# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import uuid
from typing import List, Literal, Optional, Union
from pydantic import BaseModel, ConfigDict
from sqlalchemy import (
ARRAY,
INTEGER,
JSON,
DateTime,
Float,
ForeignKey,
Index,
String,
Unicode,
)
from sqlalchemy.orm import ( # type: ignore
DeclarativeBase,
Mapped,
mapped_column,
relationship,
)
from sqlalchemy.types import Uuid # type: ignore
from pyrit.models import PromptDataType, PromptRequestPiece, Score, SeedPrompt
class Base(DeclarativeBase):
pass
[docs]
class PromptMemoryEntry(Base):
"""
Represents the prompt data.
Because of the nature of database and sql alchemy, type ignores are abundant :)
Parameters:
__tablename__ (str): The name of the database table.
__table_args__ (dict): Additional arguments for the database table.
id (Uuid): The unique identifier for the memory entry.
role (PromptType): system, assistant, user
conversation_id (str): The identifier for the conversation which is associated with a single target.
sequence (int): The order of the conversation within a conversation_id.
Can be the same number for multi-part requests or multi-part responses.
timestamp (DateTime): The timestamp of the memory entry.
labels (Dict[str, str]): The labels associated with the memory entry. Several can be standardized.
prompt_metadata (JSON): The metadata associated with the prompt. This can be specific to any scenarios.
Because memory is how components talk with each other, this can be component specific.
e.g. the URI from a file uploaded to a blob store, or a document type you want to upload.
converters (list[PromptConverter]): The converters for the prompt.
prompt_target (PromptTarget): The target for the prompt.
orchestrator_identifier (Dict[str, str]): The orchestrator identifier for the prompt.
original_value_data_type (PromptDataType): The data type of the original prompt (text, image)
original_value (str): The text of the original prompt. If prompt is an image, it's a link.
original_value_sha256 (str): The SHA256 hash of the original prompt data.
converted_value_data_type (PromptDataType): The data type of the converted prompt (text, image)
converted_value (str): The text of the converted prompt. If prompt is an image, it's a link.
converted_value_sha256 (str): The SHA256 hash of the original prompt data.
idx_conversation_id (Index): The index for the conversation ID.
original_prompt_id (UUID): The original prompt id. It is equal to id unless it is a duplicate.
scores (list[ScoreEntry]): The list of scores associated with the prompt.
Methods:
__str__(): Returns a string representation of the memory entry.
"""
__tablename__ = "PromptMemoryEntries"
__table_args__ = {"extend_existing": True}
id = mapped_column(Uuid, nullable=False, primary_key=True)
role: Mapped[Literal["system", "user", "assistant"]] = mapped_column(String, nullable=False)
conversation_id = mapped_column(String, nullable=False)
sequence = mapped_column(INTEGER, nullable=False)
timestamp = mapped_column(DateTime, nullable=False)
labels: Mapped[dict[str, str]] = mapped_column(JSON)
prompt_metadata: Mapped[dict[str, Union[str, int]]] = mapped_column(JSON)
converter_identifiers: Mapped[Optional[List[dict[str, str]]]] = mapped_column(JSON)
prompt_target_identifier: Mapped[dict[str, str]] = mapped_column(JSON)
orchestrator_identifier: Mapped[dict[str, str]] = mapped_column(JSON)
response_error: Mapped[Literal["blocked", "none", "processing", "unknown"]] = mapped_column(String, nullable=True)
original_value_data_type: Mapped[Literal["text", "image_path", "audio_path", "url", "error"]] = mapped_column(
String, nullable=False
)
original_value = mapped_column(Unicode, nullable=False)
original_value_sha256 = mapped_column(String)
converted_value_data_type: Mapped[Literal["text", "image_path", "audio_path", "url", "error"]] = mapped_column(
String, nullable=False
)
converted_value = mapped_column(Unicode)
converted_value_sha256 = mapped_column(String)
idx_conversation_id = Index("idx_conversation_id", "conversation_id")
original_prompt_id = mapped_column(Uuid, nullable=False)
scores: Mapped[List["ScoreEntry"]] = relationship(
"ScoreEntry",
primaryjoin="ScoreEntry.prompt_request_response_id == PromptMemoryEntry.original_prompt_id",
back_populates="prompt_request_piece",
foreign_keys="ScoreEntry.prompt_request_response_id",
)
def __init__(self, *, entry: PromptRequestPiece):
self.id = entry.id
self.role = entry.role
self.conversation_id = entry.conversation_id
self.sequence = entry.sequence
self.timestamp = entry.timestamp
self.labels = entry.labels
self.prompt_metadata = entry.prompt_metadata
self.converter_identifiers = entry.converter_identifiers
self.prompt_target_identifier = entry.prompt_target_identifier
self.orchestrator_identifier = entry.orchestrator_identifier
self.original_value = entry.original_value
self.original_value_data_type = entry.original_value_data_type # type: ignore
self.original_value_sha256 = entry.original_value_sha256
self.converted_value = entry.converted_value
self.converted_value_data_type = entry.converted_value_data_type # type: ignore
