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mellea.stdlib.sampling.types

Base types for sampling.

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Classes

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SamplingResult

Stores the results from a sampling operation. This includes successful and failed samplings. Methods:

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result

result(self) -> ModelOutputThunk
The final output or result from applying the sampling strategy.

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result_ctx

result_ctx(self) -> Context
The context of the final output or result from applying the sampling strategy.

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result_action

result_action(self) -> Component
The action that generated the final output or result from applying the sampling strategy.

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result_validations

result_validations(self) -> list[tuple[Requirement, ValidationResult]]
The validation results associated with the final output or result from applying the sampling strategy.

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SamplingStrategy

A SamplingStrategy class defines an abstract base class for implementing various sampling strategies. This class provides a template for creating concrete sampling strategies that can be used to generate model outputs based on given instructions. It allows setting custom validation and generation functions through properties. Methods:

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sample

sample(self, action: Component, context: Context, backend: Backend, requirements: list[Requirement] | None) -> SamplingResult
This method is the abstract method for sampling a given component. It must be implemented by any concrete subclasses to provide specific sampling logic. Args:
  • action : The action object to be sampled.
  • context: The context to be passed to the sampling strategy.
  • backend: The backend used for generating samples.
  • requirements: List of requirements to test against (merged with global requirements).
  • validation_ctx: Optional context to use for validation. If None, validation_ctx = ctx.
  • format: output format for structured outputs.
  • model_options: model options to pass to the backend during generation / validation.
  • tool_calls: True if tool calls should be used during this sampling strategy.
Returns:
  • A result object indicating the success or failure of the sampling process.
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