fix(utils.py): support azure mistral function calling

litellm/proxy/_new_secret_config.yaml

@@ -3,7 +3,8 @@ model_list:
   litellm_params:
     model: openai/my-fake-model
     api_key: my-fake-key
-    api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
+    # api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
+    api_base: http://0.0.0.0:8080
     stream_timeout: 0.001
     rpm: 10
 - litellm_params:

litellm/tests/test_completion.py

@@ -33,6 +33,21 @@ def reset_callbacks():
     litellm.callbacks = []
 
 
+def test_response_model_none():
+    """
+    Addresses - https://github.com/BerriAI/litellm/issues/2972
+    """
+    x = completion(
+        model="mymodel",
+        custom_llm_provider="openai",
+        messages=[{"role": "user", "content": "Hello!"}],
+        api_base="http://0.0.0.0:8080",
+        api_key="my-api-key",
+    )
+    print(f"x: {x}")
+    assert isinstance(x, litellm.ModelResponse)
+
+
 def test_completion_custom_provider_model_name():
     try:
         litellm.cache = None
@@ -399,6 +414,51 @@ def test_completion_claude_3_function_plus_image():
     print(response)
 
 
+def test_completion_azure_mistral_large_function_calling():
+    """
+    This primarily tests if the 'Function()' pydantic object correctly handles argument param passed in as a dict vs. string
+    """
+    litellm.set_verbose = True
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_current_weather",
+                "description": "Get the current weather in a given location",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city and state, e.g. San Francisco, CA",
+                        },
+                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                    },
+                    "required": ["location"],
+                },
+            },
+        }
+    ]
+    messages = [
+        {
+            "role": "user",
+            "content": "What's the weather like in Boston today in Fahrenheit?",
+        }
+    ]
+    response = completion(
+        model="azure/mistral-large-latest",
+        api_base=os.getenv("AZURE_MISTRAL_API_BASE"),
+        api_key=os.getenv("AZURE_MISTRAL_API_KEY"),
+        messages=messages,
+        tools=tools,
+        tool_choice="auto",
+    )
+    # Add any assertions, here to check response args
+    print(response)
+    assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
+    assert isinstance(response.choices[0].message.tool_calls[0].function.arguments, str)
+
+
 def test_completion_mistral_api():
     try:
         litellm.set_verbose = True

litellm/utils.py

@@ -226,6 +226,24 @@ class Function(OpenAIObject):
     arguments: str
     name: Optional[str] = None
 
+    def __init__(
+        self,
+        arguments: Union[Dict, str],
+        name: Optional[str] = None,
+        **params,
+    ):
+        if isinstance(arguments, Dict):
+            arguments = json.dumps(arguments)
+        else:
+            arguments = arguments
+
+        name = name
+
+        # Build a dictionary with the structure your BaseModel expects
+        data = {"arguments": arguments, "name": name, **params}
+
+        super(Function, self).__init__(**data)
+
 
 class ChatCompletionDeltaToolCall(OpenAIObject):
     id: Optional[str] = None