simplest_chat.py

import openai
openai.api_key = "YOUR_API_KEY"

def agent_response(message):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "user", "content": message}
        ],
        max_tokens=50
    )
    return response['choices'][0]['message']['content']

# Usage
print(agent_response("Hello!"))

tool_integration.py

def get_weather_by_city(city: str) -> str:
  # In real use, connect to a live weather API here.
  fake_weather = {
    "London": "Cloudy, 18°C",
    "New York": "Sunny, 24°C",
    "Beijing": "Rainy, 21°C",
    "Tokyo": "Clear, 27°C"
  }
  return fake_weather.get(city, "Sorry, I don't have the weather info for that city."

# define the tool followed by OpenAI spec
weather_tool = {
  "type": "function",
  "function": {
    "name": "get_weather_by_city",
    "description": "Get today's weather information for a given city.",
    "parameters": {
      "type": "object",
      "properties": {
        "city": {
          "type": "string",
          "description": "The name of the city (e.g., London, New York, Beijing, Tokyo)"
        }
      },
      "required": ["city"]
    }
  }
}

import openai

def agent_response(user_input):
  # Step 1: Send the user message, with tool descriptions, to OpenAI
  chat_completion = openai.ChatCompletion.create(
    model="gpt-3.5-turbo-0613",
    messages=[
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": user_input}
    ],
    tools=[weather_tool],
    tool_choice="auto",  # Let the model decide when to call the tool
  )
  response = chat_completion.choices[0].message

  # Step 2: If tool_call is requested, execute the tool in Python
  if response.tool_calls:
    for tool_call in response.tool_calls:
      fn_name = tool_call.function.name
      arguments = eval(tool_call.function.arguments)
      if fn_name == "get_weather_by_city":
        city = arguments["city"]
        weather_info = get_weather_by_city(city)
        # Now send the tool result back to the model to generate a final answer
        followup = openai.ChatCompletion.create(
            model="gpt-3.5-turbo-0613",
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": user_input},
                response,  # the original assistant response with tool call
                {
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "name": fn_name,
                    "content": weather_info
                }
            ]
        )
        return followup.choices[0].message.content
  # Otherwise, just return the model's normal message
  return response.content