> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mellea.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# mellea.core.sampling

> Abstract interfaces for sampling strategies and their results.

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Abstract interfaces for sampling strategies and their results.

`SamplingStrategy` defines the contract for all sampling algorithms: an async
`sample` method that takes an action, context, backend, and requirements, and
returns a `SamplingResult`. `SamplingResult` records the chosen generation
alongside the full history of intermediate samples, their validation outcomes,
and associated contexts — enabling detailed post-hoc inspection of the sampling
process.

## Classes

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### <span className="ml-2 inline-flex items-center rounded-full px-2 py-1 text-[0.7rem] font-bold tracking-wide bg-[#4ADE8033]/20 text-[#15803D]">CLASS</span> `SamplingResult` <sup><a href="https://github.com/generative-computing/mellea/blob/v0.6.0/mellea/core/sampling.py#L26" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

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

**Args:**

* `result_index`: Index into `sample_generations` identifying the chosen final output.
* `success`: Whether the sampling operation produced a passing result.
* `sample_generations`: All output thunks generated during sampling.
* `sample_validations`: Per-generation validation
  results; each inner list contains one tuple per requirement evaluated.
* `sample_actions`: The actions used to produce each generation.
* `sample_contexts`: The contexts associated with each generation.

**Attributes:**

* `result_index`: Index into `sample_generations` identifying the chosen final output.
* `success`: Whether the sampling operation produced a passing result.
* `sample_generations`: All output thunks generated during
  sampling; always a list (`None` input is normalised to `[]`).
* `sample_validations`: Per-generation
  validation results; always a list (`None` input is normalised to `[]`).
* `sample_actions`: The actions used to produce each generation;
  always a list (`None` input is normalised to `[]`).
* `sample_contexts`: The contexts associated with each generation;
  always a list (`None` input is normalised to `[]`).

<div className="h-8" />

**Methods:**

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#### <span className="ml-2 inline-flex items-center rounded-full px-2 py-1 text-[0.7rem] font-bold tracking-wide bg-[#3064E3]/20 text-[#1D4ED8]">FUNC</span> `result` <sup><a href="https://github.com/generative-computing/mellea/blob/v0.6.0/mellea/core/sampling.py#L88" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python theme={null}
result(self) -> ComputedModelOutputThunk[S]
```

The final output or result from applying the sampling strategy.

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#### <span className="ml-2 inline-flex items-center rounded-full px-2 py-1 text-[0.7rem] font-bold tracking-wide bg-[#3064E3]/20 text-[#1D4ED8]">FUNC</span> `result_ctx` <sup><a href="https://github.com/generative-computing/mellea/blob/v0.6.0/mellea/core/sampling.py#L93" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python theme={null}
result_ctx(self) -> Context
```

The context of the final output or result from applying the sampling strategy.

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#### <span className="ml-2 inline-flex items-center rounded-full px-2 py-1 text-[0.7rem] font-bold tracking-wide bg-[#3064E3]/20 text-[#1D4ED8]">FUNC</span> `result_action` <sup><a href="https://github.com/generative-computing/mellea/blob/v0.6.0/mellea/core/sampling.py#L98" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python theme={null}
result_action(self) -> Component[S]
```

The action that generated the final output or result from applying the sampling strategy.

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#### <span className="ml-2 inline-flex items-center rounded-full px-2 py-1 text-[0.7rem] font-bold tracking-wide bg-[#3064E3]/20 text-[#1D4ED8]">FUNC</span> `result_validations` <sup><a href="https://github.com/generative-computing/mellea/blob/v0.6.0/mellea/core/sampling.py#L103" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python theme={null}
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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### <span className="ml-2 inline-flex items-center rounded-full px-2 py-1 text-[0.7rem] font-bold tracking-wide bg-[#4ADE8033]/20 text-[#15803D]">CLASS</span> `SamplingStrategy` <sup><a href="https://github.com/generative-computing/mellea/blob/v0.6.0/mellea/core/sampling.py#L108" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

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.

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**Methods:**

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#### <span className="ml-2 inline-flex items-center rounded-full px-2 py-1 text-[0.7rem] font-bold tracking-wide bg-[#3064E3]/20 text-[#1D4ED8]">FUNC</span> `sample` <sup><a href="https://github.com/generative-computing/mellea/blob/v0.6.0/mellea/core/sampling.py#L116" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python theme={null}
sample(self, action: Component[S], context: Context, backend: Backend, requirements: list[Requirement] | None) -> SamplingResult[S]
```

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:**

* SamplingResult\[S]: A result object indicating the success or failure of the sampling process.

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