Control file and table import jobs, integration keys and BYOB connectors.
For task-oriented walkthroughs of these operations, see the Data import guide . Every mutation below is sent as a POST to https://app.arize.com/graphql with an x-api-key header; see Forming calls .
Mutations in this page Reference createFileImportJob Create a job that will import model inferences from a blob store
createFileImportJob(input: CreateFileImportJobInput!): CreateFileImportJobPayloadArguments input CreateFileImportJobInput!
required
Show CreateFileImportJobInput fields
The space id for the space under which the model should be ingested
Additional information required to locate an Azure container. Required when running with an Azure service principal or managed identity. Show AzureStorageIdentifier fields
The Azure Active Directory Tenant ID associated with the Storage Account.
The Azure Storage Account name.
The model identifier or name under which the model will be stored.
The type of model. This determines how the model will be analyzed One of: numeric, score_categorical, ranking, object_detection, generative_llm, multi_class.
modelEnvironmentName ModelEnvironmentName!
required
The environment for which the inferences are being ingested One of: production, validation, training, tracing.
A name for the ingestion batch. Only required if the environment is validation
The model version. This will tag all the inferences as being for a specific version string.
The cloud storage solution where the files reside One of: S3, GCS, Azure.
The bucket where the files are stored. Arize must have access to this bucket
The path to the files. Ex. cc_model
schema FileImportSchemaInputType!
required
The schema for the CSV or Parquet files. The schema describes a mapping from your column names to model dimensions Show FileImportSchemaInputType fields
The name of the predictionId column in the file
The name of the predictionGroupId column in the file. Required if the model is type ranking
A prefix capture group used to match feature columns
A list of column names specifying features
The name of the timestamp column in the file
The name of the prediction label column in the file. Supported on model of type score_categorical
The name of the prediction score column in the file. Supported on models of type numeric and score_categorical
The name of the prediction scores column in the file. Supported on multi_class models
The name of the actual label column in the file. Supported on model of type score_categorical
The name of the actual score column in the file. Supported on models of type numeric and score_categorical
The name of the actual scores column in the file. Supported on multi_class models
The name of the threshold scores column in the file. Supported on multi_class models
The name of the relevance label column in the file. Required if the model is type ranking
The name of the relevance score column in the file. Required if the model is type ranking
The name of the model version column in the file
The name of the validation batch id. Required if the model is type validation
A prefix capture group used to match tag column
A list of column names specifying tags
A regex like capture group that should be used to match shap column
The name of the rank column in the file. Required if the model is type ranking
The names of fields to be excluded from data ingestion
embeddingFeatures [FileImportEmbeddingInputType!]
The embedding fields that will be ingested Show FileImportEmbeddingInputType fields
The embedding feature name
The name of the vector column
The name of the raw data column
The name of the link to data column
prompt FileImportPromptResponseInputType
The columns which contain the prompt used in a generative LLM. Relevant if the model is generative_llm Show FileImportPromptResponseInputType fields
The name of the vector column
The name of the raw data column. Required if the prompt or response is specified
response FileImportPromptResponseInputType
The columns which contain the response from a generative LLM. Relevant if the model is generative_llm Show FileImportPromptResponseInputType fields
The name of the vector column
The name of the raw data column. Required if the prompt or response is specified
promptTemplate FileImportPromptTemplateInputType
The columns which contain the prompt template fields, such as the template and template version. Relevant if the model is generative_llm Show FileImportPromptTemplateInputType fields
The name of the column which contains the prompt template
The name of the column which contains the prompt template version
llmConfig FileImportLlmConfigInputType
The columns which contain the llm config fields, such as the llm name and params. Relevant if the model is generative_llm Show FileImportLlmConfigInputType fields
The name of the column which contains the llm name
The name of the column which contains a map of llm params to values
runMetadata FileImportRunMetadataInputType
The columns which contain the metadata relevant to the llm call that was used to generate the response using the prompt. Relevant if the model is generative_llm Show FileImportRunMetadataInputType fields
The name of the column which contains the total number of tokens used in the llm call
The name of the column which contains the number of prompt tokens in the llm call
The name of the column which contains the number of response tokens in the llm call
The name of the column which contains the response latency in ms of the llm call
predictionObjectDetectionLabel FileImportObjectDetectionLabelInputType
The columns which contain the object detection prediction fields, such as the bounding box coordinates, categories, and scores. Relevant if the model is object_detection Show FileImportObjectDetectionLabelInputType fields
boundingBoxesCoordinatesColumnName The name of the column which contains the bounding box coordinates
boundingBoxesCategoriesColumnName The name of the column which contains a map of bounding box categories
boundingBoxesScoresColumnName The name of the column which contains a map of bounding box scores
actualObjectDetectionLabel FileImportObjectDetectionLabelInputType
The columns which contain the object detection actual fields, such as the bounding box coordinates, categories, and scores. Relevant if the model is object_detection Show FileImportObjectDetectionLabelInputType fields
boundingBoxesCoordinatesColumnName The name of the column which contains the bounding box coordinates
boundingBoxesCategoriesColumnName The name of the column which contains a map of bounding box categories
boundingBoxesScoresColumnName The name of the column which contains a map of bounding box scores
The name of the file to perform a dry run on (ex. test-file.avro). If a file name is not selected, the dry run will be performed on the first file
If true, the import job will be attempted but no changes will be written. The fileResult field may be requested to check the validation status. Default is false
Returns Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Show JobValidationResult fields
Scalar fields: filePath, validationStatus. Object fields (select subfields): error.
