Module adcp.types.generated_poc.signals.get_signals_response
Classes
class GetSignalsResponse (**data: Any)-
Expand source code
class GetSignalsResponse(AdCPBaseModel): model_config = ConfigDict( extra='allow', ) signals: Annotated[list[Signal], Field(description='Array of matching signals')] errors: Annotated[ list[error.Error] | None, Field( description='Task-specific errors and warnings (e.g., signal discovery or pricing issues)' ), ] = None pagination: pagination_response.PaginationResponse | None = None sandbox: Annotated[ bool | None, Field(description='When true, this response contains simulated data from sandbox mode.'), ] = None context: context_1.ContextObject | None = None ext: ext_1.ExtensionObject | None = NoneBase model for AdCP types with spec-compliant serialization.
Defaults to
extra='ignore'so that unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas setadditionalProperties: trueoverride this withextra='allow'in their ownmodel_config. Consumers who want strict validation can override withextra='forbid'.Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.Ancestors
- AdCPBaseModel
- pydantic.main.BaseModel
Class variables
var context : ContextObject | Nonevar errors : list[Error] | Nonevar ext : ExtensionObject | Nonevar model_configvar pagination : PaginationResponse | Nonevar sandbox : bool | Nonevar signals : list[Signal]
Inherited members
class Range (**data: Any)-
Expand source code
class Range(AdCPBaseModel): model_config = ConfigDict( extra='forbid', ) min: Annotated[float, Field(description='Minimum value (inclusive)')] max: Annotated[float, Field(description='Maximum value (inclusive)')]Base model for AdCP types with spec-compliant serialization.
Defaults to
extra='ignore'so that unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas setadditionalProperties: trueoverride this withextra='allow'in their ownmodel_config. Consumers who want strict validation can override withextra='forbid'.Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.Ancestors
- AdCPBaseModel
- pydantic.main.BaseModel
Class variables
var max : floatvar min : floatvar model_config
Inherited members
class Signal (**data: Any)-
Expand source code
class Signal(AdCPBaseModel): model_config = ConfigDict( extra='allow', ) signal_id: Annotated[ signal_id_1.SignalId | None, Field( description="Universal signal identifier referencing the data provider's catalog. Use this to verify authorization and look up signal definitions." ), ] = None signal_agent_segment_id: Annotated[ str, Field( description="Opaque identifier used for activation. This is the signals agent's internal segment ID." ), ] name: Annotated[str, Field(description='Human-readable signal name')] description: Annotated[str, Field(description='Detailed signal description')] value_type: Annotated[ signal_value_type.SignalValueType | None, Field(description="The data type of this signal's values (binary, categorical, numeric)"), ] = None categories: Annotated[ list[str] | None, Field( description="Valid values for categorical signals. Present when value_type is 'categorical'. Buyers must use one of these values in SignalTargeting.values." ), ] = None range: Annotated[ Range | None, Field(description="Valid range for numeric signals. Present when value_type is 'numeric'."), ] = None signal_type: Annotated[ signal_catalog_type.SignalCatalogType, Field(description='Catalog type of signal (marketplace, custom, owned)'), ] data_provider: Annotated[str, Field(description='Human-readable name of the data provider')] coverage_percentage: Annotated[ float, Field(description='Percentage of audience coverage', ge=0.0, le=100.0) ] deployments: Annotated[ list[deployment.Deployment], Field(description='Array of deployment targets') ] pricing_options: Annotated[ list[signal_pricing_option.SignalPricingOption], Field( description='Pricing options available for this signal. The buyer selects one and passes its pricing_option_id in report_usage for billing verification.', min_length=1, ), ]Base model for AdCP types with spec-compliant serialization.
Defaults to
extra='ignore'so that unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas setadditionalProperties: trueoverride this withextra='allow'in their ownmodel_config. Consumers who want strict validation can override withextra='forbid'.Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.selfis explicitly positional-only to allowselfas a field name.Ancestors
- AdCPBaseModel
- pydantic.main.BaseModel
Class variables
var categories : list[str] | Nonevar coverage_percentage : floatvar data_provider : strvar deployments : list[Deployment]var description : strvar model_configvar name : strvar pricing_options : list[SignalPricingOption]var range : Range | Nonevar signal_agent_segment_id : strvar signal_id : SignalId | Nonevar signal_type : SignalCatalogTypevar value_type : SignalValueType | None
Inherited members