openai: developer message just openai

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

@@ -139,7 +139,7 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
             tool_calls=tool_calls,
             invalid_tool_calls=invalid_tool_calls,
         )
-    elif role == "system":
+    elif role == "system" or role == "developer":
         return SystemMessage(content=_dict.get("content", ""), name=name, id=id_)
     elif role == "function":
         return FunctionMessage(
@@ -180,7 +180,9 @@ def _format_message_content(content: Any) -> Any:
     return formatted_content
 
 
-def _convert_message_to_dict(message: BaseMessage) -> dict:
+def _convert_message_to_dict(
+    message: BaseMessage, *, system_message_role: str = "system"
+) -> dict:
     """Convert a LangChain message to a dictionary.
 
     Args:
@@ -233,7 +235,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
             )
             message_dict["audio"] = audio
     elif isinstance(message, SystemMessage):
-        message_dict["role"] = "system"
+        message_dict["role"] = system_message_role
     elif isinstance(message, FunctionMessage):
         message_dict["role"] = "function"
     elif isinstance(message, ToolMessage):
@@ -482,6 +484,7 @@ class BaseChatOpenAI(BaseChatModel):
     However this does not prevent a user from directly passed in the parameter during
     invocation. 
     """
+    system_message_role: str = "system"
 
     model_config = ConfigDict(populate_by_name=True)
 
@@ -493,6 +496,20 @@ class BaseChatOpenAI(BaseChatModel):
         values = _build_model_kwargs(values, all_required_field_names)
         return values
 
+    @model_validator(mode="before")
+    @classmethod
+    def validate_system_message_role(cls, values: Dict[str, Any]) -> Any:
+        """Ensure that the system message role is correctly set for the model."""
+        if "system_message_role" in values:
+            return values
+
+        model = values.get("model_name") or values.get("model") or ""
+        if model.startswith("o1"):
+            values["system_message_role"] = "developer"
+        # otherwise default is "system"
+
+        return values
+
     @model_validator(mode="before")
     @classmethod
     def validate_temperature(cls, values: Dict[str, Any]) -> Any:
@@ -701,7 +718,12 @@ class BaseChatOpenAI(BaseChatModel):
             kwargs["stop"] = stop
 
         return {
-            "messages": [_convert_message_to_dict(m) for m in messages],
+            "messages": [
+                _convert_message_to_dict(
+                    m, system_message_role=self.system_message_role
+                )
+                for m in messages
+            ],
             **self._default_params,
             **kwargs,
         }
@@ -936,7 +958,10 @@ class BaseChatOpenAI(BaseChatModel):
                 " for information on how messages are converted to tokens."
             )
         num_tokens = 0
-        messages_dict = [_convert_message_to_dict(m) for m in messages]
+        messages_dict = [
+            _convert_message_to_dict(m, system_message_role=self.system_message_role)
+            for m in messages
+        ]
         for message in messages_dict:
             num_tokens += tokens_per_message
             for key, value in message.items():

libs/partners/openai/tests/integration_tests/chat_models/test_base.py

@@ -1097,3 +1097,31 @@ def test_o1_max_tokens() -> None:
         "how are you"
     )
     assert isinstance(response, AIMessage)
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        "gpt-4o",
+        "gpt-4o-mini",
+        "o1",
+        # "o1-mini", neither supported
+        "gpt-3.5-turbo",
+    ],
+)
+@pytest.mark.parametrize("role", ["system", "developer", None])
+def test_system_message_roles(model: str, role: Optional[str]) -> None:
+    init_kwargs = {"model": model}
+    if role is not None:
+        init_kwargs["system_message_role"] = role
+    llm = ChatOpenAI(**init_kwargs)  # type: ignore[arg-type]
+    if role is None:
+        if model.startswith("o1"):
+            assert llm.system_message_role == "developer"
+        else:
+            assert llm.system_message_role == "system"
+    history = [SystemMessage("You talk like a pirate"), HumanMessage("Hello there")]
+
+    out = llm.invoke(history)
+    assert isinstance(out, AIMessage)
+    assert out.content