Merge pull request #3029 from BerriAI/litellm_add_groq_tool_calling

Feat - add groq tool calling + testing

litellm/model_prices_and_context_window_backup.json

@@ -714,7 +714,8 @@
         "input_cost_per_token": 0.00000070,
         "output_cost_per_token": 0.00000080,
         "litellm_provider": "groq",
-        "mode": "chat"
+        "mode": "chat",
+        "supports_function_calling": true
     },
     "groq/mixtral-8x7b-32768": {
         "max_tokens": 32768,
@@ -723,7 +724,8 @@
         "input_cost_per_token": 0.00000027,
         "output_cost_per_token": 0.00000027,
         "litellm_provider": "groq",
-        "mode": "chat"
+        "mode": "chat",
+        "supports_function_calling": true
     },
     "groq/gemma-7b-it": {
         "max_tokens": 8192,
@@ -732,7 +734,8 @@
         "input_cost_per_token": 0.00000010,
         "output_cost_per_token": 0.00000010,
         "litellm_provider": "groq",
-        "mode": "chat"
+        "mode": "chat",
+        "supports_function_calling": true
     },
     "claude-instant-1.2": {
         "max_tokens": 8191,

litellm/tests/test_function_calling.py

@@ -219,3 +219,94 @@ def test_parallel_function_call_stream():
 
 
 # test_parallel_function_call_stream()
+
+
+def test_groq_parallel_function_call():
+    litellm.set_verbose = True
+    try:
+        # Step 1: send the conversation and available functions to the model
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a function calling LLM that uses the data extracted from get_current_weather to answer questions about the weather in San Francisco.",
+            },
+            {
+                "role": "user",
+                "content": "What's the weather like in San Francisco?",
+            },
+        ]
+        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"],
+                    },
+                },
+            }
+        ]
+        response = litellm.completion(
+            model="groq/llama2-70b-4096",
+            messages=messages,
+            tools=tools,
+            tool_choice="auto",  # auto is default, but we'll be explicit
+        )
+        print("Response\n", response)
+        response_message = response.choices[0].message
+        tool_calls = response_message.tool_calls
+
+        assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
+        assert isinstance(
+            response.choices[0].message.tool_calls[0].function.arguments, str
+        )
+
+        print("length of tool calls", len(tool_calls))
+
+        # Step 2: check if the model wanted to call a function
+        if tool_calls:
+            # Step 3: call the function
+            # Note: the JSON response may not always be valid; be sure to handle errors
+            available_functions = {
+                "get_current_weather": get_current_weather,
+            }  # only one function in this example, but you can have multiple
+            messages.append(
+                response_message
+            )  # extend conversation with assistant's reply
+            print("Response message\n", response_message)
+            # Step 4: send the info for each function call and function response to the model
+            for tool_call in tool_calls:
+                function_name = tool_call.function.name
+                function_to_call = available_functions[function_name]
+                function_args = json.loads(tool_call.function.arguments)
+                function_response = function_to_call(
+                    location=function_args.get("location"),
+                    unit=function_args.get("unit"),
+                )
+                messages.append(
+                    {
+                        "tool_call_id": tool_call.id,
+                        "role": "tool",
+                        "name": function_name,
+                        "content": function_response,
+                    }
+                )  # extend conversation with function response
+            print(f"messages: {messages}")
+            second_response = litellm.completion(
+                model="groq/llama2-70b-4096", messages=messages
+            )  # get a new response from the model where it can see the function response
+            print("second response\n", second_response)
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")

litellm/tests/test_utils.py

@@ -219,17 +219,19 @@ def test_validate_environment_empty_model():
 @mock.patch.dict(os.environ, {"OLLAMA_API_BASE": "foo"}, clear=True)
 def test_validate_environment_ollama():
     for provider in ["ollama", "ollama_chat"]:
-        kv = validate_environment(provider+"/mistral")
+        kv = validate_environment(provider + "/mistral")
         assert kv["keys_in_environment"]
         assert kv["missing_keys"] == []
 