Show FilePreviewResult fields
Scalar fields: filePath. Object fields (select subfields): columnMetadata, rows, errorCode.
Show FileImportJob fields
Scalar fields: id, jobId, bucketName, createdAt, modelName, modelId, jobUuid, modelType, modelVersion, modelEnvironmentName, batchId, blobStore, prefix, totalFilesSuccessCount, totalFilesFailedCount, totalFilesPendingCount, jobStatus. Object fields (select subfields): schema, files.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
updateFileImportJob Update an existing job that has been imported from a blob store
updateFileImportJob(input: UpdateFileImportJobInput!): UpdateFileImportJobPayloadArguments input UpdateFileImportJobInput!
required
Show UpdateFileImportJobInput fields
The ID of an import job to delete
The status of the imported job One of: ONGOING_INACTIVE, ONGOING_DELETED.
schema FileImportSchemaInputType!
required
The schema for the CSV or Parquet files. The schema describes a mapping from your column names to model dimensions Show FileImportSchemaInputType fields
The name of the predictionId column in the file
The name of the predictionGroupId column in the file. Required if the model is type ranking
A prefix capture group used to match feature columns
A list of column names specifying features
The name of the timestamp column in the file
The name of the prediction label column in the file. Supported on model of type score_categorical
The name of the prediction score column in the file. Supported on models of type numeric and score_categorical
The name of the prediction scores column in the file. Supported on multi_class models
The name of the actual label column in the file. Supported on model of type score_categorical
The name of the actual score column in the file. Supported on models of type numeric and score_categorical
The name of the actual scores column in the file. Supported on multi_class models
The name of the threshold scores column in the file. Supported on multi_class models
The name of the relevance label column in the file. Required if the model is type ranking
The name of the relevance score column in the file. Required if the model is type ranking
The name of the model version column in the file
The name of the validation batch id. Required if the model is type validation
A prefix capture group used to match tag column
A list of column names specifying tags
A regex like capture group that should be used to match shap column
The name of the rank column in the file. Required if the model is type ranking
The names of fields to be excluded from data ingestion
embeddingFeatures [FileImportEmbeddingInputType!]
The embedding fields that will be ingested Show FileImportEmbeddingInputType fields
The embedding feature name
The name of the vector column
The name of the raw data column
The name of the link to data column
prompt FileImportPromptResponseInputType
The columns which contain the prompt used in a generative LLM. Relevant if the model is generative_llm Show FileImportPromptResponseInputType fields
The name of the vector column
The name of the raw data column. Required if the prompt or response is specified
response FileImportPromptResponseInputType
The columns which contain the response from a generative LLM. Relevant if the model is generative_llm Show FileImportPromptResponseInputType fields
The name of the vector column
The name of the raw data column. Required if the prompt or response is specified
promptTemplate FileImportPromptTemplateInputType
The columns which contain the prompt template fields, such as the template and template version. Relevant if the model is generative_llm Show FileImportPromptTemplateInputType fields
The name of the column which contains the prompt template
The name of the column which contains the prompt template version
llmConfig FileImportLlmConfigInputType
The columns which contain the llm config fields, such as the llm name and params. Relevant if the model is generative_llm Show FileImportLlmConfigInputType fields
The name of the column which contains the llm name
The name of the column which contains a map of llm params to values
runMetadata FileImportRunMetadataInputType
The columns which contain the metadata relevant to the llm call that was used to generate the response using the prompt. Relevant if the model is generative_llm Show FileImportRunMetadataInputType fields
The name of the column which contains the total number of tokens used in the llm call
The name of the column which contains the number of prompt tokens in the llm call
The name of the column which contains the number of response tokens in the llm call
The name of the column which contains the response latency in ms of the llm call
predictionObjectDetectionLabel FileImportObjectDetectionLabelInputType
The columns which contain the object detection prediction fields, such as the bounding box coordinates, categories, and scores. Relevant if the model is object_detection Show FileImportObjectDetectionLabelInputType fields
boundingBoxesCoordinatesColumnName The name of the column which contains the bounding box coordinates
boundingBoxesCategoriesColumnName The name of the column which contains a map of bounding box categories
boundingBoxesScoresColumnName The name of the column which contains a map of bounding box scores
actualObjectDetectionLabel FileImportObjectDetectionLabelInputType
The columns which contain the object detection actual fields, such as the bounding box coordinates, categories, and scores. Relevant if the model is object_detection Show FileImportObjectDetectionLabelInputType fields
boundingBoxesCoordinatesColumnName The name of the column which contains the bounding box coordinates
boundingBoxesCategoriesColumnName The name of the column which contains a map of bounding box categories
boundingBoxesScoresColumnName The name of the column which contains a map of bounding box scores
Returns The updated import job Show FileImportJob fields
Scalar fields: id, jobId, bucketName, createdAt, modelName, modelId, jobUuid, modelType, modelVersion, modelEnvironmentName, batchId, blobStore, prefix, totalFilesSuccessCount, totalFilesFailedCount, totalFilesPendingCount, jobStatus. Object fields (select subfields): schema, files.
Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
startFileImportJob Start an existing job that has been imported from a blob store
startFileImportJob(input: StartFileImportJobInput!): StartFileImportJobPayloadArguments input StartFileImportJobInput!
required
Show StartFileImportJobInput fields
The ID of an import job to start
Returns The import job which has been started Show FileImportJob fields
Scalar fields: id, jobId, bucketName, createdAt, modelName, modelId, jobUuid, modelType, modelVersion, modelEnvironmentName, batchId, blobStore, prefix, totalFilesSuccessCount, totalFilesFailedCount, totalFilesPendingCount, jobStatus. Object fields (select subfields): schema, files.
Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
pauseFileImportJob Pause an existing job that has been imported from a blob store
pauseFileImportJob(input: PauseFileImportJobInput!): PauseFileImportJobPayloadArguments input PauseFileImportJobInput!
required
Show PauseFileImportJobInput fields
The ID of an import job to pause
Returns The paused import job Show FileImportJob fields
Scalar fields: id, jobId, bucketName, createdAt, modelName, modelId, jobUuid, modelType, modelVersion, modelEnvironmentName, batchId, blobStore, prefix, totalFilesSuccessCount, totalFilesFailedCount, totalFilesPendingCount, jobStatus. Object fields (select subfields): schema, files.
Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
deleteFileImportJob Delete an existing job that has been imported from a blob store
deleteFileImportJob(input: DeleteFileImportJobInput!): DeleteFileImportJobPayloadArguments input DeleteFileImportJobInput!
required
Show DeleteFileImportJobInput fields
The ID of an import job to delete
Returns The deleted import job Show FileImportJob fields
Scalar fields: id, jobId, bucketName, createdAt, modelName, modelId, jobUuid, modelType, modelVersion, modelEnvironmentName, batchId, blobStore, prefix, totalFilesSuccessCount, totalFilesFailedCount, totalFilesPendingCount, jobStatus. Object fields (select subfields): schema, files.
Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
resetFileStatus Reset the status of a file
resetFileStatus(input: ResetFileStatusInput!): ResetFileStatusPayloadArguments input ResetFileStatusInput!
required
Show ResetFileStatusInput fields
The id of the job to reset the status of
The id of the file to reset the status of
Returns The updated import job Show FileImportJob fields
Scalar fields: id, jobId, bucketName, createdAt, modelName, modelId, jobUuid, modelType, modelVersion, modelEnvironmentName, batchId, blobStore, prefix, totalFilesSuccessCount, totalFilesFailedCount, totalFilesPendingCount, jobStatus. Object fields (select subfields): schema, files.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
createTableImportJob Create a job that will import model inferences from a table
createTableImportJob(input: CreateTableImportJobInput!): CreateTableImportJobPayloadArguments input CreateTableImportJobInput!
required
Show CreateTableImportJobInput fields
The space id for the space under which the model should be ingested
The model identifier or name under which the model will be stored.
The type of model. This determines how the model will be analyzed One of: numeric, score_categorical, ranking, object_detection, generative_llm, multi_class.
modelEnvironmentName ModelEnvironmentName!
required
The environment for which the inferences are being ingested One of: production, validation, training, tracing.
A name for the ingestion batch. Only required if the environment is validation
The model version. This will tag all the inferences as being for a specific version string.
tableStore TableIntegrationType!
required
The table solution where the inferences reside One of: BigQuery, Snowflake, Databricks.
Show BigQueryTableConfig fields
The unique string used to differentiate a Google Cloud project
The dataset where the tables are stored. Arize must have access to this dataset
Show SnowflakeTableConfig fields
The account identifier assigned to the Snowflake account.
The schema which the target Table belongs to. Together, a database and schema comprise a namespace in Snowflake.
The database which the schema of the target Table belongs to.
The table which to import data from.
Show DatabricksTableConfig fields
The base URL of the workspace ex. databricks-hostname-example.databricks.com
The URL fragment that corresponds to the resource, either a Compute Cluster or SQL Warehouse ex. /sql/1.0/warehouses/example
The port of the resource ex. 443
Personal access token (for a service principal) ex. dapi12392103129093021. Required if ‘azureResourceId’ is needed & not provided.
The Azure Resource Manager ID for the Azure Databricks workspace. Required only if not doing token-based authentication.
