diff --git a/libs/core/langchain_core/language_models/chat_models.py b/libs/core/langchain_core/language_models/chat_models.py
index 1bba3ffd5c8..d170a6a2d68 100644
--- a/libs/core/langchain_core/language_models/chat_models.py
+++ b/libs/core/langchain_core/language_models/chat_models.py
@@ -605,6 +605,9 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
params = {**params, **kwargs}
return str(sorted(params.items()))
+ def _format_params_for_provider(self, params: dict) -> dict:
+ return params
+
def generate(
self,
messages: list[list[BaseMessage]],
@@ -641,6 +644,8 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
"""
+ kwargs = self._format_params_for_provider(**kwargs)
+
structured_output_format = kwargs.pop("structured_output_format", None)
if structured_output_format:
try:
diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py
index e40c5bf98d1..0d6d07d4b75 100644
--- a/libs/partners/openai/langchain_openai/chat_models/base.py
+++ b/libs/partners/openai/langchain_openai/chat_models/base.py
@@ -1024,6 +1024,90 @@ class BaseChatOpenAI(BaseChatModel):
encoding = tiktoken.get_encoding(model)
return model, encoding
+ def _format_tool_params(
+ self,
+ tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
+ *,
+ tool_choice: Optional[
+ Union[dict, str, Literal["auto", "none", "required", "any"], bool]
+ ] = None,
+ strict: Optional[bool] = None,
+ parallel_tool_calls: Optional[bool] = None,
+ **kwargs: Any,
+ ) -> dict:
+ if parallel_tool_calls is not None: # Keep parameter explicit in signature
+ kwargs["parallel_tool_calls"] = parallel_tool_calls
+ formatted_tools = [
+ convert_to_openai_tool(tool, strict=strict) for tool in tools
+ ]
+ kwargs["tools"] = formatted_tools
+ if tool_choice:
+ if isinstance(tool_choice, str):
+ # tool_choice is a tool/function name
+ if tool_choice not in ("auto", "none", "any", "required"):
+ tool_choice = {
+ "type": "function",
+ "function": {"name": tool_choice},
+ }
+ # 'any' is not natively supported by OpenAI API.
+ # We support 'any' since other models use this instead of 'required'.
+ if tool_choice == "any":
+ tool_choice = "required"
+ elif isinstance(tool_choice, bool):
+ tool_choice = "required"
+ elif isinstance(tool_choice, dict):
+ tool_names = [
+ formatted_tool["function"]["name"]
+ for formatted_tool in formatted_tools
+ ]
+ if not any(
+ tool_name == tool_choice["function"]["name"]
+ for tool_name in tool_names
+ ):
+ raise ValueError(
+ f"Tool choice {tool_choice} was specified, but the only "
+ f"provided tools were {tool_names}."
+ )
+ else:
+ raise ValueError(
+ f"Unrecognized tool_choice type. Expected str, bool or dict. "
+ f"Received: {tool_choice}"
+ )
+ kwargs["tool_choice"] = tool_choice
+ return kwargs
+
+ def _format_params_for_provider(
+ self,
+ *,
+ tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]] = [],
+ tool_choice: Optional[
+ Union[dict, str, Literal["auto", "none", "required", "any"], bool]
+ ] = None,
+ strict: Optional[bool] = None,
+ parallel_tool_calls: Optional[bool] = None,
+ response_format: Optional[_DictOrPydanticClass] = None,
+ **kwargs: Any,
+ ) -> dict:
+
+ if tools:
+ kwargs = self._format_tool_params(
+ tools=tools,
+ tool_choice=tool_choice,
+ strict=strict,
+ parallel_tool_calls=parallel_tool_calls,
+ **kwargs,
+ )
+ if response_format:
+ kwargs["response_format"] = _convert_to_openai_response_format(
+ response_format, strict=strict,
+ )
+ kwargs["structured_output_format"] = {
+ "kwargs": {"method": "json_schema"},
+ "schema": convert_to_openai_tool(response_format),
+ }
+
+ return kwargs
+
def get_token_ids(self, text: str) -> List[int]:
"""Get the tokens present in the text with tiktoken package."""
if self.custom_get_token_ids is not None:
@@ -1229,46 +1313,7 @@ class BaseChatOpenAI(BaseChatModel):
Support for ``strict`` argument added.
""" # noqa: E501
-
- if parallel_tool_calls is not None:
- kwargs["parallel_tool_calls"] = parallel_tool_calls
- formatted_tools = [
- convert_to_openai_tool(tool, strict=strict) for tool in tools
- ]
- if tool_choice:
- if isinstance(tool_choice, str):
- # tool_choice is a tool/function name
- if tool_choice not in ("auto", "none", "any", "required"):
- tool_choice = {
- "type": "function",
- "function": {"name": tool_choice},
- }
- # 'any' is not natively supported by OpenAI API.
- # We support 'any' since other models use this instead of 'required'.
- if tool_choice == "any":
- tool_choice = "required"
- elif isinstance(tool_choice, bool):
- tool_choice = "required"
- elif isinstance(tool_choice, dict):
- tool_names = [
- formatted_tool["function"]["name"]
- for formatted_tool in formatted_tools
- ]
- if not any(
- tool_name == tool_choice["function"]["name"]
- for tool_name in tool_names
- ):
- raise ValueError(
- f"Tool choice {tool_choice} was specified, but the only "
- f"provided tools were {tool_names}."
- )
- else:
- raise ValueError(
- f"Unrecognized tool_choice type. Expected str, bool or dict. "
- f"Received: {tool_choice}"
- )
- kwargs["tool_choice"] = tool_choice
- return super().bind(tools=formatted_tools, **kwargs)
+ return super().bind(**kwargs)
def with_structured_output(
self,