Invalid tool_choice being passed to ChatLiteLLM (#28198)

- **Description:** Invalid `tool_choice` is given to `ChatLiteLLM` to
`bind_tools` due to it's parent's class default value being pass through
`with_structured_output`.
- **Issue:** #28176

libs/community/langchain_community/chat_models/litellm.py

@@ -11,6 +11,7 @@ from typing import (
     Dict,
     Iterator,
     List,
+    Literal,
     Mapping,
     Optional,
     Sequence,
@@ -212,6 +213,33 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
     return message_dict
 
 
+_OPENAI_MODELS = [
+    "o1-mini",
+    "o1-preview",
+    "gpt-4o-mini",
+    "gpt-4o-mini-2024-07-18",
+    "gpt-4o",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-05-13",
+    "gpt-4-turbo",
+    "gpt-4-turbo-preview",
+    "gpt-4-0125-preview",
+    "gpt-4-1106-preview",
+    "gpt-3.5-turbo-1106",
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-4",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+]
+
+
 class ChatLiteLLM(BaseChatModel):
     """Chat model that uses the LiteLLM API."""
 
@@ -465,6 +493,9 @@ class ChatLiteLLM(BaseChatModel):
     def bind_tools(
         self,
         tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
+        tool_choice: Optional[
+            Union[dict, str, Literal["auto", "none", "required", "any"], bool]
+        ] = None,
         **kwargs: Any,
     ) -> Runnable[LanguageModelInput, BaseMessage]:
         """Bind tool-like objects to this chat model.
@@ -476,17 +507,47 @@ class ChatLiteLLM(BaseChatModel):
                 Can be  a dictionary, pydantic model, callable, or BaseTool. Pydantic
                 models, callables, and BaseTools will be automatically converted to
                 their schema dictionary representation.
-            tool_choice: Which tool to require the model to call.
-                Must be the name of the single provided function or
-                "auto" to automatically determine which function to call
-                (if any), or a dict of the form:
-                {"type": "function", "function": {"name": <<tool_name>>}}.
+            tool_choice: Which tool to require the model to call. Options are:
+                - str of the form ``"<<tool_name>>"``: calls <<tool_name>> tool.
+                - ``"auto"``:
+                    automatically selects a tool (including no tool).
+                - ``"none"``:
+                    does not call a tool.
+                - ``"any"`` or ``"required"`` or ``True``:
+                    forces least one tool to be called.
+                - dict of the form:
+                ``{"type": "function", "function": {"name": <<tool_name>>}}``
+                - ``False`` or ``None``: no effect
             **kwargs: Any additional parameters to pass to the
                 :class:`~langchain.runnable.Runnable` constructor.
         """
 
         formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
-        return super().bind(tools=formatted_tools, **kwargs)
+
+        # In case of openai if tool_choice is `any` or if bool has been provided we
+        # change it to `required` as that is suppored by openai.
+        if (
+            (self.model is not None and "azure" in self.model)
+            or (self.model_name is not None and "azure" in self.model_name)
+            or (self.model is not None and self.model in _OPENAI_MODELS)
+            or (self.model_name is not None and self.model_name in _OPENAI_MODELS)
+        ) and (tool_choice == "any" or isinstance(tool_choice, bool)):
+            tool_choice = "required"
+        # If tool_choice is bool apart from openai we make it `any`
+        elif isinstance(tool_choice, bool):
+            tool_choice = "any"
+        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}."
+                )
+        return super().bind(tools=formatted_tools, tool_choice=tool_choice, **kwargs)
 
     @property
     def _identifying_params(self) -> Dict[str, Any]: