pyrit.models.SeedDataset

pyrit.models.SeedDataset#

class SeedDataset(*, prompts: Sequence[Dict[str, Any]] | Sequence[Seed] | None = None, data_type: Literal['text', 'image_path', 'audio_path', 'video_path', 'url', 'reasoning', 'error', 'function_call', 'tool_call', 'function_call_output'] | None = 'text', name: str | None = None, dataset_name: str | None = None, harm_categories: Sequence[str] | None = None, description: str | None = None, authors: Sequence[str] | None = None, groups: Sequence[str] | None = None, source: str | None = None, date_added: datetime | None = None, added_by: str | None = None)[source]#

Bases: YamlLoadable

SeedDataset manages seed prompts plus optional top-level defaults. Prompts are stored as a Sequence[Seed], so references to prompt properties are straightforward (e.g. ds.prompts[0].value).

__init__(*, prompts: Sequence[Dict[str, Any]] | Sequence[Seed] | None = None, data_type: Literal['text', 'image_path', 'audio_path', 'video_path', 'url', 'reasoning', 'error', 'function_call', 'tool_call', 'function_call_output'] | None = 'text', name: str | None = None, dataset_name: str | None = None, harm_categories: Sequence[str] | None = None, description: str | None = None, authors: Sequence[str] | None = None, groups: Sequence[str] | None = None, source: str | None = None, date_added: datetime | None = None, added_by: str | None = None)[source]#

Initialize the dataset. Typically, you’ll call from_dict or from_yaml_file so that top-level defaults are merged into each prompt. If you’re passing prompts directly, they can be either a list of SeedPrompt objects or prompt dictionaries (which then get converted to SeedPrompt objects).

Methods

__init__(*[, prompts, data_type, name, ...])

Initialize the dataset.

from_dict(data)

Builds a SeedDataset by merging top-level defaults into each item in 'prompts'.

from_yaml_file(file)

Creates a new object from a YAML file.

get_random_values(*, number[, harm_categories])

Extracts and returns a list of random prompt values from the dataset.

get_values(*[, first, last, harm_categories])

Extracts and returns a list of prompt values from the dataset.

group_seed_prompts_by_prompt_group_id(seed)

Groups the given list of Seeds by their prompt_group_id and creates SeedGroup instances.

render_template_value(**kwargs)

Renders self.value as a template, applying provided parameters in kwargs

Attributes

data_type

name

dataset_name

harm_categories

description

authors

groups

source

date_added

added_by

prompts

objectives

added_by: str | None#
authors: Sequence[str] | None#
data_type: str | None#
dataset_name: str | None#
date_added: datetime | None#
description: str | None#
classmethod from_dict(data: Dict[str, Any]) → SeedDataset[source]#

Builds a SeedDataset by merging top-level defaults into each item in ‘prompts’.

get_random_values(*, number: Annotated[int, Gt(gt=0)], harm_categories: Sequence[str] | None = None) → Sequence[str][source]#

Extracts and returns a list of random prompt values from the dataset.

Parameters:

  • number (int) – The number of random prompt values to return.

  • harm_categories (Optional[Sequence[str]]) – If provided, only prompts containing at least one of these harm categories are included.

Returns:

A list of prompt values.

Return type:

Sequence[str]

get_values(*, first: Annotated[int, Gt(gt=0)] | None = None, last: Annotated[int, Gt(gt=0)] | None = None, harm_categories: Sequence[str] | None = None) → Sequence[str][source]#

Extracts and returns a list of prompt values from the dataset. By default, returns all of them.

Parameters:

  • first (Optional[int]) – If provided, values from the first N prompts are included.

  • last (Optional[int]) – If provided, values from the last N prompts are included.

  • harm_categories (Optional[Sequence[str]]) – If provided, only prompts containing at least one of these harm categories are included.

Returns:

A list of prompt values.

Return type:

Sequence[str]

static group_seed_prompts_by_prompt_group_id(seed: Sequence[Seed]) → Sequence[SeedGroup][source]#

Groups the given list of Seeds by their prompt_group_id and creates SeedGroup instances. All seed prompts in a group must share the same prompt_group_id

Parameters:

seed – A list of Seed objects.

Returns:

A list of SeedGroup objects, with prompts grouped by prompt_group_id. Each SeedGroup will be ordered by the sequence number of the prompts, if available.

groups: Sequence[str] | None#
harm_categories: Sequence[str] | None#
name: str | None#
objectives: Sequence[SeedObjective]#
prompts: Sequence[SeedPrompt]#
render_template_value(**kwargs)[source]#

Renders self.value as a template, applying provided parameters in kwargs

Parameters:

kwargs – Key-value pairs to replace in the SeedDataset value.

Returns:

None

Raises:

ValueError – If parameters are missing or invalid in the template.

source: str | None#
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