nat.experimental.test_time_compute.scoring.llm_based_plan_scorer#

Attributes#

logger

Classes#

LLMBasedPlanScorer

Functions#

register_llm_based_plan_scorer(config, builder)

Register the LLM-based plan scorer strategy.

Module Contents#

logger#
class LLMBasedPlanScorer(
config: nat.data_models.ttc_strategy.TTCStrategyBaseConfig,
)#

Bases: nat.experimental.test_time_compute.models.strategy_base.StrategyBase

llm_bound = None#
async build_components(builder: nat.builder.builder.Builder) → None#

Build the components required for the planner.

supported_pipeline_types() → [nat.experimental.test_time_compute.models.stage_enums.PipelineTypeEnum]#
stage_type() → nat.experimental.test_time_compute.models.stage_enums.StageTypeEnum#
async score_single(
original_prompt: str,
agent_context: str,
planning_item: nat.experimental.test_time_compute.models.ttc_item.TTCItem,
) → float#

Score a single planning item using the LLM.

Args:

original_prompt (str): The original prompt. agent_context (str): The agent context. planning_item (TTCItem): The item to score.

Returns:

float: The score of the item.

async ainvoke(items: list[nat.experimental.test_time_compute.models.ttc_item.TTCItem], original_prompt: str | None = None, agent_context: str | None = None, \*\*kwargs) → list[nat.experimental.test_time_compute.models.ttc_item.TTCItem]#

Score a list of planning items.

Args:

original_prompt (str): The original prompt. agent_context (str): The agent context. items (list[TTCItem]): The list of planning items to score.

Returns:

list[float]: A list of scores corresponding to each planning item.

async register_llm_based_plan_scorer(
config: nat.experimental.test_time_compute.models.scoring_config.LLMBasedPlanScoringConfig,
builder: nat.builder.builder.Builder,
)#

Register the LLM-based plan scorer strategy.

Args:

config (LLMBasedPlanScoringConfig): The configuration for the strategy. builder (Builder): The builder instance.

Returns:

LLMBasedPlanScorer: The registered LLM-based plan scorer.