumerated Values

Property Value
VectorDatabaseErrorResolutionsForDrafts [An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance., A prediction timeout error occured for your vector database. Please increase your prediction timeout for your custom model validation. And then try revalidating the custom model again. Otherwise contact the DataRobot team for assistance., A prediction error occurred because inference server is not running. Try revalidating the custom model again after some time. Otherwise contact the DataRobot team for assistance., An unknown error occurred when loading the embedding weights. Choose another embedding model or contact DataRobot for assistance., An unknown error occurred when loading the dataset. Validate the dataset or contact DataRobot for assistance., Dataset must contain UTF-8 formatted text. PDFs that contain only images are not supported., Reduce the size of raw text in the dataset or choose a smaller embedding model., An unknown error occurred when splitting the dataset into chunks. Validate the dataset, try a different chunking configuration, or contact DataRobot for assistance., An unknown error occurred during embedding generation. Validate the dataset, choose a different embedding model, or contact DataRobot for assistance., An unknown error occurred during index creation. Contact DataRobot for assistance., An unknown error occurred when storing the vector database.Reduce the dataset size or choose a smaller embedding model. Otherwise, contact DataRobot for assistance., An unknown error occurred when reading the vector database settings. Contact DataRobot for assistance., The vector database size exceeds the maximum custom model image size of 13.0 GB and therefore can not be used in production.Reduce the dataset size or choose a smaller embedding model., An unknown error occurred during document retrieval. Contact DataRobot for assistance., The vector database for this draft has been deleted. To proceed, select a new database in the configuration., The embedding model cannot be loaded. Contact DataRobot for assistance., The API token of the deployed external vector database was deleted or revoked. Using a new token that provides API access, validate and recreate the external vector database., The external vector database for this draft is no longer available. To proceed, either restore the database connection or select a new database in the configuration., The deployment providing access to the configured external vector database was deleted. Create a new vector database from an active deployment., Access to the external vector database deployment was revoked. Create a new vector database from an authorized deployment., The deployment type of the external vector database is not supported. Ensure the target type for your deployment is Unstructured or TextGeneration., You are attempting to access external vector database validation through non-token authentication methods. Only token-based authentication is allowed., The prediction request to the deployed external vector database failed. Ensure the deployment accepts JSON or CSV input and expects the prompt column to have the specified name., The chat completion request to the deployed external vector database failed. To disable the chat API in custom model deployments, delete thechatfunction from the model and redeploy it., The prediction response of the deployment has an invalid format. Ensure that the unstructured deployment responds with valid JSON that includes the specified target column name., Make sure the target column name exist in the deployment prediction response., The API token of the deployed external embedding model was deleted or revoked. Using a new token that provides API access, validate and recreate the external embedding model., The external embedding model used with this LLM blueprint's vector database is no longer available. Either restore the external embedding connection or select a different vector database., The deployment providing access to the configured external embedding model was deleted. Create a new external embedding model from an active deployment., Access to the external embedding deployment was revoked. Create a new embedding model from an authorized deployment., The deployment type of the external embedding model is not supported. Ensure the target type for your deployment is Unstructured., You are attempting to access external embedding validation through non-token authentication methods. Only token-based authentication is allowed., The prediction request to the deployed external embedding model failed. Make sure the deployment accepts JSON or CSV input with specified prompt column name., The prediction response of the deployment has an invalid format. Ensure the unstructured deployment responds with valid JSON that includes the specified target column name., The worker process was unexpectedly terminated. This could have been caused by excessive memory usage or an internal system error. Try re-creating the vector database with a smaller dataset and a smaller embedding model., Make sure only one of responseColumnName or promptColumnName must be specified., Make sure that either one of responseColumnName or promptColumnName is specified.]

VectorDatabaseExportResponse

{
  "exportDatasetId": "string",
  "jobId": "string",
  "vectorDatabaseId": "string"
}

VectorDatabaseExportResponse

Properties

Name Type Required Restrictions Description
exportDatasetId string true The AI Catalog dataset ID.
jobId string(uuid4) true The ID of the export job.
vectorDatabaseId string true The ID of the vector database.

VectorDatabaseResponse

{
  "addedDatasetIds": [
    "string"
  ],
  "addedDatasetNames": [
    "string"
  ],
  "chunkOverlapPercentage": 0,
  "chunkSize": 0,
  "chunkingMethod": "recursive",
  "chunksCount": 0,
  "creationDate": "2019-08-24T14:15:22Z",
  "creationUserId": "string",
  "datasetId": "string",
  "datasetName": "string",
  "embeddingModel": "intfloat/e5-large-v2",
  "embeddingValidationId": "string",
  "errorMessage": "Unknown vector database error occurred.",
  "errorResolution": "An unknown error during vector database creation occurred. Validate the dataset or the external vector database if applicable. Otherwise, contact DataRobot for assistance.",
  "executionStatus": "NEW",
  "familyId": "string",
  "id": "string",
  "isSeparatorRegex": true,
  "lastUpdateDate": "2019-08-24T14:15:22Z",
  "metadataColumns": [
    "string"
  ],
  "name": "string",
  "organizationId": "string",
  "parentId": "string",
  "percentage": 0,
  "playgroundsCount": 0,
  "separators": [
    null
  ],
  "size": 0,
  "source": "DataRobot",
  "tenantId": "string",
  "useCaseId": "string",
  "userName": "string",
  "validationId": "string",
  "version": 0
}

