nat.experimental.test_time_compute.editing.motivation_aware_summarization#

Attributes#

logger

Classes#

MotivationAwareSummarization

A strategy that, for each incoming TTCItem, summarizes the output based on input

Functions#

register_multi_query_retrieval_search(config, builder)

Module Contents#

logger#
class MotivationAwareSummarization(
config: nat.experimental.test_time_compute.models.editor_config.MotivationAwareSummarizationConfig,
)#

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

A strategy that, for each incoming TTCItem, summarizes the output based on input and motivation.

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

Binds each LLMRef in self.config.llms to an actual LLM client.

supported_pipeline_types() → list[nat.experimental.test_time_compute.models.stage_enums.PipelineTypeEnum]#

Return the stage types supported by this selector.

stage_type() → nat.experimental.test_time_compute.models.stage_enums.StageTypeEnum#

Return the stage type of this strategy.

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]#

For each TTCItem, rewrite the ‘input’ using each LLM to create a new perspective. The new TTCItems’ ‘output’ field will store the newly generated query.