The openai npm package is a Node.js client library for accessing the OpenAI API, which provides access to powerful AI models such as GPT-3 for natural language processing tasks, including text generation, translation, summarization, and more. The package allows developers to easily integrate OpenAI's AI capabilities into their Node.js applications.
Text Completion
Generates text completions for a given prompt using the GPT-3 model.
const { Configuration, OpenAIApi } = require('openai');
const configuration = new Configuration({
apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);
openai.createCompletion({
model: 'text-davinci-003',
prompt: 'Translate the following English text to French: Hello, how are you?',
max_tokens: 60
}).then(response => {
console.log(response.data.choices[0].text);
});Text Classification
Classifies a piece of text into one of the specified categories.
const { Configuration, OpenAIApi } = require('openai');
const configuration = new Configuration({
apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);
openai.createClassification({
model: 'text-davinci-003',
examples: [
['A movie about space wars and intergalactic politics', 'Science Fiction'],
['A film focusing on the love life of a New York City woman', 'Romance']
],
query: 'A story about a boy who learns he is a wizard and attends a magical school',
labels: ['Science Fiction', 'Romance', 'Fantasy']
}).then(response => {
console.log(response.data);
});Text Summarization
Summarizes a longer piece of text into a concise version.
const { Configuration, OpenAIApi } = require('openai');
const configuration = new Configuration({
apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);
openai.createCompletion({
model: 'text-davinci-003',
prompt: 'Summarize the following text: ...',
max_tokens: 60,
temperature: 0.7
}).then(response => {
console.log(response.data.choices[0].text);
});This package provides access to IBM Watson's AI services, which include natural language processing, speech to text, text to speech, and language translation. It is similar to openai in providing AI-powered language services, but it uses IBM's Watson AI instead of OpenAI's models.
The Google Cloud npm package allows developers to interact with Google Cloud services, including its AI and machine learning services like the Natural Language API and the Translation API. It offers functionalities similar to openai but is integrated with Google's cloud ecosystem.
This package is part of Microsoft's Azure Cognitive Services and provides capabilities for speech recognition, text-to-speech, and speech translation. It offers different services compared to openai, focusing more on speech technologies rather than text-based AI models.
This library provides convenient access to the OpenAI REST API from TypeScript or JavaScript.
It is generated from our OpenAPI specification with Stainless.
To learn how to use the OpenAI API, check out our API Reference and Documentation.
npm install openai
You can import in Deno via:
import OpenAI from 'https://deno.land/x/openai@v4.44.0/mod.ts';
The full API of this library can be found in api.md file along with many code examples. The code below shows how to get started using the chat completions API.
import OpenAI from 'openai';
const openai = new OpenAI({
apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});
async function main() {
const chatCompletion = await openai.chat.completions.create({
messages: [{ role: 'user', content: 'Say this is a test' }],
model: 'gpt-3.5-turbo',
});
}
main();
We provide support for streaming responses using Server Sent Events (SSE).
import OpenAI from 'openai';
const openai = new OpenAI();
async function main() {
const stream = await openai.chat.completions.create({
model: 'gpt-4',
messages: [{ role: 'user', content: 'Say this is a test' }],
stream: true,
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content || '');
}
}
main();
If you need to cancel a stream, you can break from the loop
or call stream.controller.abort().
This library includes TypeScript definitions for all request params and response fields. You may import and use them like so:
import OpenAI from 'openai';
const openai = new OpenAI({
apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});
async function main() {
const params: OpenAI.Chat.ChatCompletionCreateParams = {
messages: [{ role: 'user', content: 'Say this is a test' }],
model: 'gpt-3.5-turbo',
};
const chatCompletion: OpenAI.Chat.ChatCompletion = await openai.chat.completions.create(params);
}
main();
Documentation for each method, request param, and response field are available in docstrings and will appear on hover in most modern editors.
[!IMPORTANT] Previous versions of this SDK used a
Configurationclass. See the v3 to v4 migration guide.
