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76 lines
2.8 KiB
76 lines
2.8 KiB
import openai
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import json
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# Example dummy function hard coded to return the same weather
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# In production, this could be your backend API or an external API
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def get_current_weather(location, unit="fahrenheit"):
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"""Get the current weather in a given location"""
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weather_info = {
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"location": location,
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"temperature": "72",
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"unit": unit,
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"forecast": ["sunny", "windy"],
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}
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return json.dumps(weather_info)
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def run_conversation():
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# Step 1: send the conversation and available functions to GPT
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messages = [{"role": "user", "content": "What's the weather like in Boston?"}]
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functions = [
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{
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
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},
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"required": ["location"],
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},
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}
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]
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=messages,
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functions=functions,
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function_call="auto", # auto is default, but we'll be explicit
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)
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response_message = response["choices"][0]["message"]
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# Step 2: check if GPT wanted to call a function
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if response_message.get("function_call"):
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# Step 3: call the function
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# Note: the JSON response may not always be valid; be sure to handle errors
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available_functions = {
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"get_current_weather": get_current_weather,
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} # only one function in this example, but you can have multiple
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function_name = response_message["function_call"]["name"]
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fuction_to_call = available_functions[function_name]
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function_args = json.loads(response_message["function_call"]["arguments"])
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function_response = fuction_to_call(
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location=function_args.get("location"),
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unit=function_args.get("unit"),
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)
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# Step 4: send the info on the function call and function response to GPT
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messages.append(response_message) # extend conversation with assistant's reply
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messages.append(
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{
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"role": "function",
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"name": function_name,
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"content": function_response,
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}
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) # extend conversation with function response
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second_response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=messages,
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) # get a new response from GPT where it can see the function response
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return second_response
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print(run_conversation()) |