If using Unity Catalog, this is the catalog name. If not, this will be hive_metastore
A logical collection of tables
The name of the table or view
schema TableImportSchemaInputType!
required
The schema for the CSV or Parquet files. The schema describes a mapping from your column names to model dimensions Show TableImportSchemaInputType fields
The name of the predictionId column in the file
The name of the predictionGroupId column in the file. Required if the model is type ranking
A prefix capture group used to match feature columns
A list of column names specifying features
The name of the timestamp column in the file
The name of the prediction label column in the file. Supported on model of type score_categorical
The name of the prediction score column in the file. Supported on models of type numeric and score_categorical
The name of the prediction scores column in the file. Supported on multi_class models
The name of the actual label column in the file. Supported on model of type score_categorical
The name of the actual score column in the file. Supported on models of type numeric and score_categorical
The name of the actual scores column in the file. Supported on multi_class models
The name of the threshold scores column in the file. Supported on multi_class models
The name of the relevance label column in the file. Required if the model is type ranking
The name of the relevance score column in the file. Required if the model is type ranking
The name of the model version column in the file
The name of the validation batch id. Required if the model is type validation
A prefix capture group used to match tag column
A list of column names specifying tags
A regex like capture group that should be used to match shap column
The name of the rank column in the file. Required if the model is type ranking
The names of fields to be excluded from data ingestion
embeddingFeatures [FileImportEmbeddingInputType!]
The embedding fields that will be ingested Show FileImportEmbeddingInputType fields
The embedding feature name
The name of the vector column
The name of the raw data column
The name of the link to data column
prompt FileImportPromptResponseInputType
The columns which contain the prompt used in a generative LLM. Relevant if the model is generative_llm Show FileImportPromptResponseInputType fields
The name of the vector column
The name of the raw data column. Required if the prompt or response is specified
response FileImportPromptResponseInputType
The columns which contain the response from a generative LLM. Relevant if the model is generative_llm Show FileImportPromptResponseInputType fields
The name of the vector column
The name of the raw data column. Required if the prompt or response is specified
promptTemplate FileImportPromptTemplateInputType
The columns which contain the prompt template fields, such as the template and template version. Relevant if the model is generative_llm Show FileImportPromptTemplateInputType fields
The name of the column which contains the prompt template
The name of the column which contains the prompt template version
llmConfig FileImportLlmConfigInputType
The columns which contain the llm config fields, such as the llm name and params. Relevant if the model is generative_llm Show FileImportLlmConfigInputType fields
The name of the column which contains the llm name
The name of the column which contains a map of llm params to values
runMetadata FileImportRunMetadataInputType
The columns which contain the metadata relevant to the llm call that was used to generate the response using the prompt. Relevant if the model is generative_llm Show FileImportRunMetadataInputType fields
The name of the column which contains the total number of tokens used in the llm call
The name of the column which contains the number of prompt tokens in the llm call
The name of the column which contains the number of response tokens in the llm call
The name of the column which contains the response latency in ms of the llm call
predictionObjectDetectionLabel FileImportObjectDetectionLabelInputType
The columns which contain the object detection prediction fields, such as the bounding box coordinates, categories, and scores. Relevant if the model is object_detection Show FileImportObjectDetectionLabelInputType fields
boundingBoxesCoordinatesColumnName The name of the column which contains the bounding box coordinates
boundingBoxesCategoriesColumnName The name of the column which contains a map of bounding box categories
boundingBoxesScoresColumnName The name of the column which contains a map of bounding box scores
actualObjectDetectionLabel FileImportObjectDetectionLabelInputType
The columns which contain the object detection actual fields, such as the bounding box coordinates, categories, and scores. Relevant if the model is object_detection Show FileImportObjectDetectionLabelInputType fields
boundingBoxesCoordinatesColumnName The name of the column which contains the bounding box coordinates
boundingBoxesCategoriesColumnName The name of the column which contains a map of bounding box categories
boundingBoxesScoresColumnName The name of the column which contains a map of bounding box scores
The name of the change timestamp column in the file.
If true, the import job will be attempted but no changes will be written. The fileResult field may be requested to check the validation status. Default is false
Returns Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Show TableJobValidationResult fields
Scalar fields: jobId, tableQueryId, queryId, validationStatus. Object fields (select subfields): error.
Show TableImportJob fields
Scalar fields: id, jobId, jobStatus, tableStore, projectId, dataset, createdAt, modelName, modelId, modelType, modelVersion, modelEnvironmentName, batchId, table, totalQueriesSuccessCount, totalQueriesFailedCount, totalQueriesPendingCount. Object fields (select subfields): schema, tableIngestionParameters, queries.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
updateTableImportJob Update an existing job that has been imported from a table
updateTableImportJob(input: UpdateTableImportJobInput!): UpdateTableImportJobPayloadArguments input UpdateTableImportJobInput!
required
Show UpdateTableImportJobInput fields
The ID of an import job to delete
The status of the imported job One of: ONGOING_INACTIVE, ONGOING_DELETED.
