OpenAI Function calling

OpenAI Function calling

Google Colab notebook: https://colab.research.google.com/drive/1hb6tQVBsE6jeyVZjhRvUHxyi3GgzZzne?authuser=1#forceEdit=true&sandboxMode=true

“An essential part of creativity is not being afraid to fail.” -Edwin H. Land

# OpenAI Function calling

Source: https://platform.openai.com/docs/guides/gpt/function-calling

!pip install openai

import openai

import json

import os

os.environ["OPENAI_API_KEY"] = "OPENAI_API_KEY" #find this in your files; hard coding is NOT the way to go!

openai.api_key = os.getenv('OPENAI_API_KEY')

# Step 0: Define function

# 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"""

weather_info = {

"location": location,

"temperature": "65",

"unit": unit,

"forecast": ["sunny", "windy"],

}

return json.dumps(weather_info)

get_current_weather("Ann Arbor", unit="fahrenheit")

# functions: A list of functions the model may generate JSON inputs for.

# name: string; Required; The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

# description; string; Optional; The description of what the function does.

# parameters; object; Optional; The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.

functions = [

{

"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"],

},

}

]

# Step 1, send model the user query and what functions it has access to

# functions: A list of functions the model may generate JSON inputs for.

# function_call: Controls how the model responds to function calls.

# "none" means the model does not call a function, and responds to the end-user.

# "auto" means the model can pick between an end-user or calling a function.

# Specifying a particular function via {"name":\ "my_function"} forces the model to call that function.

# "none" is the default when no functions are present. "auto" is the default if functions are present.

response = openai.ChatCompletion.create(

model="gpt-3.5-turbo-0613",

messages=[{"role": "user", "content": "What's the weather like in Ann Arbor?"}],

functions=functions,

function_call="auto",

)

message = response["choices"][0]["message"]

message

# Step 2, check if the model wants to call a function

if message.get("function_call"):

function_name = message["function_call"]["name"]

function_name

# Step 3, call the function

# Note: the JSON response from the model may not be valid JSON

function_response = get_current_weather(

location = eval(message['function_call']['arguments']).get("location")

)

function_response

# Step 4, send model the info on the function call and function response

second_response = openai.ChatCompletion.create(

model="gpt-3.5-turbo-0613",

messages=[

{"role": "user", "content": "What is the weather like in Ann Arbor?"},

message,

{

"role": "function",

"name": function_name,

"content": function_response,

},

],

)

second_response

second_response['choices'][0]['message']['content']

# Convert LangChain tools to OpenAI functions

Source: https://python.langchain.com/en/latest/modules/agents/tools/tools_as_openai_functions.html

!pip install langchain

from langchain.chat_models import ChatOpenAI

from langchain.schema import HumanMessage

model = ChatOpenAI(model="gpt-3.5-turbo-0613")

from langchain.tools import MoveFileTool, format_tool_to_openai_function

??MoveFileTool

tools = [MoveFileTool()]

tools

functions = [format_tool_to_openai_function(t) for t in tools]

functions

message = model.predict_messages(

[HumanMessage(content='move file foo to bar')],

functions=functions)

message

message.additional_kwargs['function_call']