nat.experimental.test_time_compute.models.scoring_config#

Classes#

`LLMBasedPlanScoringConfig`	Configuration for LLMBasedScoring.
`LLMBasedAgentScoringConfig`	Configuration for LLMBasedScoring.
`MotivationAwareScoringConfig`	Configuration for a scoring strategy that considers both the original input (task)

class LLMBasedPlanScoringConfig(/, **data: Any)#

Configuration for LLMBasedScoring.

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.

validate_strategies(values: dict[str, Any]) → dict[str, Any]#: Ensure that the scoring_llm is provided when using LLMBasedScoring.

class LLMBasedAgentScoringConfig(/, **data: Any)#

Configuration for LLMBasedScoring.

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.

validate_strategies(values: dict[str, Any]) → dict[str, Any]#: Ensure that the scoring_llm is provided when using LLMBasedScoring.

class MotivationAwareScoringConfig(/, **data: Any)#

Configuration for a scoring strategy that considers both the original input (task) and the motivation (from metadata) along with the current output.

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.