Merge pull request #3030 from BerriAI/docs_groq_tool_calling_example

Docs - add groq tool calling example

docs/my-website/docs/providers/groq.md

@@ -50,4 +50,105 @@ We support ALL Groq models, just set `groq/` as a prefix when sending completion
 |--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | llama2-70b-4096 | `completion(model="groq/llama2-70b-4096", messages)` | 
 | mixtral-8x7b-32768 | `completion(model="groq/mixtral-8x7b-32768", messages)` |
-| gemma-7b-it | `completion(model="groq/gemma-7b-it", messages)` |  
+| gemma-7b-it | `completion(model="groq/gemma-7b-it", messages)` |  
+
+## Groq - Tool / Function Calling Example
+
+```python
+# Example dummy function hard coded to return the current weather
+import json
+def get_current_weather(location, unit="fahrenheit"):
+    """Get the current weather in a given location"""
+    if "tokyo" in location.lower():
+        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
+    elif "san francisco" in location.lower():
+        return json.dumps(
+            {"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}
+        )
+    elif "paris" in location.lower():
+        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
+    else:
+        return json.dumps({"location": location, "temperature": "unknown"})
+
+
+
+
+# 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
+
+
+# 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,
+    }
+    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)
+```