self.converted_value_sha256 = entry.converted_value_sha256
self.response_error = entry.response_error # type: ignore
self.original_prompt_id = entry.original_prompt_id
[docs]
def get_prompt_request_piece(self) -> PromptRequestPiece:
prompt_request_piece = PromptRequestPiece(
role=self.role,
original_value=self.original_value,
original_value_sha256=self.original_value_sha256,
converted_value=self.converted_value,
converted_value_sha256=self.converted_value_sha256,
id=self.id,
conversation_id=self.conversation_id,
sequence=self.sequence,
labels=self.labels,
prompt_metadata=self.prompt_metadata,
converter_identifiers=self.converter_identifiers,
prompt_target_identifier=self.prompt_target_identifier,
orchestrator_identifier=self.orchestrator_identifier,
original_value_data_type=self.original_value_data_type,
converted_value_data_type=self.converted_value_data_type,
response_error=self.response_error,
original_prompt_id=self.original_prompt_id,
timestamp=self.timestamp,
)
prompt_request_piece.scores = [score.get_score() for score in self.scores]
return prompt_request_piece
[docs]
def __str__(self):
if self.prompt_target_identifier:
return f"{self.prompt_target_identifier['__type__']}: {self.role}: {self.converted_value}"
return f": {self.role}: {self.converted_value}"
[docs]
class EmbeddingDataEntry(Base): # type: ignore
"""
Represents the embedding data associated with conversation entries in the database.
Each embedding is linked to a specific conversation entry via an id
Parameters:
id (Uuid): The primary key, which is a foreign key referencing the UUID in the PromptMemoryEntries table.
embedding (ARRAY(Float)): An array of floats representing the embedding vector.
embedding_type_name (String): The name or type of the embedding, indicating the model or method used.
"""
__tablename__ = "EmbeddingData"
# Allows table redefinition if already defined.
__table_args__ = {"extend_existing": True}
id = mapped_column(Uuid(as_uuid=True), ForeignKey(f"{PromptMemoryEntry.__tablename__}.id"), primary_key=True)
embedding = mapped_column(ARRAY(Float).with_variant(JSON, "mssql")) # type: ignore
embedding_type_name = mapped_column(String)
def __str__(self):
return f"{self.id}"
class ScoreEntry(Base): # type: ignore
"""
Represents the Score Memory Entry
"""
__tablename__ = "ScoreEntries"
__table_args__ = {"extend_existing": True}
id = mapped_column(Uuid(as_uuid=True), nullable=False, primary_key=True)
score_value = mapped_column(String, nullable=False)
score_value_description = mapped_column(String, nullable=True)
score_type: Mapped[Literal["true_false", "float_scale"]] = mapped_column(String, nullable=False)
score_category = mapped_column(String, nullable=False)
score_rationale = mapped_column(String, nullable=True)
score_metadata = mapped_column(String, nullable=True)
scorer_class_identifier: Mapped[dict[str, str]] = mapped_column(JSON)
prompt_request_response_id = mapped_column(Uuid(as_uuid=True), ForeignKey(f"{PromptMemoryEntry.__tablename__}.id"))
timestamp = mapped_column(DateTime, nullable=False)
task = mapped_column(String, nullable=True)
prompt_request_piece: Mapped["PromptMemoryEntry"] = relationship("PromptMemoryEntry", back_populates="scores")
def __init__(self, *, entry: Score):
self.id = entry.id
self.score_value = entry.score_value
self.score_value_description = entry.score_value_description
self.score_type = entry.score_type
self.score_category = entry.score_category
self.score_rationale = entry.score_rationale
self.score_metadata = entry.score_metadata
self.scorer_class_identifier = entry.scorer_class_identifier
self.prompt_request_response_id = entry.prompt_request_response_id if entry.prompt_request_response_id else None
self.timestamp = entry.timestamp
self.task = entry.task
def get_score(self) -> Score:
return Score(
id=self.id,
score_value=self.score_value,
score_value_description=self.score_value_description,
score_type=self.score_type,
score_category=self.score_category,
score_rationale=self.score_rationale,
score_metadata=self.score_metadata,
scorer_class_identifier=self.scorer_class_identifier,
prompt_request_response_id=self.prompt_request_response_id,
timestamp=self.timestamp,
task=self.task,
)
def to_dict(self) -> dict:
return {
"id": str(self.id),
"score_value": self.score_value,
"score_value_description": self.score_value_description,
"score_type": self.score_type,
"score_category": self.score_category,
"score_rationale": self.score_rationale,
"score_metadata": self.score_metadata,
"scorer_class_identifier": self.scorer_class_identifier,
"prompt_request_response_id": str(self.prompt_request_response_id),
"timestamp": self.timestamp.isoformat() if self.timestamp else None,
"task": self.task,
}
class ConversationMessageWithSimilarity(BaseModel):
model_config = ConfigDict(extra="forbid")
role: str
content: str
metric: str
score: float = 0.0
class EmbeddingMessageWithSimilarity(BaseModel):
model_config = ConfigDict(extra="forbid")
uuid: uuid.UUID
metric: str
score: float = 0.0
class SeedPromptEntry(Base):
"""
Represents the raw prompt or prompt template data as found in open datasets.
