OpenAI Functions - Multi-Tool Agent

gistfile1.txt

import requests
from functools import partial
import json
import openai
from termcolor import colored


MAX_ITERATIONS = 4
BASE_URL = "https://api.example.com"

# request structure
CITY_PARAM = {
  "type": "object",
  "properties": {
    "city": {
      "type": "string", 
      "description": "the names of the city you want to get the weather for"
    }
  },
  "required": ["city"],
}

# run a get request
def get_request(endpoint, params):
  r = requests.get(BASE_URL+endpoint, params)
  return (r.text)

# functions
functions = {
  "getWeather": {
    "name": "getWeather",
    "description": "Realtime info about the weather.",
    "parameters": CITY_PARAM,
    "function": partial(get_request, "/api/weather")
  },
  "getPopulation": {
    "name": "getWeather",
    "description": "Realtime info about the weather.",
    "parameters": CITY_PARAM,
    "function": partial(get_request, "/api/population")
  },
}

def get_functions_for_model():
  specs = []
  for f in functions:
    spec = {
      "name": functions[f]['name'],
      "description": functions[f]['description'],
      "parameters": functions[f]['parameters'],
    }
    specs.append(spec)
  return specs

def call_function(name, params):
  f = functions[name]
  return f['function'](json.loads(params))


# chat history
messages = []

# add the system prompt
messages.append({"role": "system", "content":"You are an AI assistant with multiple tools..."})

def run(input):
  # add the user message to history
  messages.append({"role":"user", "content":input})

  # run the agent loop
  iter = 0
  while iter < MAX_ITERATIONS:

    # run the completion
    response = openai.ChatCompletion.create(
      model="gpt-3.5-turbo-0613",
      messages=messages,
      functions=get_functions_for_model(),
      function_call="auto",
    )
    # add the response to the chat history
    message = response["choices"][0]["message"]
    messages.append(message)

    # check if the model wants to call a function
    if message.get("function_call"):
      # if so, get the function info
      function_name = message["function_call"]["name"]
      function_input = message["function_call"]["arguments"]

      # can replace this with callbacks
      print(colored(f"Using {function_name}...", "yellow"))

      # call the function
      results = call_function(function_name, function_input)

      # this is for debug
      print(colored(results, "red"))

      # add the function results to the chat history
      messages.append({"role":"function","name":function_name,"content":results})
      # then return to recall the model, +1 iterations
      iter = iter + 1

    # otherwise print the response and get user input
    else:
      return message['content']

  # when iterations max out, call the model for a chat completion
  # without any available tools, forcing the model to respond

  # run the completion
  response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo-0613",
    messages=messages
  )
  # add the response to the chat history
  message = response["choices"][0]["message"]
  messages.append(message)
  # return the message
  return message['content']

print(colored("Hi I'm an example functions agent, what can I do ya for", "green))
while True:
  print(colored(run(input()), "green")

(its python code)
Multi-tool agent loop replicating how chat GPT plugins work
The one thing you'll run into is maxing out the context
You gotta write a wrapper on the memory to clip old messages as the conversation goes on, I left it out for simplicity