import os import litellm import json from dotenv import load_dotenv load_dotenv() # Example dummy function hard coded to return the same weather # In production, this could be your backend API or an external API 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"}) def test_parallel_function_call(): try: # Step 1: send the conversation and available functions to the model messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}] 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="gpt-4o-mini", messages=messages, tools=tools, tool_choice="auto", # auto is default, but we'll be explicit api_key=os.getenv("OPENAI_API_KEY"), ) # print("\nFirst LLM Response:\n", response) # response_message = response.choices[0].message # tool_calls = response_message.tool_calls # print(tool_calls) print(response.model_dump_json(indent=4)) except Exception as e: print(f"Error occurred: {e}") test_parallel_function_call()