Validation

When using Benchling to store essential data, it is important to validate the data to ensure accuracy and consistency. Liminal provides an easy way to define "validators" on entity schemas that ensure entities adhere to your defined business logic. These validators can be run easily by calling the validate() method on the entity schema.

Note

Warehouse access is required to run validators.

Defining a Liminal Validator decorator

Any functions decorated with liminal_validator are detected as validators for the entity schema. Each validator returns a BenchlingValidatorReport object per entity it is run on, with either valid=True or valid=False. If no errors are raised when it is run on an entity, the report will be valid. If an error is raised, the report will be invalid.

from liminal.validation import ValidationSeverity, liminal_validator

class Pizza(BaseModel, CustomEntityMixin):
    ...

    @liminal_validator
    def cook_time_and_temp_validator(self) -> None:
        if self.cook_time is not None and self.cook_temp is None:
            raise ValueError("Cook temp is required if cook time is set")
        if self.cook_time is None and self.cook_temp is not None:
            raise ValueError("Cook time is required if cook temp is set")

Parameters

• validator_level: ValidationSeverity

  The severity of the validator. Defaults to ValidationSeverity.LOW.

• validator_name: str | None

  The name of the validator. Defaults to converting the function name to PascalCase.

Running Validation

To run validation using Liminal, call the validate() method on the entity schema:

with BenchlingSession(benchling_connection, with_db=True) as session:
    reports = Pizza.validate(session, only_invalid=True)

Tip

The validate_to_df method returns a pandas DataFrame with all the reports. For example:

with BenchlingSession(benchling_connection, with_db=True) as session:
    df = Pizza.validate_to_df(session, only_invalid=True)

Parameters

• session : Session

  The Benchling database session.

• base_filters: BaseValidatorFilters | None

  Filters to apply to the query.

• only_invalid: bool

  If True, only returns reports for entities that failed validation.

Returns

• list[BenchlingValidatorReport]

  List of reports from running all validators on all entities returned from the query.

BenchlingValidatorReport: [class](https://github.com/dynotx/liminal-orm/blob/main/liminal/validation/init.py#L13

These reports are filled out by the liminal_validator decorator behind the scenes.

Parameters

• valid : bool

  Indicates whether the validation passed or failed.

• model : str

  The name of the model being validated. (eg: Pizza)

• level : ValidationSeverity

  The severity level of the validation report.

• validator_name : str | None

  The name of the validator that generated this report. (eg: CookTimeAndTempValidator)

• entity_id : str | None

  The entity ID of the entity being validated.

• registry_id : str | None

  The registry ID of the entity being validated.

• entity_name : str | None

  The name of the entity being validated.

• message : str | None

  A message describing the result of the validation.

• creator_name : str | None

  The name of the creator of the entity being validated.

• creator_email : str | None

  The email of the creator of the entity being validated.

• updated_date : datetime | None

  The date the entity was last updated.

BaseValidatorFilters: class

This class is used to pass base filters to benchling warehouse database queries. These columns are found on all tables in the benchling warehouse database.

Parameters

• created_date_start: date | None

  Start date for created date filter.

• created_date_end: date | None

  End date for created date filter.

• updated_date_start: date | None

  Start date for updated date filter.

• updated_date_end: date | None

  End date for updated date filter.

• entity_ids: list[str] | None

  List of entity IDs to filter by.

• creator_full_names: list[str] | None

  List of creator full names to filter by.