The model version set as a constant
schema TableImportSchemaInputType!
required
The schema describes a mapping from your column names to model dimensions Show TableImportSchemaInputType fields
The name of the predictionId column in the file
The name of the predictionGroupId column in the file. Required if the model is type ranking
A prefix capture group used to match feature columns
A list of column names specifying features
The name of the timestamp column in the file
The name of the prediction label column in the file. Supported on model of type score_categorical
The name of the prediction score column in the file. Supported on models of type numeric and score_categorical
The name of the prediction scores column in the file. Supported on multi_class models
The name of the actual label column in the file. Supported on model of type score_categorical
The name of the actual score column in the file. Supported on models of type numeric and score_categorical
The name of the actual scores column in the file. Supported on multi_class models
The name of the threshold scores column in the file. Supported on multi_class models
The name of the relevance label column in the file. Required if the model is type ranking
The name of the relevance score column in the file. Required if the model is type ranking
The name of the model version column in the file
The name of the validation batch id. Required if the model is type validation
A prefix capture group used to match tag column
A list of column names specifying tags
A regex like capture group that should be used to match shap column
The name of the rank column in the file. Required if the model is type ranking
The names of fields to be excluded from data ingestion
embeddingFeatures [FileImportEmbeddingInputType!]
The embedding fields that will be ingested Show FileImportEmbeddingInputType fields
The embedding feature name
The name of the vector column
The name of the raw data column
The name of the link to data column
prompt FileImportPromptResponseInputType
The columns which contain the prompt used in a generative LLM. Relevant if the model is generative_llm Show FileImportPromptResponseInputType fields
The name of the vector column
The name of the raw data column. Required if the prompt or response is specified
response FileImportPromptResponseInputType
The columns which contain the response from a generative LLM. Relevant if the model is generative_llm Show FileImportPromptResponseInputType fields
The name of the vector column
The name of the raw data column. Required if the prompt or response is specified
promptTemplate FileImportPromptTemplateInputType
The columns which contain the prompt template fields, such as the template and template version. Relevant if the model is generative_llm Show FileImportPromptTemplateInputType fields
The name of the column which contains the prompt template
The name of the column which contains the prompt template version
llmConfig FileImportLlmConfigInputType
The columns which contain the llm config fields, such as the llm name and params. Relevant if the model is generative_llm Show FileImportLlmConfigInputType fields
The name of the column which contains the llm name
The name of the column which contains a map of llm params to values
runMetadata FileImportRunMetadataInputType
The columns which contain the metadata relevant to the llm call that was used to generate the response using the prompt. Relevant if the model is generative_llm Show FileImportRunMetadataInputType fields
The name of the column which contains the total number of tokens used in the llm call
The name of the column which contains the number of prompt tokens in the llm call
The name of the column which contains the number of response tokens in the llm call
The name of the column which contains the response latency in ms of the llm call
predictionObjectDetectionLabel FileImportObjectDetectionLabelInputType
The columns which contain the object detection prediction fields, such as the bounding box coordinates, categories, and scores. Relevant if the model is object_detection Show FileImportObjectDetectionLabelInputType fields
boundingBoxesCoordinatesColumnName The name of the column which contains the bounding box coordinates
boundingBoxesCategoriesColumnName The name of the column which contains a map of bounding box categories
boundingBoxesScoresColumnName The name of the column which contains a map of bounding box scores
actualObjectDetectionLabel FileImportObjectDetectionLabelInputType
The columns which contain the object detection actual fields, such as the bounding box coordinates, categories, and scores. Relevant if the model is object_detection Show FileImportObjectDetectionLabelInputType fields
boundingBoxesCoordinatesColumnName The name of the column which contains the bounding box coordinates
boundingBoxesCategoriesColumnName The name of the column which contains a map of bounding box categories
boundingBoxesScoresColumnName The name of the column which contains a map of bounding box scores
The name of the change timestamp column in the file.
tableIngestionParameters TableIngestionParametersInputType!
required
Table ingestion parameters Show TableIngestionParametersInputType fields
How often a user wants to query data in minutes
How large of a query window to use in hours
Returns The updated table import job Show TableImportJob fields
Scalar fields: id, jobId, jobStatus, tableStore, projectId, dataset, createdAt, modelName, modelId, modelType, modelVersion, modelEnvironmentName, batchId, table, totalQueriesSuccessCount, totalQueriesFailedCount, totalQueriesPendingCount. Object fields (select subfields): schema, tableIngestionParameters, queries.
Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
updateTableIngestionParameters Update an existing job that has been imported from a table
updateTableIngestionParameters(input: UpdateTableIngestionParametersInput!): UpdateTableIngestionParametersPayloadArguments input UpdateTableIngestionParametersInput!
required
Show UpdateTableIngestionParametersInput fields
tableIngestionParameters TableIngestionParametersInputType!
required
Table ingestion parameters Show TableIngestionParametersInputType fields
How often a user wants to query data in minutes
How large of a query window to use in hours
Returns The updated table import job Show TableImportJob fields
Scalar fields: id, jobId, jobStatus, tableStore, projectId, dataset, createdAt, modelName, modelId, modelType, modelVersion, modelEnvironmentName, batchId, table, totalQueriesSuccessCount, totalQueriesFailedCount, totalQueriesPendingCount. Object fields (select subfields): schema, tableIngestionParameters, queries.
Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
startTableImportJob Start an existing job that has been imported from a table
startTableImportJob(input: StartTableImportJobInput!): StartTableImportJobPayloadArguments input StartTableImportJobInput!
required
Show StartTableImportJobInput fields
The ID of an import job to start
Returns The import job which has been started Show FileImportJob fields
Scalar fields: id, jobId, bucketName, createdAt, modelName, modelId, jobUuid, modelType, modelVersion, modelEnvironmentName, batchId, blobStore, prefix, totalFilesSuccessCount, totalFilesFailedCount, totalFilesPendingCount, jobStatus. Object fields (select subfields): schema, files.
Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
pauseTableImportJob Pause an existing job that has been imported from a table
pauseTableImportJob(input: PauseTableImportJobInput!): PauseTableImportJobPayloadArguments input PauseTableImportJobInput!
required
Show PauseTableImportJobInput fields
The ID of an import job to pause
Returns The paused import job Show TableImportJob fields
Scalar fields: id, jobId, jobStatus, tableStore, projectId, dataset, createdAt, modelName, modelId, modelType, modelVersion, modelEnvironmentName, batchId, table, totalQueriesSuccessCount, totalQueriesFailedCount, totalQueriesPendingCount. Object fields (select subfields): schema, tableIngestionParameters, queries.
Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
deleteTableImportJob Delete an existing job that has been imported from a table
deleteTableImportJob(input: DeleteTableImportJobInput!): DeleteTableImportJobPayloadArguments input DeleteTableImportJobInput!
required
Show DeleteTableImportJobInput fields
The ID of a table import job to delete
Returns The deleted table import job Show TableImportJob fields
Scalar fields: id, jobId, jobStatus, tableStore, projectId, dataset, createdAt, modelName, modelId, modelType, modelVersion, modelEnvironmentName, batchId, table, totalQueriesSuccessCount, totalQueriesFailedCount, totalQueriesPendingCount. Object fields (select subfields): schema, tableIngestionParameters, queries.
Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
createTriggeredOngoingTableImportJob Create a job that will import model inferences from a table
createTriggeredOngoingTableImportJob(input: CreateTriggeredOngoingTableImportJobInput!): CreateTriggeredOngoingTableImportJobPayloadArguments input CreateTriggeredOngoingTableImportJobInput!
required
Show CreateTriggeredOngoingTableImportJobInput fields
The space id for the space under which the model should be ingested
The model identifier or name under which the model will be stored.
The type of model. This determines how the model will be analyzed One of: numeric, score_categorical, ranking, object_detection, generative_llm, multi_class.
modelEnvironmentName ModelEnvironmentName!
required
The environment for which the inferences are being ingested One of: production, validation, training, tracing.
A name for the ingestion batch. Only required if the environment is validation
The model version. This will tag all the inferences as being for a specific version string.
tableStore TableIntegrationType!
required
The table solution where the inferences reside One of: BigQuery, Snowflake, Databricks.
Show BigQueryTableConfig fields
The unique string used to differentiate a Google Cloud project
The dataset where the tables are stored. Arize must have access to this dataset
Show SnowflakeTableConfig fields
The account identifier assigned to the Snowflake account.
The schema which the target Table belongs to. Together, a database and schema comprise a namespace in Snowflake.
The database which the schema of the target Table belongs to.
The table which to import data from.
Show DatabricksTableConfig fields
The base URL of the workspace ex. databricks-hostname-example.databricks.com
The URL fragment that corresponds to the resource, either a Compute Cluster or SQL Warehouse ex. /sql/1.0/warehouses/example
The port of the resource ex. 443
Personal access token (for a service principal) ex. dapi12392103129093021. Required if ‘azureResourceId’ is needed & not provided.
The Azure Resource Manager ID for the Azure Databricks workspace. Required only if not doing token-based authentication.
If using Unity Catalog, this is the catalog name. If not, this will be hive_metastore
A logical collection of tables
The name of the table or view
schema TableImportSchemaInputType!
required
The schema for the CSV or Parquet files. The schema describes a mapping from your column names to model dimensions Show TableImportSchemaInputType fields
The name of the predictionId column in the file
The name of the predictionGroupId column in the file. Required if the model is type ranking
A prefix capture group used to match feature columns
A list of column names specifying features
The name of the timestamp column in the file
The name of the prediction label column in the file. Supported on model of type score_categorical
The name of the prediction score column in the file. Supported on models of type numeric and score_categorical
The name of the prediction scores column in the file. Supported on multi_class models
The name of the actual label column in the file. Supported on model of type score_categorical
The name of the actual score column in the file. Supported on models of type numeric and score_categorical
The name of the actual scores column in the file. Supported on multi_class models
The name of the threshold scores column in the file. Supported on multi_class models
The name of the relevance label column in the file. Required if the model is type ranking
The name of the relevance score column in the file. Required if the model is type ranking
The name of the model version column in the file
The name of the validation batch id. Required if the model is type validation
A prefix capture group used to match tag column
A list of column names specifying tags
A regex like capture group that should be used to match shap column
The name of the rank column in the file. Required if the model is type ranking
The names of fields to be excluded from data ingestion
embeddingFeatures [FileImportEmbeddingInputType!]
