# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import json
import logging
from typing import Optional
import httpx
from pyrit.common import net_utility
from pyrit.exceptions.exception_classes import (
EmptyResponseException,
RateLimitException,
handle_bad_request_exception,
pyrit_target_retry,
)
from pyrit.models import PromptRequestResponse, construct_response_from_request
from pyrit.models.prompt_request_piece import PromptRequestPiece
from pyrit.prompt_target import OpenAITarget, limit_requests_per_minute
logger = logging.getLogger(__name__)
[docs]
class OpenAICompletionTarget(OpenAITarget):
[docs]
def __init__(
self,
max_tokens: Optional[int] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
presence_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
n: Optional[int] = None,
*args,
**kwargs,
):
"""
Args:
model_name (str, Optional): The name of the model.
endpoint (str, Optional): The target URL for the OpenAI service.
api_key (str, Optional): The API key for accessing the Azure OpenAI service.
Defaults to the OPENAI_CHAT_KEY environment variable.
headers (str, Optional): Headers of the endpoint (JSON).
use_aad_auth (bool, Optional): When set to True, user authentication is used
instead of API Key. DefaultAzureCredential is taken for
https://cognitiveservices.azure.com/.default . Please run `az login` locally
to leverage user AuthN.
api_version (str, Optional): The version of the Azure OpenAI API. Defaults to
"2024-06-01".
max_requests_per_minute (int, Optional): Number of requests the target can handle per
minute before hitting a rate limit. The number of requests sent to the target
will be capped at the value provided.
httpx_client_kwargs (dict, Optional): Additional kwargs to be passed to the
httpx.AsyncClient() constructor.
max_tokens (int, Optional): The maximum number of tokens that can be generated in the
completion. The token count of your prompt plus `max_tokens` cannot exceed the model's
context length.
temperature (float, Optional): What sampling temperature to use, between 0 and 2.
Values like 0.8 will make the output more random, while lower values like 0.2 will
make it more focused and deterministic.
top_p (float, Optional): An alternative to sampling with temperature, called nucleus
sampling, where the model considers the results of the tokens with top_p probability mass.
presence_penalty (float, Optional): Number between -2.0 and 2.0. Positive values penalize new
tokens based on whether they appear in the text so far, increasing the model's likelihood to
talk about new topics.
n (int, Optional): How many completions to generate for each prompt.
httpx_client_kwargs (dict, Optional): Additional kwargs to be passed to the
httpx.AsyncClient() constructor.
For example, to specify a 3 minutes timeout: httpx_client_kwargs={"timeout": 180}
"""
super().__init__(*args, **kwargs)
self._max_tokens = max_tokens
self._temperature = temperature
self._top_p = top_p
self._frequency_penalty = frequency_penalty
self._presence_penalty = presence_penalty
self._n = n
def _set_openai_env_configuration_vars(self):
self.model_name_environment_variable = "OPENAI_COMPLETION_MODEL"
self.endpoint_environment_variable = "OPENAI_COMPLETION_ENDPOINT"
self.api_key_environment_variable = "OPENAI_COMPLETION_API_KEY"
@limit_requests_per_minute
@pyrit_target_retry
async def send_prompt_async(self, *, prompt_request: PromptRequestResponse) -> PromptRequestResponse:
self._validate_request(prompt_request=prompt_request)
request_piece = prompt_request.request_pieces[0]
logger.info(f"Sending the following prompt to the prompt target: {request_piece}")
self.refresh_auth_headers()
body = await self._construct_request_body(request=request_piece)
params = {}
if self._api_version is not None:
params["api-version"] = self._api_version
try:
str_response: httpx.Response = await net_utility.make_request_and_raise_if_error_async(
endpoint_uri=self._endpoint,
method="POST",
headers=self._headers,
request_body=body,
params=params,
**self._httpx_client_kwargs,
)
except httpx.HTTPStatusError as StatusError:
if StatusError.response.status_code == 400:
# Handle Bad Request
return handle_bad_request_exception(response_text=StatusError.response.text, request=request_piece)
elif StatusError.response.status_code == 429:
raise RateLimitException()
else:
raise
logger.info(f'Received the following response from the prompt target "{str_response.text}"')
response_entry = self._construct_prompt_response_from_openai_json(
open_ai_str_response=str_response.text, request_piece=request_piece
)
return response_entry
async def _construct_request_body(self, request: PromptRequestPiece) -> dict:
body_parameters = {
"model": self._model_name,
"prompt": request.converted_value,
"top_p": self._top_p,
"temperature": self._temperature,
"frequency_penalty": self._frequency_penalty,
"presence_penalty": self._presence_penalty,
"max_tokens": self._max_tokens,
"n": self._n,
}
# Filter out None values
return {k: v for k, v in body_parameters.items() if v is not None}
def _construct_prompt_response_from_openai_json(
self,
*,
open_ai_str_response: str,
request_piece: PromptRequestPiece,
) -> PromptRequestResponse:
response = json.loads(open_ai_str_response)
extracted_response = []
for response_piece in response["choices"]:
extracted_response.append(response_piece["text"])
if not extracted_response:
logger.log(logging.ERROR, "The chat returned an empty response.")
raise EmptyResponseException(message="The chat returned an empty response.")
return construct_response_from_request(request=request_piece, response_text_pieces=extracted_response)
def _validate_request(self, *, prompt_request: PromptRequestResponse) -> None:
if len(prompt_request.request_pieces) != 1:
raise ValueError("This target only supports a single prompt request piece.")
if prompt_request.request_pieces[0].converted_value_data_type != "text":
raise ValueError("This target only supports text prompt input.")
request = prompt_request.request_pieces[0]
messages = self._memory.get_chat_messages_with_conversation_id(conversation_id=request.conversation_id)
if len(messages) > 0:
raise ValueError("This target only supports a single turn conversation.")
[docs]
def is_json_response_supported(self) -> bool:
"""Indicates that this target supports JSON response format."""
return False
