# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from pathlib import Path
from typing import MutableSequence, Optional, Sequence, Union
import logging
from sqlalchemy import create_engine, MetaData, and_
from sqlalchemy.orm import sessionmaker
from sqlalchemy.orm.session import Session
from sqlalchemy.exc import SQLAlchemyError
from sqlalchemy.engine.base import Engine
from contextlib import closing
from pyrit.memory.memory_models import Base, EmbeddingDataEntry, PromptMemoryEntry
from pyrit.memory.memory_interface import MemoryInterface
from pyrit.common.path import RESULTS_PATH
from pyrit.common.singleton import Singleton
from pyrit.models import DiskStorageIO, PromptRequestPiece
logger = logging.getLogger(__name__)
[docs]
class DuckDBMemory(MemoryInterface, metaclass=Singleton):
"""
A class to manage conversation memory using DuckDB as the backend database. It leverages SQLAlchemy Base models
for creating tables and provides CRUD operations to interact with the tables.
This class encapsulates the setup of the database connection, table creation based on SQLAlchemy models,
and session management to perform database operations.
"""
DEFAULT_DB_FILE_NAME = "pyrit_duckdb_storage.db"
[docs]
def __init__(
self,
*,
db_path: Union[Path, str] = None,
verbose: bool = False,
):
super(DuckDBMemory, self).__init__()
if db_path == ":memory:":
self.db_path: Union[Path, str] = ":memory:"
else:
self.db_path = Path(db_path or Path(RESULTS_PATH, self.DEFAULT_DB_FILE_NAME)).resolve()
self.results_path = str(RESULTS_PATH)
self.engine = self._create_engine(has_echo=verbose)
self.SessionFactory = sessionmaker(bind=self.engine)
self._create_tables_if_not_exist()
def _init_storage_io(self):
# Handles disk-based storage for DuckDB local memory.
self.storage_io = DiskStorageIO()
def _create_engine(self, *, has_echo: bool) -> Engine:
"""Creates the SQLAlchemy engine for DuckDB.
Creates an engine bound to the specified database file. The `has_echo` parameter
controls the verbosity of SQL execution logging.
Args:
has_echo (bool): Flag to enable detailed SQL execution logging.
"""
try:
# Create the SQLAlchemy engine.
engine = create_engine(f"duckdb:///{self.db_path}", echo=has_echo)
logger.info(f"Engine created successfully for database: {self.db_path}")
return engine
except SQLAlchemyError as e:
logger.exception(f"Error creating the engine for the database: {e}")
raise
def _create_tables_if_not_exist(self):
"""
Creates all tables defined in the Base metadata, if they don't already exist in the database.
Raises:
Exception: If there's an issue creating the tables in the database.
"""
try:
# Using the 'checkfirst=True' parameter to avoid attempting to recreate existing tables
Base.metadata.create_all(self.engine, checkfirst=True)
except Exception as e:
logger.error(f"Error during table creation: {e}")
[docs]
def get_all_prompt_pieces(self) -> list[PromptRequestPiece]:
"""
Fetches all entries from the specified table and returns them as model instances.
"""
entries = self.query_entries(PromptMemoryEntry)
result: list[PromptRequestPiece] = [entry.get_prompt_request_piece() for entry in entries]
return result
[docs]
def get_all_embeddings(self) -> list[EmbeddingDataEntry]:
"""
Fetches all entries from the specified table and returns them as model instances.
"""
result: list[EmbeddingDataEntry] = self.query_entries(EmbeddingDataEntry)
return result
def _get_prompt_pieces_with_conversation_id(self, *, conversation_id: str) -> list[PromptRequestPiece]:
"""
Retrieves a list of PromptRequestPiece objects that have the specified conversation ID.
Args:
conversation_id (str): The conversation ID to match.
Returns:
list[PromptRequestPiece]: A list of PromptRequestPieces with the specified conversation ID.