The embedding fields that will be ingested Show FileImportEmbeddingInputType fields
The embedding feature name
The name of the vector column
The name of the raw data column
The name of the link to data column
prompt FileImportPromptResponseInputType
The columns which contain the prompt used in a generative LLM. Relevant if the model is generative_llm Show FileImportPromptResponseInputType fields
The name of the vector column
The name of the raw data column. Required if the prompt or response is specified
response FileImportPromptResponseInputType
The columns which contain the response from a generative LLM. Relevant if the model is generative_llm Show FileImportPromptResponseInputType fields
The name of the vector column
The name of the raw data column. Required if the prompt or response is specified
promptTemplate FileImportPromptTemplateInputType
The columns which contain the prompt template fields, such as the template and template version. Relevant if the model is generative_llm Show FileImportPromptTemplateInputType fields
The name of the column which contains the prompt template
The name of the column which contains the prompt template version
llmConfig FileImportLlmConfigInputType
The columns which contain the llm config fields, such as the llm name and params. Relevant if the model is generative_llm Show FileImportLlmConfigInputType fields
The name of the column which contains the llm name
The name of the column which contains a map of llm params to values
runMetadata FileImportRunMetadataInputType
The columns which contain the metadata relevant to the llm call that was used to generate the response using the prompt. Relevant if the model is generative_llm Show FileImportRunMetadataInputType fields
The name of the column which contains the total number of tokens used in the llm call
The name of the column which contains the number of prompt tokens in the llm call
The name of the column which contains the number of response tokens in the llm call
The name of the column which contains the response latency in ms of the llm call
predictionObjectDetectionLabel FileImportObjectDetectionLabelInputType
The columns which contain the object detection prediction fields, such as the bounding box coordinates, categories, and scores. Relevant if the model is object_detection Show FileImportObjectDetectionLabelInputType fields
boundingBoxesCoordinatesColumnName The name of the column which contains the bounding box coordinates
boundingBoxesCategoriesColumnName The name of the column which contains a map of bounding box categories
boundingBoxesScoresColumnName The name of the column which contains a map of bounding box scores
actualObjectDetectionLabel FileImportObjectDetectionLabelInputType
The columns which contain the object detection actual fields, such as the bounding box coordinates, categories, and scores. Relevant if the model is object_detection Show FileImportObjectDetectionLabelInputType fields
boundingBoxesCoordinatesColumnName The name of the column which contains the bounding box coordinates
boundingBoxesCategoriesColumnName The name of the column which contains a map of bounding box categories
boundingBoxesScoresColumnName The name of the column which contains a map of bounding box scores
The name of the change timestamp column in the file.
Returns Scalar fields: id, name, description, uuid, createdAt, liveEnabled, isLLMOnlySpace, playgroundTracingModelId, evalTracingModelId, sandboxTracingModelId, mlModelsEnabled, private, gradientStartColor, gradientEndColor, hasDashboardsCreated. Object fields (select subfields): organization, models, monitors, dashboards, spaceUsers, byobConnectors, importJobs, tableJobs, sandboxJobs, signalInsightSummary, signalEnabledProjectSummary, signalIssues, agentIssues, automations, datasets, experiments, customMetrics, onlineTasks, prompts, tags, evaluators, annotationQueues, playgroundViews, annotationConfigs, traceFilters, llmIntegrations, defaultLlmIntegration, defaultAgentRuntime, gates, _access.
Show TableJobValidationResult fields
Scalar fields: jobId, tableQueryId, queryId, validationStatus. Object fields (select subfields): error.
Show TableImportJob fields
Scalar fields: id, jobId, jobStatus, tableStore, projectId, dataset, createdAt, modelName, modelId, modelType, modelVersion, modelEnvironmentName, batchId, table, totalQueriesSuccessCount, totalQueriesFailedCount, totalQueriesPendingCount. Object fields (select subfields): schema, tableIngestionParameters, queries.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
createTriggeredOngoingTableRun Schedule a job run that will import model inferences from a table
createTriggeredOngoingTableRun(input: CreateTriggeredOngoingTableRunInput!): CreateTriggeredOngoingTableRunPayloadArguments input CreateTriggeredOngoingTableRunInput!
required
Show CreateTriggeredOngoingTableRunInput fields
The job id to schedule a table ingestion run for
The start time for the query
The end time for the query
Returns Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
createIntegrationKey createIntegrationKey(input: CreateIntegrationKeyInput!): CreateIntegrationKeyPayloadArguments input CreateIntegrationKeyInput!
required
Show CreateIntegrationKeyInput fields
The global relay id of the organization to create the integration key in.
providerName IntegrationProvider!
required
The name of an integration provider e.g. opsgeneie, pagerduty, slack One of: opsgenie, pagerduty, slack.
The name of the integration, used to denote a target service or team e.x. ML Team Opsgenie.
The integration’s api key
The priority/severity level when sending alerts to the given integration provider. One of: opsgenieP1, opsgenieP2, opsgenieP3, opsgenieP4, opsgenieP5, pagerdutycritical, pagerdutyerror, pagerdutywarning, pagerdutyinfo.
