# 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 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 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 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}" 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, )