> For the complete documentation index, see [llms.txt](https://docs.mozilla.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.mozilla.ai/any-guardrail/api-reference/types.md).

# Types

Runtime-validated wrappers used throughout the pipeline and the output type returned by every guardrail.

A machine-readable JSON Schema for `GuardrailOutput` (generated from these models) is published at <https://raw.githubusercontent.com/mozilla-ai/any-guardrail/main/schemas/guardrail_output.schema.json>. Pin a release tag in the URL for a specific version.

## GuardrailOutput

Represents the output of a guardrail evaluation with runtime validation.

This is the single concrete result shape shared by every guardrail implementation, so application code can swap guardrails without changes.

Example: >>> output = GuardrailOutput(valid=True, explanation="Content is safe", score=0.05) >>> output.valid True

| Field           | Type                   | Description |
| --------------- | ---------------------- | ----------- |
| `valid`         | `bool`                 |             |
| `explanation`   | \`str                  | None\`      |
| `score`         | \`float                | None\`      |
| `categories`    | `list[CategoryResult]` |             |
| `spans`         | \`list\[SpanResult]    | None\`      |
| `modified_text` | \`str                  | None\`      |
| `action`        | \`str                  | None\`      |
| `usage`         | \`GuardrailUsage       | None\`      |
| `extra`         | \`dict\[str, Any]      | None\`      |
| `raw`           | \`Any                  | None\`      |

## CategoryResult

Result for one taxonomy category evaluated by a guardrail.

Categories give multi-label and taxonomy guardrails (DuoGuard, Llama Guard S-codes, Azure severities, binary classifiers' label distributions) a lossless, uniform home instead of overloading `explanation`.

Example: >>> CategoryResult(name="S1", description="Violent Crimes", triggered=True)

| Field         | Type    | Description |
| ------------- | ------- | ----------- |
| `name`        | `str`   |             |
| `description` | \`str   | None\`      |
| `triggered`   | \`bool  | None\`      |
| `score`       | \`float | None\`      |
| `severity`    | \`int   | None\`      |

## SpanResult

A character span flagged by a guardrail.

Reserved for span-producing guardrails (PII/NER detection, span-level toxicity, token classification). Offsets are zero-based character indices into the validated text.

| Field   | Type    | Description |
| ------- | ------- | ----------- |
| `start` | `int`   |             |
| `end`   | `int`   |             |
| `text`  | \`str   | None\`      |
| `label` | \`str   | None\`      |
| `score` | \`float | None\`      |

## GuardrailUsage

Provenance and cost envelope for one `validate()` call.

| Field               | Type    | Description |
| ------------------- | ------- | ----------- |
| `model_id`          | \`str   | None\`      |
| `latency_ms`        | \`float | None\`      |
| `prompt_tokens`     | \`int   | None\`      |
| `completion_tokens` | \`int   | None\`      |

## GuardrailPreprocessOutput

Wrapper for preprocessing outputs with runtime validation.

This class wraps the output of the preprocessing stage, providing runtime validation and a consistent interface across all guardrail implementations.

Type Parameters: PreprocessT: The type of the preprocessing result (e.g., tokenized input, API options, chat messages).

Example: >>> output = GuardrailPreprocessOutput(data={"input\_ids": tensor, "attention\_mask": tensor}) >>> output.data\["input\_ids"]

| Field  | Type           | Description |
| ------ | -------------- | ----------- |
| `data` | `~PreprocessT` |             |

## GuardrailInferenceOutput

Wrapper for inference outputs with runtime validation.

This class wraps the output of the inference stage, providing runtime validation and a consistent interface across all guardrail implementations.

Type Parameters: InferenceT: The type of the inference result (e.g., model logits, API response, generated tokens).

Example: >>> output = GuardrailInferenceOutput(data=model\_output) >>> logits = output.data\["logits"]

| Field  | Type          | Description |
| ------ | ------------- | ----------- |
| `data` | `~InferenceT` |             |


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