How to build an agent with the Node.js SDK

# How to build an agent with the Node.js SDK OpenAI functions enable your app to take action based on user inputs. This means that it can, e.g., search the web, send emails, or book tickets on behalf of your users, making it more powerful than a regular chatbot. In this tutorial, you will build an app that uses OpenAI functions along with the latest version of the Node.js SDK. The app runs in the browser, so you only need a code editor and, e.g., VS Code Live Server to follow along locally. Alternatively, write your code directly in the browser via [this code playground at Scrimba.](https://scrimba.com/scrim/c6r3LkU9) ## What you will build Our app is a simple agent that helps you find activities in your area. It has access to two functions, `getLocation()` and `getCurrentWeather()`, which means it can figure out where you’re located and what the weather is at the moment. At this point, it's important to understand that OpenAI doesn't execute any code for you. It just tells your app which functions it should use in a given scenario, and then leaves it up to your app to invoke them. Once our agent knows your location and the weather, it'll use GPT’s internal knowledge to suggest suitable local activities for you. ## Importing the SDK and authenticating with OpenAI We start by importing the OpenAI SDK at the top of our JavaScript file and authenticate with our API key, which we have stored as an environment variable. ```js import OpenAI from "openai"; const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY, dangerouslyAllowBrowser: true, }); ``` Since we're running our code in a browser environment at Scrimba, we also need to set `dangerouslyAllowBrowser: true` to confirm we understand the risks involved with client-side API requests. Please note that you should move these requests over to a Node server in a production app. ## Creating our two functions Next, we'll create the two functions. The first one - `getLocation` - uses the [IP API](https://ipapi.co/) to get the location of the user. ```js async function getLocation() { const response = await fetch("https://ipapi.co/json/"); const locationData = await response.json(); return locationData; } ``` The IP API returns a bunch of data about your location, including your latitude and longitude, which we’ll use as arguments in the second function `getCurrentWeather`. It uses the [Open Meteo API](https://open-meteo.com/) to get the current weather data, like this: ```js async function getCurrentWeather(latitude, longitude) { const url = `https://api.open-meteo.com/v1/forecast?latitude=${latitude}&longitude=${longitude}&hourly=apparent_temperature`; const response = await fetch(url); const weatherData = await response.json(); return weatherData; } ``` ## Describing our functions for OpenAI For OpenAI to understand the purpose of these functions, we need to describe them using a specific schema. We'll create an array called `tools` that contains one object per function. Each object will have two keys: `type`, `function`, and the `function` key has three subkeys: `name`, `description`, and `parameters`. ```js const tools = [ { type: "function", function: { name: "getCurrentWeather", description: "Get the current weather in a given location", parameters: { type: "object", properties: { latitude: { type: "string", }, longitude: { type: "string", }, }, required: ["longitude", "latitude"], }, } }, { type: "function", function: { name: "getLocation", description: "Get the user's location based on their IP address", parameters: { type: "object", properties: {}, }, } }, ]; ``` ## Setting up the messages array We also need to define a `messages` array. This will keep track of all of the messages back and forth between our app and OpenAI. The first object in the array should always have the `role` property set to `"system"`, which tells OpenAI that this is how we want it to behave. ```js const messages = [ { role: "system", content: "You are a helpful assistant. Only use the functions you have been provided with.", }, ]; ``` ## Creating the agent function We are now ready to build the logic of our app, which lives in the `agent` function. It is asynchronous and takes one argument: the `userInput`. We start by pushing the `userInput` to the messages array. This time, we set the `role` to `"user"`, so that OpenAI knows that this is the input from the user. ```js async function agent(userInput) { messages.push({ role: "user", content: userInput, }); const response = await openai.chat.completions.create({ model: "gpt-4", messages: messages, tools: tools, }); console.log(response); } ``` Next, we'll send a request to the Chat completions endpoint via the `chat.completions.create()` method in the Node SDK. This method takes a configuration object as an argument. In it, we'll specify three properties: - `model` - Decides which AI model we want to use (in our case, GPT-4). - `messages` - The entire history of messages between the user and the AI up until this point. - `tools` - A list of tools the model may call. Currently, only functions are supported as a tool., we'll we use the `tools` array we created earlier. ## Running our app with a simple input Let's try to run the `agent` with an input that requires a function call to give a suitable reply. ```js agent("Where am I located right now?"); ``` When we run the code above, we see the response from OpenAI logged out to the console like this: ```js { id: "chatcmpl-84ojoEJtyGnR6jRHK2Dl4zTtwsa7O", object: "chat.completion", created: 1696159040, model: "gpt-4-0613", choices: [{ index: 0, message: { role: "assistant", content: null, tool_c