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Python class

ResponseResource

ResponseResource​

class max.pipelines.request.ResponseResource(*, id, object='response', created_at, status, model, citations=None, error=None, frequency_penalty=None, include=None, incomplete_details=None, logit_bias=None, logprobs=None, max_output_tokens=None, metadata=None, modalities=None, n=None, output=None, parallel_tool_calls=None, presence_penalty=None, reasoning=None, reasoning_reference=None, response_format=None, seed=None, service_tier=None, stop=None, store=None, stream=None, stream_options=None, temperature=None, tool_choice=None, tools=None, top_logprobs=None, top_p=None, truncation=None, usage=None, user=None, verbosity=None)

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Bases: BaseModel

The response from the model.

Parameters:

citations​

citations: list[UrlCitationBody | ItemReferenceParam] | None

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created_at​

created_at: int

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error​

error: Error | None

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frequency_penalty​

frequency_penalty: float | None

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from_generation_output()​

classmethod from_generation_output(generation_output, model)

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Create a ResponseResource from a GenerationOutput.

Converts pipeline generation output to the OpenResponses API response format.

Parameters:

  • generation_output (Any) – The generation output from the pipeline, containing the generated content (images, etc.) and request metadata.
  • model (str) – The model name used for generation.

Returns:

A ResponseResource instance with the generated content formatted as an assistant message in the output field.

Return type:

ResponseResource

Example:

>>> from max.pipelines.modeling.types.generation import GenerationOutput
>>> from max.pipelines.request import RequestID
>>> from max.pipelines.request.open_responses import OutputImageContent
>>> from max.pipelines.modeling.types.status import GenerationStatus
>>> import numpy as np
>>>
>>> # Create generation output
>>> img_array = (np.random.rand(512, 512, 3) * 255).astype(np.uint8)
>>> gen_output = GenerationOutput(
...     request_id=RequestID(value="req-123"),
...     final_status=GenerationStatus.END_OF_SEQUENCE,
...     output=[OutputImageContent.from_numpy(img_array, format="png")]
... )
>>>
>>> # Convert to ResponseResource
>>> response = ResponseResource.from_generation_output(
...     gen_output, model="flux-2-dev-t2i-bfloat16-v2"
... )

id​

id: str

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include​

include: list[IncludeEnum] | None

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incomplete_details​

incomplete_details: IncompleteDetails | None

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logit_bias​

logit_bias: dict[str, float] | None

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logprobs​

logprobs: bool | None

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max_output_tokens​

max_output_tokens: int | None

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metadata​

metadata: dict[str, str] | None

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modalities​

modalities: list[str] | None

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model​

model: str

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model_config​

model_config: ClassVar[ConfigDict] = {'frozen': True}

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Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

n​

n: int | None

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object​

object: Literal['response']

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output​

output: list[Message] | None

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parallel_tool_calls​

parallel_tool_calls: bool | None

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presence_penalty​

presence_penalty: float | None

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reasoning​

reasoning: ReasoningBody | None

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reasoning_reference​

reasoning_reference: ReasoningReference | None

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response_format​

response_format: ResponseFormat | None

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seed​

seed: int | None

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service_tier​

service_tier: ServiceTierEnum | None

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status​

status: str

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stop​

stop: str | list[str] | None

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store​

store: bool | None

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stream​

stream: bool | None

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stream_options​

stream_options: StreamOptionsParam | None

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temperature​

temperature: float | None

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tool_choice​

tool_choice: ToolChoice | None

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tools​

tools: list[FunctionTool] | None

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top_logprobs​

top_logprobs: int | None

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top_p​

top_p: float | None

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truncation​

truncation: TruncationEnum | None

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usage​

usage: Usage | None

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user​

user: str | None

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verbosity​

verbosity: VerbosityEnum | None

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