62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
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import warnings
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import pytest
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from litellm.types.utils import Choices, Message, ModelResponse
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def test_modelresponse_normalizes_openai_base_models() -> None:
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# OpenAI SDK returns Pydantic BaseModel objects for message/choice.
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# LiteLLM should normalize these into its own internal `Message` / `Choices` types.
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from openai.types.chat.chat_completion import Choice as OpenAIChoice
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from openai.types.chat.chat_completion_message import ChatCompletionMessage
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message = ChatCompletionMessage(role="assistant", content="hi")
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choice = OpenAIChoice(finish_reason="stop", index=0, message=message, logprobs=None)
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with warnings.catch_warnings(record=True) as captured:
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warnings.simplefilter("always")
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response = ModelResponse(model="gpt-4o-mini", choices=[choice])
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_ = response.model_dump()
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assert isinstance(response.choices[0], Choices)
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assert isinstance(response.choices[0].message, Message)
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assert not any(
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"Pydantic serializer warnings" in str(w.message)
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for w in captured
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if isinstance(w.message, Warning)
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)
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def test_modelresponse_serialization_avoids_pydantic_warnings() -> None:
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pytest.importorskip("openai")
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from openai.types.chat import ChatCompletion as OpenAIChatCompletion
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openai_completion = OpenAIChatCompletion(
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id="test-1",
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created=1719868600,
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model="gpt-4o-mini",
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object="chat.completion",
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choices=[
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{
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"index": 0,
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"finish_reason": "stop",
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"message": {"role": "assistant", "content": "hi"},
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"logprobs": None,
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}
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],
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usage={"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2},
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)
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with warnings.catch_warnings(record=True) as captured:
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warnings.simplefilter("always")
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response = ModelResponse(**openai_completion.model_dump())
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_ = response.model_dump(exclude_none=True)
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assert not any(
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"PydanticSerializationUnexpectedValue" in str(w.message)
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or "Pydantic serializer warnings" in str(w.message)
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for w in captured
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)
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