Calling Your Own Functions In Chatbot - OpenAI Function Calling

In this article, I’ll explain how we can make a call to external functions and this we will be doing using OpenAI’s Assistant API. Let's get started to see, how we can build AI assistants in our own applications with just a few sets of instructions.

Open AI

Before we get started, make sure that you have installed OpenAI, a Python library. Let’s get started by grabbing the keys.

Get OpenAI API Key And Organization ID

To get the keys, you need to go to https://openai.com/, log in, and then grab the keys using the highlighted way.

Here we need to grab 2 things — API key and Organization ID.

API Keys

Then set these values in an environment variable.

import os
os.environ["ORGANIZATION_ID"] = "ORG_ID_HERE"
os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"

Import Required Packages And Create Client Object

Do install the dependent library OpenAI and import the below package.

from openai import OpenAI

Once the package is installed, we are good to go ahead and create a client object for OpenAI, which takes 2 parameters.

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"],organization=os.environ["ORGANIZATION_ID"])

Add Function

Next, we need to create a function, along with all the required properties for parameters.

get_homeless_count = {
    "name" : "get_homeless_count",
    "description" : "Function takes in country name, year and returns count of homeless people",
    "parameters" : {
        "type" : "object",
        "properties" : {
            "year": {
                "type":"integer",
                "description" : "Year for which data needs to be fetched"
            },
            "country":{
                "type" : "string",
                "description" : "Country for which data needs to be fetched"
            }
        },
        "required" : ["year","country"]
    }
}

For a detailed explanation of the above JSON, please watch the video attached here.

Create Assistant

Here comes the important part, where we will set up our assistant. It is the assistant’s responsibility to pack all the required parts together:

Instructions: This contains the instructions for the assistant, which should clearly depict what an assistant is supposed to do.
Model: This contains the model’s name that the assistant should be using to respond to user queries. You can specify any GPT 3.5 or GPT-4 models, compatible with the tool.
Tools: Currently it supports 3 tools - Code interpreter, Functional Calling, and Retrieval.

assistant = client.beta.assistants.create(
    instructions="You are a bot who works like a data analyst",
    model="gpt-3.5-turbo-1106",
    tools = [{"type":"function", "function":get_homeless_count}]
)

Create Thread And Run Instance

Next, we need to define a thread to handle user messages and that can be done by calling create() on the threads class.

Once we have the thread id and assistant ID, we are ready to create our un object as shown below.

thread = client.beta.threads.create()
run = client.beta.threads.runs.create(
    thread_id=thread.id,
    assistant_id=assistant.id
)

Create Message

In order to have communication between the user and the assistant, we need messages, and messages are handled using a thread. So, let’s go ahead and define our message.

message = client.beta.threads.messages.create(
    thread_id=thread.id,
    role='user',
    content='How many people were homeless in ABC in 2022?'
)

Make sure to define the correct thread ID and associate the role.

Invoke Tool

Till this point, we are done with our initial setup. The only thing remaining is invoking the tool and reading the response.

run = client.beta.threads.runs.retrieve(
    thread_id=thread.id,
    run_id=run.id
)

Check the status of the run using run.Status and print the message, if status is requires_action.

Status requires_action means here comes the role of the agent as API cannot make calls outside directly.

Associate Action With Tools

Next, we need to submit all the tool calls to status and that can be done as:

tool_calls = run.required_action.submit_tool_outputs.tool_calls
print(tool_calls)

On execution of the above statement, you will the response.

Statement

We can easily validate the response above.

Making An API Call And Collecting the Response

Now, we are ready to make a call to our external APIs or private functions. Once the call is made, make sure to append proper mapping of id and output as well.

if tool_calls[0].function.name == "get_homeless_count":
    # place to make a call and fetch data
    response = "123"

tool_outputs = []
tool_outputs.append({"tool_call_id":tool_calls[0].id, "output": response})

Once the results are collected, we need to associate them to run:

run = client.beta.threads.runs.submit_tool_outputs(
    thread_id=thread.id,
    run_id=run.id,
    tool_outputs=tool_outputs
)

It’s time to check the status and then move ahead. Here is the loop to do so.

import time
while run.status not in ["completed", "failed","requires_action"]:
    time.sleep(5)
    run = client.beta.threads.runs.retrieve(
        thread_id=thread.id,
        run_id=run.id
    )

Reading The Response

Finally, time to read the response from our assistant, and that can be done using the below lines of code.

messages = client.beta.threads.messages.list(thread_id=thread.id)
for msg in messages:
    print(msg.content[0].text.value)

Response

I hope you enjoyed calling external functions from your AI assistant.