pyrit.prompt_target.OpenAICompletionTarget

pyrit.prompt_target.OpenAICompletionTarget#

class OpenAICompletionTarget(max_tokens: int | None = None, temperature: float | None = None, top_p: float | None = None, presence_penalty: float | None = None, frequency_penalty: float | None = None, n: int | None = None, *args, **kwargs)[source]#

Bases: OpenAITarget

A prompt target for OpenAI completion endpoints.

__init__(max_tokens: int | None = None, temperature: float | None = None, top_p: float | None = None, presence_penalty: float | None = None, frequency_penalty: float | None = None, n: int | None = None, *args, **kwargs)[source]#

Initialize the OpenAICompletionTarget with the given parameters.

Parameters:

  • model_name (str, Optional) – The name of the model. If no value is provided, the OPENAI_COMPLETION_MODEL environment variable will be used.

  • endpoint (str, Optional) – The target URL for the OpenAI service.

  • api_key (str | Callable[[], str], Optional) – The API key for accessing the OpenAI service, or a callable that returns an access token. For Azure endpoints with Entra authentication, pass a token provider from pyrit.auth (e.g., get_azure_openai_auth(endpoint)). Defaults to the OPENAI_CHAT_KEY environment variable.

  • headers (str, Optional) – Headers of the endpoint (JSON).

  • 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.

  • 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.

  • frequency_penalty (float, Optional) – Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.

  • n (int, Optional) – How many completions to generate for each prompt.

  • *args – Variable length argument list passed to the parent class.

  • **kwargs – Additional keyword arguments passed to the parent OpenAITarget class.

  • httpx_client_kwargs (dict, Optional) – Additional kwargs to be passed to the httpx.AsyncClient() constructor. For example, to specify a 3 minute timeout: httpx_client_kwargs={"timeout": 180}

Methods

__init__([max_tokens, temperature, top_p, ...])

Initialize the OpenAICompletionTarget with the given parameters.

dispose_db_engine()

Dispose database engine to release database connections and resources.

get_identifier()

Get an identifier dictionary for this prompt target.

is_json_response_supported()

Check if the target supports JSON as a response format.

is_response_format_json(message_piece)

Check if the response format is JSON and ensure the target supports it.

send_prompt_async(**kwargs)

Send a normalized prompt async to the prompt target.

set_model_name(*, model_name)

Set the model name for this target.

set_system_prompt(*, system_prompt, ...[, ...])

Set the system prompt for the prompt target.

Attributes

ADDITIONAL_REQUEST_HEADERS

model_name_environment_variable

endpoint_environment_variable

api_key_environment_variable

supported_converters

A list of PromptConverters that are supported by the prompt target.
api_key_environment_variable: str#
endpoint_environment_variable: str#
is_json_response_supported() → bool[source]#

Check if the target supports JSON as a response format.

Returns:

True if JSON response is supported, False otherwise.

Return type:

bool

model_name_environment_variable: str#
async send_prompt_async(**kwargs)#

Send a normalized prompt async to the prompt target.

Returns:

A list of message responses. Most targets return a single message,

but some (like response target with tool calls) may return multiple messages.

Return type:

list[Message]

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