When interacting with the API some actions such as starting a Run and adding files to vector stores are asynchronous and take time to complete. The SDK includes helper functions which will poll the status until it reaches a terminal state and then return the resulting object. If an API method results in an action which could benefit from polling there will be a corresponding version of the method ending in 'AndPoll'.
For instance to create a Run and poll until it reaches a terminal state you can run:
const run = await openai.beta.threads.runs.createAndPoll(thread.id, {
assistant_id: assistantId,
});
More information on the lifecycle of a Run can be found in the Run Lifecycle Documentation
When creating and interacting with vector stores, you can use the polling helpers to monitor the status of operations. For convenience, we also provide a bulk upload helper to allow you to simultaneously upload several files at once.
const fileList = [
createReadStream('/home/data/example.pdf'),
...
];
const batch = await openai.vectorStores.fileBatches.uploadAndPoll(vectorStore.id, fileList);
The SDK also includes helpers to process streams and handle the incoming events.
const run = openai.beta.threads.runs
.stream(thread.id, {
assistant_id: assistant.id,
})
.on('textCreated', (text) => process.stdout.write('\nassistant > '))
.on('textDelta', (textDelta, snapshot) => process.stdout.write(textDelta.value))
.on('toolCallCreated', (toolCall) => process.stdout.write(`\nassistant > ${toolCall.type}\n\n`))
.on('toolCallDelta', (toolCallDelta, snapshot) => {
if (toolCallDelta.type === 'code_interpreter') {
if (toolCallDelta.code_interpreter.input) {
process.stdout.write(toolCallDelta.code_interpreter.input);
}
if (toolCallDelta.code_interpreter.outputs) {
process.stdout.write('\noutput >\n');
toolCallDelta.code_interpreter.outputs.forEach((output) => {
if (output.type === 'logs') {
process.stdout.write(`\n${output.logs}\n`);
}
});
}
}
});
More information on streaming helpers can be found in the dedicated documentation: helpers.md
This library provides several conveniences for streaming chat completions, for example:
import OpenAI from 'openai';
const openai = new OpenAI();
async function main() {
const stream = await openai.beta.chat.completions.stream({
model: 'gpt-4',
messages: [{ role: 'user', content: 'Say this is a test' }],
stream: true,
});
stream.on('content', (delta, snapshot) => {
process.stdout.write(delta);
});
// or, equivalently:
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content || '');
}
const chatCompletion = await stream.finalChatCompletion();
console.log(chatCompletion); // {id: "…", choices: […], …}
}
main();
Streaming with openai.beta.chat.completions.stream({…}) exposes
various helpers for your convenience including event handlers and promises.
Alternatively, you can use openai.chat.completions.create({ stream: true, … })
which only returns an async iterable of the chunks in the stream and thus uses less memory
(it does not build up a final chat completion object for you).
If you need to cancel a stream, you can break from a for await loop or call stream.abort().
We provide the openai.beta.chat.completions.runTools({…})
convenience helper for using function tool calls with the /chat/completions endpoint
which automatically call the JavaScript functions you provide
and sends their results back to the /chat/completions endpoint,
looping as long as the model requests tool calls.
If you pass a parse function, it will automatically parse the arguments for you
and returns any parsing errors to the model to attempt auto-recovery.
Otherwise, the args will be passed to the function you provide as a string.
If you pass tool_choice: {function: {name: …}} instead of auto,
it returns immediately after calling that function (and only loops to auto-recover parsing errors).
import OpenAI from 'openai';
const client = new OpenAI();
async function main() {
const runner = client.beta.chat.completions
.runTools({
model: 'gpt-3.5-turbo',
messages: [{ role: 'user', content: 'How is the weather this week?' }],
tools: [
{
type: 'function',
function: {
function: getCurrentLocation,
parameters: { type: 'object', properties: {} },
},
},
{
type: 'function',
function: {
function: getWeather,
parse: JSON.parse, // or use a validation library like zod for typesafe parsing.
