Module adcp.types.domains.content_standards.calibrate_content_response

Classes

class CalibrateContentResponse1 (**data: Any)

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class CalibrateContentResponse1(AdcpResponse, AdcpVersionEnvelope, ProtocolEnvelope):
    model_config = ConfigDict(extra='allow')
    verdict: binary_verdict_1.BinaryVerdict
    confidence: Annotated[float, Field(ge=0, le=1)] | None = None
    explanation: str | None = None
    features: list[Feature] | None = None
    context: context_1.ContextObject | None = None
    ext: ext_1.ExtensionObject | None = None

The response message of a task in the pinned bundle's task registry.

A consumer holding one can route on task state, pick up an async task_id, split envelope from payload and log uniformly, before knowing which tool answered. Which makes one generic poll-to-terminal loop possible for all 77 tasks, where today each arm has no common type at all.

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

• AdcpResponse
• adcp.types.base._AdcpMessage
• AdcpVersionEnvelope
• ProtocolEnvelope
• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var confidence : float | None

var context : ContextObject | None

var explanation : str | None

var ext : ExtensionObject | None

var features : list[Feature] | None

var model_config

var verdict : BinaryVerdict

Inherited members

• AdcpResponse:
  • get_adcp_error
  • get_context
  • get_message
  • get_replayed
  • get_status
  • get_task_id
• AdcpVersionEnvelope:
  • model_dump
  • model_dump_json
  • model_summary

class CalibrateContentResponse2 (**data: Any)

Expand source code

class CalibrateContentResponse2(AdcpResponse, AdcpVersionEnvelope, ProtocolEnvelope):
    model_config = ConfigDict(extra='allow')
    errors: list[error_1.Error]
    context: context_1.ContextObject | None = None
    ext: ext_1.ExtensionObject | None = None

The response message of a task in the pinned bundle's task registry.

A consumer holding one can route on task state, pick up an async task_id, split envelope from payload and log uniformly, before knowing which tool answered. Which makes one generic poll-to-terminal loop possible for all 77 tasks, where today each arm has no common type at all.

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

• AdcpResponse
• adcp.types.base._AdcpMessage
• AdcpVersionEnvelope
• ProtocolEnvelope
• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var context : ContextObject | None

var errors : list[Error]

var ext : ExtensionObject | None

var model_config

Inherited members

• AdcpResponse:
  • get_adcp_error
  • get_context
  • get_message
  • get_replayed
  • get_status
  • get_task_id
• AdcpVersionEnvelope:
  • model_dump
  • model_dump_json
  • model_summary

class Feature (**data: Any)

Expand source code

class Feature(AdcpVersionEnvelope):
    model_config = ConfigDict(extra='allow')
    feature_id: str
    status: feature_check_status_1.FeatureCheckStatus
    policy_id: str | None = None
    explanation: str | None = None
    confidence: Annotated[float, Field(ge=0, le=1)] | None = 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

• AdcpVersionEnvelope
• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var confidence : float | None

var explanation : str | None

var feature_id : str

var model_config

var policy_id : str | None

var status : FeatureCheckStatus

Inherited members

• AdcpVersionEnvelope:
  • model_dump
  • model_dump_json
  • model_summary