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Python class

TextGenerationRequestMessage

TextGenerationRequestMessage

class max.pipelines.modeling.types.TextGenerationRequestMessage(*, role, content='', tool_calls=None, tool_call_id=None, reasoning_content=None)

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Bases: BaseModel

A single message in a text generation request conversation.

Parameters:

• role (Literal['system', 'user', 'assistant', 'tool', 'function', 'root'])
• content (str | list[TextContentPart | ImageContentPart | VideoContentPart])
• tool_calls (list[dict[str, Any]] | None)
• tool_call_id (str | None)
• reasoning_content (str | None)

content

content: str | list[MessageContent]

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flatten_content()

flatten_content()

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Flattens message content to a role/content dict for text-only messages.

Preserves OpenAI-style tool-calling metadata (tool_calls, tool_call_id, reasoning_content) when set so chat templates that consume conversation history with tool use receive a faithful representation of each turn.

Return type:

dict[str, Any]

model_config

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

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

number_of_images

property number_of_images: int

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Returns the number of ImageContentPart instances in the message content.

number_of_videos

property number_of_videos: int

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Returns the number of VideoContentPart instances in the message content.

reasoning_content

reasoning_content: str | None

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role

role: _MessageRole

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tool_call_id

tool_call_id: str | None

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tool_calls

tool_calls: list[dict[str, Any]] | None

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validate_content_format()

classmethod validate_content_format(v)

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Normalizes message content to a string or list of content parts.

Parameters:

v (Any)

Return type:

str | list[TextContentPart | ImageContentPart | VideoContentPart]