Module adcp.types.domains.core.deployment

Classes

class Deployment1 (**data: Any)

Expand source code

class Deployment1(AdCPBaseModel):
    model_config = ConfigDict(
        extra='allow',
    )
    type: Annotated[
        Literal['platform'],
        Field(description='Discriminator indicating this is a platform-based deployment'),
    ] = 'platform'
    platform: Annotated[str, Field(description='Platform identifier for DSPs')]
    account: Annotated[str | None, Field(description='Account identifier if applicable')] = None
    is_live: Annotated[
        StrictBool, Field(description='Whether signal is currently active on this deployment')
    ]
    activation_key: Annotated[
        activation_key_1.ActivationKey | None,
        Field(
            description='The key to use for targeting. Only present if is_live=true AND requester has access to this deployment.'
        ),
    ] = None
    estimated_activation_duration_minutes: Annotated[
        StrictFloat | None,
        Field(
            description='Estimated time to activate if not live, or to complete activation if in progress',
            ge=0.0,
        ),
    ] = None
    deployed_at: Annotated[
        AwareDatetime | None,
        Field(description='Timestamp when activation completed (if is_live=true)'),
    ] = None

Base model for AdCP types with spec-compliant serialization.

Defaults to extra='ignore' so unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas set additionalProperties: true override this with extra='allow' in their own model_config.

Set ADCP_STRICT_VALIDATION=1 in the environment ("1", "true", "yes", "on" are accepted) to flip the default to extra='forbid'. Use this during spec upgrades to catch silently-dropped renamed fields in tests. See :func:_resolve_extra_policy.

Important

The env var is resolved once at module import time. Set it in your shell or CI environment before import adcp runs — mutating os.environ["ADCP_STRICT_VALIDATION"] after the first adcp import has no effect on already-imported model classes (they captured the policy at class-body evaluation).

Consumers who want per-model strict validation can override model_config on their subclass.

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.

self is explicitly positional-only to allow self as a field name.

Ancestors

• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var account : str | None

var activation_key : ActivationKey1 | ActivationKey2 | None

var deployed_at : pydantic.types.AwareDatetime | None

var estimated_activation_duration_minutes : float | None

var is_live : bool

var model_config

var platform : str

var type : Literal['platform']

Inherited members

• AdCPBaseModel:
  • model_dump
  • model_dump_json
  • model_summary

class Deployment2 (**data: Any)

Expand source code

class Deployment2(AdCPBaseModel):
    model_config = ConfigDict(
        extra='allow',
    )
    type: Annotated[
        Literal['agent'],
        Field(description='Discriminator indicating this is an agent URL-based deployment'),
    ] = 'agent'
    agent_url: Annotated[AnyUrl, Field(description='URL identifying the deployment agent')]
    account: Annotated[str | None, Field(description='Account identifier if applicable')] = None
    is_live: Annotated[
        StrictBool, Field(description='Whether signal is currently active on this deployment')
    ]
    activation_key: Annotated[
        activation_key_1.ActivationKey | None,
        Field(
            description='The key to use for targeting. Only present if is_live=true AND requester has access to this deployment.'
        ),
    ] = None
    estimated_activation_duration_minutes: Annotated[
        StrictFloat | None,
        Field(
            description='Estimated time to activate if not live, or to complete activation if in progress',
            ge=0.0,
        ),
    ] = None
    deployed_at: Annotated[
        AwareDatetime | None,
        Field(description='Timestamp when activation completed (if is_live=true)'),
    ] = None

Base model for AdCP types with spec-compliant serialization.

Defaults to extra='ignore' so unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas set additionalProperties: true override this with extra='allow' in their own model_config.

Set ADCP_STRICT_VALIDATION=1 in the environment ("1", "true", "yes", "on" are accepted) to flip the default to extra='forbid'. Use this during spec upgrades to catch silently-dropped renamed fields in tests. See :func:_resolve_extra_policy.

Important

The env var is resolved once at module import time. Set it in your shell or CI environment before import adcp runs — mutating os.environ["ADCP_STRICT_VALIDATION"] after the first adcp import has no effect on already-imported model classes (they captured the policy at class-body evaluation).

Consumers who want per-model strict validation can override model_config on their subclass.

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.

self is explicitly positional-only to allow self as a field name.

Ancestors

• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var account : str | None

var activation_key : ActivationKey1 | ActivationKey2 | None

var agent_url : pydantic.networks.AnyUrl

var deployed_at : pydantic.types.AwareDatetime | None

var estimated_activation_duration_minutes : float | None

var is_live : bool

var model_config

var type : Literal['agent']

Inherited members

• AdCPBaseModel:
  • model_dump
  • model_dump_json
  • model_summary