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82 lines
3 KiB
Python
82 lines
3 KiB
Python
import os
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import json
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import openai
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from dotenv import load_dotenv
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load_dotenv()
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openai.api_base = 'http://localhost:2332/v1'
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openai.api_key = os.environ['NOVA_KEY']
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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-0613',
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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-0613',
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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()) |