nat.experimental.test_time_compute.models.selection_config#

Classes#

`LLMBasedPlanSelectionConfig`	Configuration for LLMBasedSelection.
`LLMBasedAgentOutputSelectionConfig`	Configuration for LLMBasedSelection.
`LLMBasedOutputMergingConfig`	Configuration for LLMBasedSelection.
`ThresholdSelectionConfig`	Configuration for a selection strategy that keeps only the items
`BestOfNSelectionConfig`	Configuration for Best of N Selection

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

Configuration for LLMBasedSelection.

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 selection_llm is provided when using LLMBasedSelection.

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

Configuration for LLMBasedSelection.

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 selection_llm is provided when using LLMBasedSelection.

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

Configuration for LLMBasedSelection.

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 selection_llm is provided when using LLMBasedSelection.

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

Configuration for a selection strategy that keeps only the items whose scores exceed a specified threshold.

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.

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

Configuration for Best of N Selection

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.