Overview Use Cases Connectors Authentication Getting Started with the Python SDK API Reference ai_agents ai_chat algorithm annotations application_connectors batch_prediction code_completion custom_loss_function custom_metric database_connectors dataset deployment deployment_conversations docstore document_retrievers documentation eda external_app feature_drift feature_group feature_group_row_process feature_group_template file_connectors graph_dashboard holdout_analysis llm_apps model model_monitoring module natural_language_explanation organization pipelines predict prediction_operator project addFeatureGroupToProject addProjectTags createProject deleteProject describeProject describeUseCaseRequirements getProjectFeatureGroupConfig listProjects listUseCases removeFeatureGroupFromProject removeProjectTags renameProject setFeatureGroupType setFeatureMapping setProjectFeatureGroupConfig validateProject python_function refresh secrets streaming streaming_connectors upload webhooks API Classes API Inputs Documentation Chat Bot API Search Video Tutorials ChatLLM

Method

validateProject GET

Copy GET

Validates that the specified project has all required feature group types for its use case and that all required feature columns are set.

Arguments:

REQUIRED	KEY	TYPE	DESCRIPTION
Yes	projectId	str	The unique ID associated with the project.
No	featureGroupIds	List[str]	The list of feature group IDs to validate.

Note: The arguments for the API methods follow camelCase but for Python SDK underscore_case is followed.

Response:

KEY

TYPE

DESCRIPTION

success

Boolean

true if the call succeeded, false if there was an error

result

ProjectValidation

KEY	TYPE	Description
valid	bool	`true` if the project is valid and ready to be trained, otherwise `false`.
datasetErrors	List[dict]	A list of errors keeping the dataset from being valid
columnHints	dict	Hints for what to set on the columns

Exceptions:

TYPE	WHEN
DataNotFoundError	`projectId` is not found.
DataNotFoundError	`featureGroupIds` is not found.

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