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https://github.com/openai/openai-cookbook
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Update the node example as "function_call" is deprecated. (#934)
Co-authored-by: Logan Kilpatrick <23kilpatrick23@gmail.com>
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Haomin Liu
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@ -4,10 +4,6 @@ OpenAI functions enable your app to take action based on user inputs. This means
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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)
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If you prefer watching screencasts over reading, then you can check out [this scrim, which walks through the code line-by-line:](https://scrimba.com/scrim/co0044b2d9b9b7f5bf16e0391)
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<ScrimPlayer scrimId="co0044b2d9b9b7f5bf16e0391" />
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## What you will build
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Our app is a simple agent that helps you find activities in your area.
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@ -71,34 +67,41 @@ async function getCurrentWeather(latitude, longitude) {
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For OpenAI to understand the purpose of these functions, we need to
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describe them using a specific schema. We'll create an array called
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`functionDefinitions` that contains one object per function. Each object
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will have three keys: `name`, `description`, and `parameters`.
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`tools` that contains one object per function. Each object
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will have two keys: `type`, `function`, and the `function` key has
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three subkeys: `name`, `description`, and `parameters`.
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```js
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const functionDefinitions = [
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const tools = [
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{
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name: "getCurrentWeather",
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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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longitude: {
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type: "string",
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},
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latitude: {
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type: "string",
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type: "function",
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function: {
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name: "getCurrentWeather",
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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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latitude: {
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type: "string",
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},
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longitude: {
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type: "string",
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},
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},
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required: ["longitude", "latitude"],
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},
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required: ["longitude", "latitude"],
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},
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}
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},
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{
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name: "getLocation",
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description: "Get the user's location based on their IP address",
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parameters: {
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type: "object",
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properties: {},
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},
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type: "function",
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function: {
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name: "getLocation",
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description: "Get the user's location based on their IP address",
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parameters: {
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type: "object",
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properties: {},
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},
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}
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},
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];
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```
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@ -136,7 +139,7 @@ async function agent(userInput) {
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const response = await openai.chat.completions.create({
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model: "gpt-4",
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messages: messages,
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functions: functionDefinitions,
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tools: tools,
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});
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console.log(response);
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}
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@ -151,9 +154,9 @@ properties:
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GPT-4).
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- `messages` - The entire history of messages between the user and the
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AI up until this point.
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- `functions` - A description of the functions our app has access to.
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Here, we'll we use the `functionDefinitions` array we created
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earlier.
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- `tools` - A list of tools the model may call. Currently, only
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functions are supported as a tool., we'll we use the `tools` array we
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created earlier.
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## Running our app with a simple input
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@ -177,35 +180,41 @@ to the console like this:
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message: {
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role: "assistant",
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content: null,
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function_call: {
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name: "getLocation", // The function OpenAI wants us to call
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tool_calls: [
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id: "call_CBwbo9qoXUn1kTR5pPuv6vR1",
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type: "function",
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function: {
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name: "getLocation",
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arguments: "{}"
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}
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}
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]
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},
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finish_reason: "function_call" // OpenAI wants us to call a function
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logprobs: null,
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finish_reason: "tool_calls" // OpenAI wants us to call a function
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}],
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usage: {
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prompt_tokens: 134,
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completion_tokens: 6,
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total_tokens: 140
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}
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system_fingerprint: null
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}
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```
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This response tells us that we should call one of our functions, as it contains the following key: `finish:_reason: "function_call"`.
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This response tells us that we should call one of our functions, as it contains the following key: `finish_reason: "tool_calls"`.
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The name of the function can be found in the
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`response.choices[0].message.function_call.name` key, which is set to
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`response.choices[0].message.tool_calls[0].function.name` key, which is set to
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`"getLocation"`.
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## Turning the OpenAI response into a function call
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Now that we have the name of the function as a string, we'll need to
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translate that into a function call. To help us with that, we'll gather
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both of our functions in an object called `availableFunctions`:
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both of our functions in an object called `availableTools`:
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```js
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const availableFunctions = {
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const availableTools = {
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getCurrentWeather,
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getLocation,
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};
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@ -213,15 +222,15 @@ const availableFunctions = {
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This is handy because we'll be able to access the `getLocation` function
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via bracket notation and the string we got back from OpenAI, like this:
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`availableFunctions["getLocation"]`.
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`availableTools["getLocation"]`.
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```js
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const { finish_reason, message } = response.choices[0];
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if (finish_reason === "function_call") {
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const functionName = message.function_call.name;
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const functionToCall = availableFunctions[functionName];
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const functionArgs = JSON.parse(message.function_call.arguments);
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if (finish_reason === "tool_calls" && message.tool_calls) {
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const functionName = message.tool_calls[0].function.name;
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const functionToCall = availableTools[functionName];
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const functionArgs = JSON.parse(message.tool_calls[0].function.arguments);
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const functionArgsArr = Object.values(functionArgs);
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const functionResponse = await functionToCall.apply(null, functionArgsArr);
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console.log(functionResponse);
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@ -229,7 +238,7 @@ if (finish_reason === "function_call") {
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```
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We're also grabbing ahold of any arguments OpenAI wants us to pass into
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the function: `message.function_call.arguments`.