Returns The created integration key. Show IntegrationKey fields
Scalar fields: id, name, providerName, createdAt, alertSeverity, channelName. Object fields (select subfields): creator.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
patchIntegrationKey patchIntegrationKey(input: PatchIntegrationKeyInput!): PatchIntegrationKeyPayloadArguments input PatchIntegrationKeyInput!
required
Show PatchIntegrationKeyInput fields
The ID of the integration to patch
set IntegrationKeyPatchInput!
required
Patch of the integration Show IntegrationKeyPatchInput fields
The name of the integration, used to denote a target service or team e.x. ML Team Opsgenie.
The integration’s api key
The priority/severity level when sending alerts to the given integration provider. One of: opsgenieP1, opsgenieP2, opsgenieP3, opsgenieP4, opsgenieP5, pagerdutycritical, pagerdutyerror, pagerdutywarning, pagerdutyinfo.
Returns The updated integration. Show IntegrationKey fields
Scalar fields: id, name, providerName, createdAt, alertSeverity, channelName. Object fields (select subfields): creator.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
testIntegrationKey testIntegrationKey(input: TestIntegrationKeyInput!): TestIntegrationKeyPayloadArguments input TestIntegrationKeyInput!
required
Show TestIntegrationKeyInput fields
The global relay id for the integration key to update
Returns The http status code of the test integration endpoint response e.g. 201, 401, etc
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
deleteIntegrationKey deleteIntegrationKey(input: DeleteIntegrationKeyInput!): DeleteIntegrationKeyPayloadArguments input DeleteIntegrationKeyInput!
required
Show DeleteIntegrationKeyInput fields
The global relay id for the integration key to delete
Returns The soft deleted integration key. Show IntegrationKey fields
Scalar fields: id, name, providerName, createdAt, alertSeverity, channelName. Object fields (select subfields): creator.
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
validateByobConnector Validate a byob connector
validateByobConnector(input: ValidateByobConnectorInput!): ValidateByobConnectorPayloadArguments input ValidateByobConnectorInput!
required
Show ValidateByobConnectorInput fields
The id of the space that the byob connector belongs to
connectorIdentifier ByobConnectorIdentifier!
required
The object that identifies the byob connector Show ByobConnectorIdentifier fields
The id of the space that the byob connector belongs to
cloudStorageProvider CloudStorageProvider!
required
The cloud storage provider that the byob connector belongs to One of: GCS, S3, AZURE.
The full path of the bucket that the byob connector belongs to
The Azure tenant ID for Azure Blob Storage connectors
Returns Whether the byob connector is valid
The reason why the byob connector is valid One of: UNKNOWN_INVALID_REASON, FAILED_BUCKET_OWNERSHIP_CHECK, FAILED_BUCKET_ACCESS.
The message that explains why the byob connector is valid
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
createByobConnector Create a byob connector
createByobConnector(input: CreateByobConnectorInput!): CreateByobConnectorPayloadArguments input CreateByobConnectorInput!
required
Show CreateByobConnectorInput fields
The id of the space that the byob connector belongs to
connectorIdentifier ByobConnectorIdentifier!
required
The identifier of the byob connector Show ByobConnectorIdentifier fields
The id of the space that the byob connector belongs to
cloudStorageProvider CloudStorageProvider!
required
The cloud storage provider that the byob connector belongs to One of: GCS, S3, AZURE.
The full path of the bucket that the byob connector belongs to
The name of the byob connector
The external model ids of the byob connector
The table format for the connector (defaults to ICEBERG) One of: ICEBERG, DELTA.
The Azure tenant ID for Azure Blob Storage connectors
Returns The id of the created byob connector
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
updateByobConnector Update a byob connector
updateByobConnector(input: UpdateByobConnectorInput!): UpdateByobConnectorPayloadArguments input UpdateByobConnectorInput!
required
Show UpdateByobConnectorInput fields
The id of the byob connector
The id of the space that the byob connector belongs to
The name of the byob connector
The external model ids of the byob connector
Returns Whether the update was successful
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
deleteByobConnector Delete a byob connector
deleteByobConnector(input: DeleteByobConnectorInput!): DeleteByobConnectorPayloadArguments input DeleteByobConnectorInput!
required
Show DeleteByobConnectorInput fields
The id of the byob connector
The id of the space that the byob connector belongs to
Returns Whether the delete was successful
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
updateByobSyncedDatasource Update a byob synced datasource
updateByobSyncedDatasource(input: UpdateByobSyncedDatasourceInput!): UpdateByobSyncedDatasourcePayloadArguments input UpdateByobSyncedDatasourceInput!
required
Show UpdateByobSyncedDatasourceInput fields
The id of the byob synced datasource
The active status of the byob synced datasource One of: active, paused.
The sync frequency minutes of the byob synced datasource
Returns Whether the update was successful
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
deleteByobDatasource Delete a byob datasource
deleteByobDatasource(input: DeleteByobDatasourceInput!): DeleteByobDatasourcePayloadArguments input DeleteByobDatasourceInput!
required
Show DeleteByobDatasourceInput fields
The id of the byob datasource
The id of the space that the byob datasource belongs to
Returns Whether the delete was successful
Example Only required input fields are shown. Replace <ID> and <string> placeholders with real values; the object graph page shows how to look IDs up.
