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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.
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": [
    {
      "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",
      "executionStatus": "NEW",
      "id": "string",
      "isSeparatorRegex": true,
      "lastUpdateDate": "2019-08-24T14:15:22Z",
      "name": "string",
      "organizationId": "string",
      "playgroundsCount": 0,
      "separators": [
        null
      ],
      "size": 0,
      "source": "DataRobot",
      "tenantId": "string",
      "useCaseId": "string",
      "userName": "string",
      "validationId": "string"
    }
  ],
  "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}"

Body parameter

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

Parameters

Name In Type Required Description
body body CreateVectorDatabaseRequest true none

Example responses

202 Response

{
  "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",
  "executionStatus": "NEW",
  "id": "string",
  "isSeparatorRegex": true,
  "lastUpdateDate": "2019-08-24T14:15:22Z",
  "name": "string",
  "organizationId": "string",
  "playgroundsCount": 0,
  "separators": [
    null
  ],
  "size": 0,
  "source": "DataRobot",
  "tenantId": "string",
  "useCaseId": "string",
  "userName": "string",
  "validationId": "string"
}

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}"

Body parameter

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

Parameters

Name In Type Required Description
body body any true none

Example responses

201 Response

{
  "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",
  "executionStatus": "NEW",
  "id": "string",
  "isSeparatorRegex": true,
  "lastUpdateDate": "2019-08-24T14:15:22Z",
  "name": "string",
  "organizationId": "string",
  "playgroundsCount": 0,
  "separators": [
    null
  ],
  "size": 0,
  "source": "DataRobot",
  "tenantId": "string",
  "useCaseId": "string",
  "userName": "string",
  "validationId": "string"
}

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/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"
        }
      ]
    }
  ]
}

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

{
  "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",
  "executionStatus": "NEW",
  "id": "string",
  "isSeparatorRegex": true,
  "lastUpdateDate": "2019-08-24T14:15:22Z",
  "name": "string",
  "organizationId": "string",
  "playgroundsCount": 0,
  "separators": [
    null
  ],
  "size": 0,
  "source": "DataRobot",
  "tenantId": "string",
  "useCaseId": "string",
  "userName": "string",
  "validationId": "string"
}

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}"

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

{
  "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",
  "executionStatus": "NEW",
  "id": "string",
  "isSeparatorRegex": true,
  "lastUpdateDate": "2019-08-24T14:15:22Z",
  "name": "string",
  "organizationId": "string",
  "playgroundsCount": 0,
  "separators": [
    null
  ],
  "size": 0,
  "source": "DataRobot",
  "tenantId": "string",
  "useCaseId": "string",
  "userName": "string",
  "validationId": "string"
}

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

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

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 true maximum: 50
minimum: 0
The chunk overlap percentage to use for text chunking.
chunkSize integer true The chunk size to use for text chunking (measured in tokens).
chunkingMethod ChunkingMethodNames true 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] true 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
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
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",
  "useCaseId": "string"
}

CreateVectorDatabaseRequest

Properties

Name Type Required Restrictions Description
chunkingParameters ChunkingParameters true 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
The name of the vector database.
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
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, sentence-transformers/all-MiniLM-L6-v2, jinaai/jina-embedding-t-en-v1, cl-nagoya/sup-simcse-ja-base]

ExecutionStatus

"NEW"

ExecutionStatus

Properties

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

Enumerated Values

Property Value
ExecutionStatus [NEW, RUNNING, COMPLETED, 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": [
    {
      "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",
      "executionStatus": "NEW",
      "id": "string",
      "isSeparatorRegex": true,
      "lastUpdateDate": "2019-08-24T14:15:22Z",
      "name": "string",
      "organizationId": "string",
      "playgroundsCount": 0,
      "separators": [
        null
      ],
      "size": 0,
      "source": "DataRobot",
      "tenantId": "string",
      "useCaseId": "string",
      "userName": "string",
      "validationId": "string"
    }
  ],
  "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.

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"
        }
      ]
    }
  ]
}

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"
    }
  ]
}

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"
}

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.

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., 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]

VectorDatabaseResponse

{
  "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",
  "executionStatus": "NEW",
  "id": "string",
  "isSeparatorRegex": true,
  "lastUpdateDate": "2019-08-24T14:15:22Z",
  "name": "string",
  "organizationId": "string",
  "playgroundsCount": 0,
  "separators": [
    null
  ],
  "size": 0,
  "source": "DataRobot",
  "tenantId": "string",
  "useCaseId": "string",
  "userName": "string",
  "validationId": "string"
}

VectorDatabaseResponse

Properties

Name Type Required Restrictions Description
chunkOverlapPercentage integer true The chunk overlap percentage the vector database uses.
chunkSize integer true The size of the text chunk (measured in tokens) 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 EmbeddingModelNames¦null true The name of the embedding model the vector database uses.
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).
executionStatus ExecutionStatus true The creation status of the vector database.
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).
name string true The name of the vector database.
organizationId string true The ID of the DataRobot organization this vector database belongs to.
playgroundsCount integer true The number of playgrounds that use this vector database.
separators [any] true The text chunking separators 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).

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 June 21, 2024