"""
try:
entries = self.query_entries(
PromptMemoryEntry, conditions=PromptMemoryEntry.conversation_id == str(conversation_id)
)
prompt_pieces: list[PromptRequestPiece] = [entry.get_prompt_request_piece() for entry in entries]
return sorted(prompt_pieces, key=lambda x: (x.conversation_id, x.sequence))
except Exception as e:
logger.exception(f"Failed to retrieve conversation_id {conversation_id} with error {e}")
return []
[docs]
def get_prompt_request_pieces_by_id(self, *, prompt_ids: list[str]) -> list[PromptRequestPiece]:
"""
Retrieves a list of PromptRequestPiece objects that have the specified prompt ids.
Args:
prompt_ids (list[str]): The prompt IDs to match.
Returns:
list[PromptRequestPiece]: A list of PromptRequestPiece with the specified prompt ID.
"""
try:
entries = self.query_entries(
PromptMemoryEntry,
conditions=PromptMemoryEntry.id.in_(prompt_ids),
)
result: list[PromptRequestPiece] = [entry.get_prompt_request_piece() for entry in entries]
return result
except Exception as e:
logger.exception(
f"Unexpected error: Failed to retrieve ConversationData with orchestrator {prompt_ids}. {e}"
)
return []
[docs]
def get_prompt_request_piece_by_memory_labels(
self, *, memory_labels: dict[str, str] = {}
) -> list[PromptRequestPiece]:
"""
Retrieves a list of PromptRequestPiece objects that have the specified memory labels.
Args:
memory_labels (dict[str, str]): A free-form dictionary for tagging prompts with custom labels.
These labels can be used to track all prompts sent as part of an operation, score prompts based on
the operation ID (op_id), and tag each prompt with the relevant Responsible AI (RAI) harm category.
Users can define any key-value pairs according to their needs. Defaults to an empty dictionary.
Returns:
list[PromptRequestPiece]: A list of PromptRequestPiece with the specified memory labels.
"""
try:
conditions = [PromptMemoryEntry.labels.op("->>")(key) == value for key, value in memory_labels.items()]
query_condition = and_(*conditions)
entries = self.query_entries(PromptMemoryEntry, conditions=query_condition)
result: list[PromptRequestPiece] = [entry.get_prompt_request_piece() for entry in entries]
return result
except Exception as e:
logger.exception(
f"Unexpected error: Failed to retrieve ConversationData with memory labels {memory_labels}. {e}"
)
return []
def _get_prompt_pieces_by_orchestrator(self, *, orchestrator_id: str) -> list[PromptRequestPiece]:
"""
Retrieves a list of PromptRequestPiece objects that have the specified orchestrator ID.
Args:
orchestrator_id (str): The id of the orchestrator.
Can be retrieved by calling orchestrator.get_identifier()["id"]
Returns:
list[PromptRequestPiece]: A list of PromptRequestPiece objects matching the specified orchestrator ID.
"""
try:
entries = self.query_entries(
PromptMemoryEntry,
conditions=PromptMemoryEntry.orchestrator_identifier.op("->>")("id") == orchestrator_id,
) # type: ignore
result: list[PromptRequestPiece] = [entry.get_prompt_request_piece() for entry in entries]
return result
except Exception as e:
logger.exception(
f"Unexpected error: Failed to retrieve ConversationData with orchestrator {orchestrator_id}. {e}"
)
return []
[docs]
def add_request_pieces_to_memory(self, *, request_pieces: Sequence[PromptRequestPiece]) -> None:
"""
Inserts a list of prompt request pieces into the memory storage.
"""
self.insert_entries(entries=[PromptMemoryEntry(entry=piece) for piece in request_pieces])
def _add_embeddings_to_memory(self, *, embedding_data: list[EmbeddingDataEntry]) -> None:
"""
Inserts embedding data into memory storage
"""
self.insert_entries(entries=embedding_data)
[docs]
def get_all_table_models(self) -> list[Base]: # type: ignore
"""
Returns a list of all table models used in the database by inspecting the Base registry.
Returns:
list[Base]: A list of SQLAlchemy model classes.
