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libs/partners/openai/langchain_openai/chat_models/base.py

@@ -440,7 +440,7 @@ class BaseChatOpenAI(BaseChatModel):
     async_client: Any = Field(default=None, exclude=True)  #: :meta private:
     root_client: Any = Field(default=None, exclude=True)  #: :meta private:
     root_async_client: Any = Field(default=None, exclude=True)  #: :meta private:
-    model_name: str = Field(default="gpt-3.5-turbo", alias="model")
+    model: str = Field(default="gpt-3.5-turbo", alias="model_name")
     """Model name to use."""
     temperature: Optional[float] = None
     """What sampling temperature to use."""
@@ -545,6 +545,10 @@ class BaseChatOpenAI(BaseChatModel):
 
     model_config = ConfigDict(populate_by_name=True)
 
+    @property
+    def model_name(self) -> str:
+        return self.model
+
     @model_validator(mode="before")
     @classmethod
     def build_extra(cls, values: Dict[str, Any]) -> Any:

libs/standard-tests/langchain_tests/unit_tests/chat_models.py

@@ -534,6 +534,22 @@ class ChatModelUnitTests(ChatModelTests):
             If this test fails, ensure that the model can be initialized with a
             ``model`` parameter, and that the model parameter can be accessed as
             ``.model``.
+
+            If not, the easiest way to configure this is likely to add
+            ``from pydantic import ConfigDict`` at the top of your file, and add a
+            ``model_config`` class attribute to your model class:
+
+            .. code-block:: python
+            
+                class MyChatModel(BaseChatModel):
+                    model: str = Field(alias="model_name")
+                    model_config = ConfigDict(populate_by_name=True)
+
+                    # optional property for backwards-compatibility
+                    # for folks accessing chat_model.model_name
+                    @property
+                    def model_name(self) -> str:
+                        return self.model
         """
         params = {
             **self.standard_chat_model_params,