# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from __future__ import annotations
import logging
import os
import uuid
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
from typing import Dict, Optional, Sequence, Union
from jinja2 import BaseLoader, Environment, StrictUndefined, Template, Undefined
from tinytag import TinyTag
from pyrit.common.path import PATHS_DICT
from pyrit.common.yaml_loadable import YamlLoadable
from pyrit.models import DataTypeSerializer
from pyrit.models.literals import ChatMessageRole, PromptDataType
logger = logging.getLogger(__name__)
class PartialUndefined(Undefined):
# Return the original placeholder format
def __str__(self):
return f"{{{{ {self._undefined_name} }}}}" if self._undefined_name else ""
def __repr__(self):
return f"{{{{ {self._undefined_name} }}}}" if self._undefined_name else ""
def __iter__(self):
"""Prevent Jinja from evaluating loops by returning a placeholder string instead of an iterable."""
return self
def __bool__(self):
return True # Ensures it doesn't evaluate to False
[docs]
@dataclass
class SeedPrompt(YamlLoadable):
"""Represents a seed prompt with various attributes and metadata."""
# The actual prompt value, which can be a string or a file path
value: str
# SHA256 hash of the value, used for deduplication
value_sha256: Optional[str] = None
# The type of data this prompt represents (e.g., text, image, audio, video)
data_type: Optional[PromptDataType] = None
# Unique identifier for the prompt
id: Optional[uuid.UUID] = field(default_factory=lambda: uuid.uuid4())
# Name of the prompt
name: Optional[str] = None
# Name of the dataset this prompt belongs to
dataset_name: Optional[str] = None
# Categories of harm associated with this prompt
harm_categories: Optional[Sequence[str]] = field(default_factory=lambda: [])
# Description of the prompt
description: Optional[str] = None
# Authors of the prompt
authors: Optional[Sequence[str]] = field(default_factory=lambda: [])
# Groups affiliated with the prompt
groups: Optional[Sequence[str]] = field(default_factory=lambda: [])
# Source of the prompt
source: Optional[str] = None
# Date when the prompt was added to the dataset
date_added: Optional[datetime] = field(default_factory=lambda: datetime.now())
# User who added the prompt to the dataset
added_by: Optional[str] = None
# Arbitrary metadata that can be attached to the prompt
metadata: Optional[Dict[str, Union[str, int]]] = field(default_factory=lambda: {})
# Parameters that can be used in the prompt template
parameters: Optional[Sequence[str]] = field(default_factory=lambda: [])
# Unique identifier for the prompt group
prompt_group_id: Optional[uuid.UUID] = None
# Alias for the prompt group
prompt_group_alias: Optional[str] = None
# Role of the prompt in a conversation (e.g., "user", "assistant")
role: Optional[ChatMessageRole] = None
# Sequence number for ordering prompts in a conversation, prompts with
# the same sequence number are grouped together if they also share the same prompt_group_id
sequence: Optional[int] = 0
def __post_init__(self) -> None:
"""Post-initialization to render the template to replace existing values"""
self.value = self.render_template_value_silent(**PATHS_DICT)
if not self.data_type:
# If data_type is not provided, infer it from the value
# Note: Does not assign 'error' or 'url' implicitly
if os.path.isfile(self.value):
_, ext = os.path.splitext(self.value)
ext = ext.lstrip(".")
if ext in ["mp4", "avi", "mov", "mkv", "ogv", "flv", "wmv", "webm"]:
self.data_type = "video_path"
elif ext in ["flac", "mp3", "mpeg", "mpga", "m4a", "ogg", "wav"]:
self.data_type = "audio_path"
elif ext in ["jpg", "jpeg", "png", "gif", "bmp", "tiff", "tif"]:
self.data_type = "image_path"
else:
raise ValueError(f"Unable to infer data_type from file extension: {ext}")
else:
self.data_type = "text"
[docs]
def render_template_value(self, **kwargs) -> str:
"""Renders self.value as a template, applying provided parameters in kwargs
Args:
kwargs:Key-value pairs to replace in the SeedPrompt value.
Returns:
A new prompt with the parameters applied.
Raises:
ValueError: If parameters are missing or invalid in the template.
"""
jinja_template = Template(self.value, undefined=StrictUndefined)
try:
return jinja_template.render(**kwargs)
except Exception as e:
raise ValueError(f"Error applying parameters: {str(e)}")
[docs]
def render_template_value_silent(self, **kwargs) -> str:
"""Renders self.value as a template, applying provided parameters in kwargs. For parameters in the template
that are not provided as kwargs here, this function will leave them as is instead of raising an error.
Args:
kwargs: Key-value pairs to replace in the SeedPrompt value.
Returns:
A new prompt with the parameters applied.
Raises:
ValueError: If parameters are missing or invalid in the template.
"""
# Create a Jinja template with PartialUndefined placeholders
env = Environment(loader=BaseLoader, undefined=PartialUndefined) # type: ignore
jinja_template = env.from_string(self.value)
try:
# Render the template with the provided kwargs
return jinja_template.render(**kwargs)
except Exception as e:
logging.error("Error rendering template: %s", e)
return self.value
[docs]
async def set_sha256_value_async(self):
"""
This method computes the SHA256 hash value asynchronously.
It should be called after prompt `value` is serialized to text,
as file paths used in the `value` may have changed from local to memory storage paths.
Note, this method is async due to the blob retrieval. And because of that, we opted
to take it out of main and setter functions. The disadvantage is that it must be explicitly called.
"""
from pyrit.models.data_type_serializer import data_serializer_factory
original_serializer = data_serializer_factory(
category="seed-prompt-entries", data_type=self.data_type, value=self.value
)
self.value_sha256 = await original_serializer.get_sha256()
[docs]
@classmethod
def from_yaml_with_required_parameters(
cls, template_path: Union[str, Path], required_parameters: list[str], error_message: Optional[str] = None
) -> "SeedPrompt":
"""
Load a SeedPrompt from a YAML file and validate that it contains specific parameters.
Args:
template_path: Path to the YAML file containing the template.
required_parameters: List of parameter names that must exist in the template.
error_message: Custom error message if validation fails. If None, a default message is used.
Returns:
SeedPrompt: The loaded and validated seed prompt.
Raises:
ValueError: If the template doesn't contain all required parameters.
"""
sp = cls.from_yaml_file(template_path)
if sp.parameters is None or not all(param in sp.parameters for param in required_parameters):
if error_message is None:
error_message = f"Template must have these parameters: {', '.join(required_parameters)}"
raise ValueError(f"{error_message}: '{sp}'")
return sp