"""
# The '__subclasses__()' method returns a list of all subclasses of Base, which includes table models
return Base.__subclasses__()
[docs]
def get_session(self) -> Session:
"""
Provides a session for database operations.
"""
return self.SessionFactory()
[docs]
def insert_entry(self, entry: Base) -> None: # type: ignore
"""
Inserts an entry into the Table.
Args:
entry: An instance of a SQLAlchemy model to be added to the Table.
"""
with closing(self.get_session()) as session:
try:
session.add(entry)
session.commit()
except SQLAlchemyError as e:
session.rollback()
logger.exception(f"Error inserting entry into the table: {e}")
[docs]
def insert_entries(self, *, entries: list[Base]) -> None: # type: ignore
"""Inserts multiple entries into the database."""
with closing(self.get_session()) as session:
try:
session.add_all(entries)
session.commit()
except SQLAlchemyError as e:
session.rollback()
logger.exception(f"Error inserting multiple entries into the table: {e}")
raise
[docs]
def query_entries(
self, model, *, conditions: Optional = None, distinct: bool = False # type: ignore
) -> list[Base]:
"""
Fetches data from the specified table model with optional conditions.
Args:
model: The SQLAlchemy model class corresponding to the table you want to query.
conditions: SQLAlchemy filter conditions (Optional).
distinct: Flag to return distinct rows (default is False).
Returns:
List of model instances representing the rows fetched from the table.
"""
with closing(self.get_session()) as session:
try:
query = session.query(model)
if conditions is not None:
query = query.filter(conditions)
if distinct:
return query.distinct().all()
return query.all()
except SQLAlchemyError as e:
logger.exception(f"Error fetching data from table {model.__tablename__}: {e}")
return []
[docs]
def update_entries(self, *, entries: MutableSequence[Base], update_fields: dict) -> bool: # type: ignore
"""
Updates the given entries with the specified field values.
Args:
entries (list[Base]): A list of SQLAlchemy model instances to be updated.
update_fields (dict): A dictionary of field names and their new values.
Returns:
bool: True if the update was successful, False otherwise.
"""
if not update_fields:
raise ValueError("update_fields must be provided to update prompt entries.")
with closing(self.get_session()) as session:
try:
for entry in entries:
# Ensure the entry is attached to the session. If it's detached, merge it.
if not session.is_modified(entry):
entry_in_session = session.merge(entry)
else:
entry_in_session = entry
for field, value in update_fields.items():
if field in vars(entry_in_session):
setattr(entry_in_session, field, value)
else:
session.rollback()
raise ValueError(
f"Field '{field}' does not exist in the table \
'{entry_in_session.__tablename__}'. Rolling back changes..."
)
session.commit()
return True
except SQLAlchemyError as e:
session.rollback()
logger.exception(f"Error updating entries: {e}")
return False
[docs]
def export_all_tables(self, *, export_type: str = "json"):
"""
Exports all table data using the specified exporter.
Iterates over all tables, retrieves their data, and exports each to a file named after the table.
Args:
export_type (str): The format to export the data in (defaults to "json").
"""
table_models = self.get_all_table_models()
for model in table_models:
data = self.query_entries(model)
table_name = model.__tablename__
file_extension = f".{export_type}"
file_path = RESULTS_PATH / f"{table_name}{file_extension}"
self.exporter.export_data(data, file_path=file_path, export_type=export_type)
[docs]
def print_schema(self):
metadata = MetaData()
metadata.reflect(bind=self.engine)
for table_name in metadata.tables:
table = metadata.tables[table_name]
print(f"Schema for {table_name}:")
for column in table.columns:
print(f" Column {column.name} ({column.type})")
[docs]
def dispose_engine(self):
"""
Dispose the engine and clean up resources.
"""
if self.engine:
self.engine.dispose()
logger.info("Engine disposed successfully.")
[docs]
def reset_database(self):
"""Drop and recreate existing tables"""
# Drop all existing tables
Base.metadata.drop_all(self.engine)
# Recreate the tables
Base.metadata.create_all(self.engine, checkfirst=True)
