class SegmentAnalysis(LLMOutput):
    chain_of_thought: str = Field(
        description="Let's think step by step to analyze what needs updates in this segment"
    )
    source_text: str = Field(
        description="some_desc"
    )
    updated_text: str = Field(
        description="some_desc"
    )

    @model_validator(mode="after")
    def source_text_exists(self, info: ValidationInfo):
        context = info.context
        if context:
            context = context.get("input_text")
            if self.source_text not in context:
                raise ValueError(
                    f"source_text {self.source_text} not found in input text {context}"
                )
        return self


segments = client.chat.completions.create_iterable(
        model="gpt-4o",
        response_model=SegmentAnalysis,
        stream=True,
        max_retries=3,
        messages=[
            {"role": "system", "content": "some prompt"},
            {"role": "user", "content": "1. 2024, this date is old, update. 2. 2025, this date is new, don't update"},
        ],
        context={"input_text": text},
    )

def process_response(...):
...
    if (
        inspect.isclass(response_model)
        and issubclass(response_model, (IterableBase, PartialBase))
        and stream
    ):
        model = response_model.from_streaming_response(
            response,
            mode=mode,
        )
        return model

    model = response_model.from_response(
        response,
        validation_context=validation_context,
        strict=strict,
        mode=mode,
    )

instructor.process_response ->
  IterableBase.from_streaming_response -> 
  IterableBase.tasks_from_chunks ->
  cls.task_type.model_validate_json