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3b441f312a
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07b1ccb230
libs
partners/openai/langchain_openai/chat_models
standard-tests/langchain_tests/unit_tests
@ -440,7 +440,7 @@ class BaseChatOpenAI(BaseChatModel):
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async_client: Any = Field(default=None, exclude=True) #: :meta private:
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root_client: Any = Field(default=None, exclude=True) #: :meta private:
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root_async_client: Any = Field(default=None, exclude=True) #: :meta private:
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model_name: str = Field(default="gpt-3.5-turbo", alias="model")
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model: str = Field(default="gpt-3.5-turbo", alias="model_name")
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"""Model name to use."""
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temperature: Optional[float] = None
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"""What sampling temperature to use."""
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@ -545,6 +545,10 @@ class BaseChatOpenAI(BaseChatModel):
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model_config = ConfigDict(populate_by_name=True)
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@property
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def model_name(self) -> str:
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return self.model
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@model_validator(mode="before")
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@classmethod
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def build_extra(cls, values: Dict[str, Any]) -> Any:
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@ -534,6 +534,22 @@ class ChatModelUnitTests(ChatModelTests):
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If this test fails, ensure that the model can be initialized with a
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``model`` parameter, and that the model parameter can be accessed as
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``.model``.
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If not, the easiest way to configure this is likely to add
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``from pydantic import ConfigDict`` at the top of your file, and add a
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``model_config`` class attribute to your model class:
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.. code-block:: python
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class MyChatModel(BaseChatModel):
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model: str = Field(alias="model_name")
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model_config = ConfigDict(populate_by_name=True)
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# optional property for backwards-compatibility
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# for folks accessing chat_model.model_name
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@property
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def model_name(self) -> str:
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return self.model
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"""
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params = {
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**self.standard_chat_model_params,
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