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the function: `message.tool_calls[0].function.arguments`.
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However, we won't need any arguments for this first function call.
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If we run the code again with the same input
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@ -273,7 +282,7 @@ simplicity.
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At the top of the `agent` function, we'll create a loop that lets us run
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the entire procedure up to five times.
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If we get back `finish_reason: "function_call"` from GPT, we'll just
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If we get back `finish_reason: "tool_calls"` from GPT, we'll just
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push the result of the function call to the `messages` array and jump to
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the next iteration of the loop, triggering a new request.
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@ -285,14 +294,14 @@ for (let i = 0; i < 5; i++) {
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const response = await openai.chat.completions.create({
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model: "gpt-4",
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messages: messages,
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functions: functionDefinitions,
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tools: tools,
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});
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const { finish_reason, message } = response.choices[0];
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if (finish_reason === "function_call") {
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const functionName = message.function_call.name;
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const functionToCall = availableFunctions[functionName];
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const functionArgs = JSON.parse(message.function_call.arguments);
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if (finish_reason === "tool_calls" && message.tool_calls) {
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const functionName = message.tool_calls[0].function.name;
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const functionToCall = availableTools[functionName];
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const functionArgs = JSON.parse(message.tool_calls[0].function.arguments);
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const functionArgsArr = Object.values(functionArgs);
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const functionResponse = await functionToCall.apply(null, functionArgsArr);
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@ -354,8 +363,8 @@ to call the `getCurrentWeather` function with
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arguments. This is data it got back from the first function call we did.
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```js
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{role: "assistant", content: null, function_call: {name: "getLocation", arguments: "{}"}}
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{role: "assistant", content: null, function_call: {name: "getCurrentWeather", arguments: " { "longitude": "10.859", "latitude": "59.955" }"}}
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{"role":"assistant","content":null,"tool_calls":[{"id":"call_Cn1KH8mtHQ2AMbyNwNJTweEP","type":"function","function":{"name":"getLocation","arguments":"{}"}}]}
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{"role":"assistant","content":null,"tool_calls":[{"id":"call_uc1oozJfGTvYEfIzzcsfXfOl","type":"function","function":{"name":"getCurrentWeather","arguments":"{\n\"latitude\": \"10.859\",\n\"longitude\": \"59.955\"\n}"}}]}
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```
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You've now built an AI agent using OpenAI functions and the Node.js SDK! If you're looking for an extra challenge, consider enhancing this app. For example, you could add a function that fetches up-to-date information on events and activities in the user's location.
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@ -386,35 +395,40 @@ async function getCurrentWeather(latitude, longitude) {
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return weatherData;
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}
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const functionDefinitions = [
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const tools = [
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{
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name: "getCurrentWeather",
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description:
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"Get the current weather in a given location given in latitude and longitude",
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parameters: {
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type: "object",
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properties: {
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latitude: {
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type: "string",
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},
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longitude: {
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type: "string",
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type: "function",
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function: {
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name: "getCurrentWeather",
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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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latitude: {
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type: "string",
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},
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longitude: {
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type: "string",
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},
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},
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required: ["longitude", "latitude"],
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},
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required: ["longitude", "latitude"],
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},
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}
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},
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{
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name: "getLocation",
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description: "Get the user's location based on their IP address",
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parameters: {
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type: "object",
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properties: {},
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},
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type: "function",
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function: {
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name: "getLocation",
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description: "Get the user's location based on their IP address",
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parameters: {
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type: "object",
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properties: {},
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},
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}
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},
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];
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const availableFunctions = {
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const availableTools = {
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getCurrentWeather,
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getLocation,
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};
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@ -436,15 +450,15 @@ async function agent(userInput) {
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const response = await openai.chat.completions.create({
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model: "gpt-4",
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messages: messages,
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functions: functionDefinitions,
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tools: tools,
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});
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const { finish_reason, message } = response.choices[0];
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if (finish_reason === "function_call") {
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const functionName = message.function_call.name;
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const functionToCall = availableFunctions[functionName];
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const functionArgs = JSON.parse(message.function_call.arguments);
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if (finish_reason === "tool_calls" && message.tool_calls) {
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const functionName = message.tool_calls[0].function.name;
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const functionToCall = availableTools[functionName];
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const functionArgs = JSON.parse(message.tool_calls[0].function.arguments);
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const functionArgsArr = Object.values(functionArgs);
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const functionResponse = await functionToCall.apply(
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null,
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