+
 @mock.patch.dict(os.environ, {}, clear=True)
 def test_validate_environment_ollama_failed():
     for provider in ["ollama", "ollama_chat"]:
-        kv = validate_environment(provider+"/mistral")
+        kv = validate_environment(provider + "/mistral")
         assert not kv["keys_in_environment"]
         assert kv["missing_keys"] == ["OLLAMA_API_BASE"]
 
+
 def test_function_to_dict():
     print("testing function to dict for get current weather")
 
@@ -338,6 +340,7 @@ def test_supports_function_calling():
         assert (
             litellm.supports_function_calling(model="azure/gpt-4-1106-preview") == True
         )
+        assert litellm.supports_function_calling(model="groq/gemma-7b-it") == True
         assert (
             litellm.supports_function_calling(model="anthropic.claude-instant-v1")
             == False

litellm/utils.py

@@ -4523,6 +4523,7 @@ def get_optional_params(
             and custom_llm_provider != "vertex_ai"
             and custom_llm_provider != "anyscale"
             and custom_llm_provider != "together_ai"
+            and custom_llm_provider != "groq"
             and custom_llm_provider != "mistral"
             and custom_llm_provider != "anthropic"
             and custom_llm_provider != "cohere_chat"
@@ -5222,6 +5223,29 @@ def get_optional_params(
         optional_params["extra_body"] = (
             extra_body  # openai client supports `extra_body` param
         )
+    elif custom_llm_provider == "groq":
+        supported_params = get_supported_openai_params(
+            model=model, custom_llm_provider=custom_llm_provider
+        )
+        _check_valid_arg(supported_params=supported_params)
+
+        if temperature is not None:
+            optional_params["temperature"] = temperature
+        if max_tokens is not None:
+            optional_params["max_tokens"] = max_tokens
+        if top_p is not None:
+            optional_params["top_p"] = top_p
+        if stream is not None:
+            optional_params["stream"] = stream
+        if stop is not None:
+            optional_params["stop"] = stop
+        if tools is not None:
+            optional_params["tools"] = tools
+        if tool_choice is not None:
+            optional_params["tool_choice"] = tool_choice
+        if response_format is not None:
+            optional_params["response_format"] = tool_choice
+
     elif custom_llm_provider == "openrouter":
         supported_params = get_supported_openai_params(
             model=model, custom_llm_provider=custom_llm_provider
@@ -5426,6 +5450,17 @@ def get_supported_openai_params(model: str, custom_llm_provider: str):
             "tools",
             "tool_choice",
         ]
+    elif custom_llm_provider == "groq":
+        return [
+            "temperature",
+            "max_tokens",
+            "top_p",
+            "stream",
+            "stop",
+            "tools",
+            "tool_choice",
+            "response_format",
+        ]
     elif custom_llm_provider == "cohere":
         return [
             "stream",

model_prices_and_context_window.json

@@ -714,7 +714,8 @@
         "input_cost_per_token": 0.00000070,
         "output_cost_per_token": 0.00000080,
         "litellm_provider": "groq",
-        "mode": "chat"
+        "mode": "chat",
+        "supports_function_calling": true
     },
     "groq/mixtral-8x7b-32768": {
         "max_tokens": 32768,
@@ -723,7 +724,8 @@
         "input_cost_per_token": 0.00000027,
         "output_cost_per_token": 0.00000027,
         "litellm_provider": "groq",
-        "mode": "chat"
+        "mode": "chat",
+        "supports_function_calling": true
     },
     "groq/gemma-7b-it": {
         "max_tokens": 8192,
@@ -732,7 +734,8 @@
         "input_cost_per_token": 0.00000010,
         "output_cost_per_token": 0.00000010,
         "litellm_provider": "groq",
-        "mode": "chat"
+        "mode": "chat",
+        "supports_function_calling": true
     },
     "claude-instant-1.2": {
         "max_tokens": 8191,