Note: This is different from the PromptMemoryEntry which is the processed prompt data.
SeedPrompt merely reflects basic prompts before plugging into orchestrators,
running through models with corresponding attack strategies, and applying converters.
PromptMemoryEntry captures the processed prompt data before and after the above steps.
Parameters:
__tablename__ (str): The name of the database table.
__table_args__ (dict): Additional arguments for the database table.
id (Uuid): The unique identifier for the memory entry.
value (str): The value of the seed prompt.
value_sha256 (str): The SHA256 hash of the value of the seed prompt data.
data_type (PromptDataType): The data type of the seed prompt.
dataset_name (str): The name of the dataset the seed prompt belongs to.
harm_categories (List[str]): The harm categories associated with the seed prompt.
description (str): The description of the seed prompt.
authors (List[str]): The authors of the seed prompt.
groups (List[str]): The groups involved in authoring the seed prompt (if any).
source (str): The source of the seed prompt.
date_added (DateTime): The date the seed prompt was added.
added_by (str): The user who added the seed prompt.
prompt_metadata (dict[str, str | int]): The metadata associated with the seed prompt. This includes
information that is useful for the specific target you're probing, such as encoding data.
parameters (List[str]): The parameters included in the value.
Note that seed prompts do not have parameters, only prompt templates do.
However, they are stored in the same table.
prompt_group_id (uuid.UUID): The ID of a group the seed prompt may optionally belong to.
Groups are used to organize prompts for multi-turn conversations or multi-modal prompts.
sequence (int): The turn of the seed prompt in a group. When entire multi-turn conversations
are stored, this is used to order the prompts.
Methods:
__str__(): Returns a string representation of the memory entry.
"""
__tablename__ = "SeedPromptEntries"
__table_args__ = {"extend_existing": True}
id = mapped_column(Uuid, nullable=False, primary_key=True)
value = mapped_column(Unicode, nullable=False)
value_sha256 = mapped_column(Unicode, nullable=True)
data_type: Mapped[PromptDataType] = mapped_column(String, nullable=False)
name = mapped_column(String, nullable=True)
dataset_name = mapped_column(String, nullable=True)
harm_categories: Mapped[Optional[List[str]]] = mapped_column(JSON, nullable=True)
description = mapped_column(String, nullable=True)
authors: Mapped[Optional[List[str]]] = mapped_column(JSON, nullable=True)
groups: Mapped[Optional[List[str]]] = mapped_column(JSON, nullable=True)
source = mapped_column(String, nullable=True)
date_added = mapped_column(DateTime, nullable=False)
added_by = mapped_column(String, nullable=False)
prompt_metadata: Mapped[dict[str, Union[str, int]]] = mapped_column(JSON, nullable=True)
parameters: Mapped[Optional[List[str]]] = mapped_column(JSON, nullable=True)
prompt_group_id: Mapped[Optional[uuid.UUID]] = mapped_column(Uuid, nullable=True)
sequence: Mapped[Optional[int]] = mapped_column(INTEGER, nullable=True)
def __init__(self, *, entry: SeedPrompt):
self.id = entry.id
self.value = entry.value
self.value_sha256 = entry.value_sha256
self.data_type = entry.data_type
self.name = entry.name
self.dataset_name = entry.dataset_name
self.harm_categories = entry.harm_categories # type: ignore
self.description = entry.description
self.authors = entry.authors # type: ignore
self.groups = entry.groups # type: ignore
self.source = entry.source
self.date_added = entry.date_added
self.added_by = entry.added_by
self.prompt_metadata = entry.metadata
self.parameters = entry.parameters # type: ignore
self.prompt_group_id = entry.prompt_group_id
self.sequence = entry.sequence
def get_seed_prompt(self) -> SeedPrompt:
return SeedPrompt(
id=self.id,
value=self.value,
value_sha256=self.value_sha256,
data_type=self.data_type,
name=self.name,
dataset_name=self.dataset_name,
harm_categories=self.harm_categories,
description=self.description,
authors=self.authors,
groups=self.groups,
source=self.source,
date_added=self.date_added,
added_by=self.added_by,
metadata=self.prompt_metadata,
parameters=self.parameters,
prompt_group_id=self.prompt_group_id,
sequence=self.sequence,
)