VectorDatabaseResponse

Properties

Name Type Required Restrictions Description
addedDatasetIds [string]¦null true The list of dataset IDs that were added to the vector database in addition to the initial creation dataset.
addedDatasetNames [string]¦null true The list of dataset names that were added to the vector database in addition to the initial creation dataset.
chunkOverlapPercentage integer¦null true The chunk overlap percentage that the vector database uses.
chunkSize integer¦null true The size of the text chunk (measured in tokens) that the vector database uses.
chunkingMethod ChunkingMethodNames¦null true The text chunking method the vector database uses.
chunksCount integer true The number of text chunks in the vector database.
creationDate string(date-time) true The creation date of the vector database (ISO 8601 formatted).
creationUserId string true The ID of the user that created this vector database.
datasetId string¦null true The ID of the dataset the vector database was built from.
datasetName string true The name of the dataset this vector database was built from.
embeddingModel any true The name of the embedding model the vector database uses.

anyOf

Name Type Required Restrictions Description
» anonymous EmbeddingModelNames false Embedding model names (matching the format of HuggingFace repositories).

or

Name Type Required Restrictions Description
» anonymous string false none

continued

Name Type Required Restrictions Description
embeddingValidationId string¦null true The validation ID of the custom model embedding (in case of using a custom model for embeddings).
errorMessage VectorDatabaseErrorMessages¦null true The error message associated with the vector database creation error (in case of a creation error).
errorResolution any true The suggested error resolution for the vector database creation error (in case of a creation error).

anyOf

Name Type Required Restrictions Description
» anonymous VectorDatabaseErrorResolutions false Vector database resolutions are not part of the vector database API response,
but added to llm-blueprint and chat- and comparison-prompt responses
to provide the user verbose information on how to resolve existing issues.

or

Name Type Required Restrictions Description
» anonymous VectorDatabaseErrorResolutionsForDrafts false Error resolutions for vector databases used in blueprint drafts.

continued

Name Type Required Restrictions Description
executionStatus ExecutionStatus true The creation status of the vector database.
familyId string¦null true An ID associated with a family of vector databases, that is, a parent and all descendant vector databases. All vector databases that are descendants of the same root parent share a family ID.The family ID is equal to the vector database ID of the root parent.Like this each vector database knows it's direct parent and the root parent.
id string true The ID of the vector database.
isSeparatorRegex boolean true Whether the text chunking separator uses a regular expression.
lastUpdateDate string(date-time) true The date of the most recent update of this playground (ISO 8601 formatted).
metadataColumns [string]¦null false The list of metadata columns in the vector database.
name string true The name of the vector database.
organizationId string true The ID of the DataRobot organization this vector database belongs to.
parentId string¦null true The ID of the direct parent vector database.It is generated when a vector database is created from another vector database.For the root (parent), ID is 'None'.
percentage number¦null false Vector database progress percentage.
playgroundsCount integer true The number of playgrounds that use this vector database.
separators [any]¦null true The text chunking separators that the vector database uses.
size integer true The size of the vector database (in bytes).
source VectorDatabaseSource true The source of the vector database.
tenantId string(uuid4) true The ID of the DataRobot tenant this vector database belongs to.
useCaseId string true The ID of the use case the vector database is linked to.
userName string true The name of the user that created this vector database.
validationId string¦null true The validation ID of the custom model vector database (in case of using a custom model vector database).
version integer true The version of the vector database linked to a certain family ID.

VectorDatabaseSettingParameter

{
  "default": null,
  "description": "string",
  "enum": [
    "string"
  ],
  "groupId": "string",
  "maximum": 0,
  "minimum": 0,
  "name": "string",
  "settings": [
    {
      "default": null,
      "description": "string",
      "enum": [
        "string"
      ],
      "groupId": "string",
      "maximum": 0,
      "minimum": 0,
      "name": "string",
      "settings": [
        {}
      ],
      "title": "string",
      "type": "string"
    }
  ],
  "title": "string",
  "type": "string"
}

VectorDatabaseSettingParameter

Properties

Name Type Required Restrictions Description
default any false The default value of the parameter.
description string true The description of the parameter.
enum [string]¦null false The list of possible values for the parameter.
groupId string¦null false The identifier of the group the parameter belongs to.
maximum integer¦null false The maximum value of the parameter.
minimum integer¦null false The minimum value of the parameter.
name string true The name of the parameter.
settings [VectorDatabaseSettingParameter]¦null false The list of available settings for the parameter.
title string true The title of the parameter.
type any true The type of the parameter.

anyOf

Name Type Required Restrictions Description
» anonymous VectorDatabaseSettingTypes false The types of vector database setting parameters.

or

Name Type Required Restrictions Description
» anonymous [VectorDatabaseSettingTypes] false [The types of vector database setting parameters.]

VectorDatabaseSettingTypes

"string"

VectorDatabaseSettingTypes

Properties

Name Type Required Restrictions Description
VectorDatabaseSettingTypes string false The types of vector database setting parameters.

Enumerated Values

Property Value
VectorDatabaseSettingTypes [string, integer, boolean, null, number, array]

VectorDatabaseSource

"DataRobot"

VectorDatabaseSource

Properties

Name Type Required Restrictions Description
VectorDatabaseSource string false The source of the vector database.

Enumerated Values

Property Value
VectorDatabaseSource [DataRobot, External]
Updated January 15, 2025