parameters: {
type: 'object',
properties: {
location: { type: 'string' },
},
},
},
},
],
})
.on('message', (message) => console.log(message));
const finalContent = await runner.finalContent();
console.log();
console.log('Final content:', finalContent);
}
async function getCurrentLocation() {
return 'Boston'; // Simulate lookup
}
async function getWeather(args: { location: string }) {
const { location } = args;
// … do lookup …
return { temperature, precipitation };
}
main();
// {role: "user", content: "How's the weather this week?"}
// {role: "assistant", tool_calls: [{type: "function", function: {name: "getCurrentLocation", arguments: "{}"}, id: "123"}
// {role: "tool", name: "getCurrentLocation", content: "Boston", tool_call_id: "123"}
// {role: "assistant", tool_calls: [{type: "function", function: {name: "getWeather", arguments: '{"location": "Boston"}'}, id: "1234"}]}
// {role: "tool", name: "getWeather", content: '{"temperature": "50degF", "preciptation": "high"}', tool_call_id: "1234"}
// {role: "assistant", content: "It's looking cold and rainy - you might want to wear a jacket!"}
//
// Final content: "It's looking cold and rainy - you might want to wear a jacket!"
Like with .stream(), we provide a variety of helpers and events.
Note that runFunctions was previously available as well, but has been deprecated in favor of runTools.
Read more about various examples such as with integrating with zod, next.js, and proxying a stream to the browser.
Request parameters that correspond to file uploads can be passed in many different forms:
File (or an object with the same structure)fetch Response (or an object with the same structure)fs.ReadStreamtoFile helperimport fs from 'fs';
import fetch from 'node-fetch';
import OpenAI, { toFile } from 'openai';
const openai = new OpenAI();
// If you have access to Node `fs` we recommend using `fs.createReadStream()`:
await openai.files.create({ file: fs.createReadStream('input.jsonl'), purpose: 'fine-tune' });
// Or if you have the web `File` API you can pass a `File` instance:
await openai.files.create({ file: new File(['my bytes'], 'input.jsonl'), purpose: 'fine-tune' });
// You can also pass a `fetch` `Response`:
await openai.files.create({ file: await fetch('https://somesite/input.jsonl'), purpose: 'fine-tune' });
// Finally, if none of the above are convenient, you can use our `toFile` helper:
await openai.files.create({
file: await toFile(Buffer.from('my bytes'), 'input.jsonl'),
purpose: 'fine-tune',
});
await openai.files.create({
file: await toFile(new Uint8Array([0, 1, 2]), 'input.jsonl'),
purpose: 'fine-tune',
});
When the library is unable to connect to the API,
or if the API returns a non-success status code (i.e., 4xx or 5xx response),
a subclass of APIError will be thrown:
async function main() {
const job = await openai.fineTuning.jobs
.create({ model: 'gpt-3.5-turbo', training_file: 'file-abc123' })
.catch(async (err) => {
if (err instanceof OpenAI.APIError) {
console.log(err.status); // 400
console.log(err.name); // BadRequestError
console.log(err.headers); // {server: 'nginx', ...}
} else {
throw err;
}
});
}
main();
Error codes are as followed:
| Status Code | Error Type |
|---|---|
| 400 | BadRequestError |
| 401 | AuthenticationError |
| 403 | PermissionDeniedError |
| 404 | NotFoundError |
| 422 | UnprocessableEntityError |
| 429 | RateLimitError |
| >=500 | InternalServerError |
| N/A | APIConnectionError |
To use this library with Azure OpenAI, use the AzureOpenAI
class instead of the OpenAI class.
[!IMPORTANT] The Azure API shape differs from the core API shape which means that the static types for responses / params won't always be correct.
const openai = new AzureOpenAI();
const result = await openai.chat.completions.create({
model: 'gpt-4-1106-preview',
messages: [{ role: 'user', content: 'Say hello!' }],
});
console.log(result.choices[0]!.message?.content);
Certain errors will be automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors will all be retried by default.
You can use the maxRetries option to configure or disable this:
// Configure the default for all requests:
const openai = new OpenAI({
maxRetries: 0, // default is 2
});
// Or, configure per-request:
await openai.chat.completions.create({ messages: [{ role: 'user', content: 'How can I get the name of the current day in Node.js?' }], model: 'gpt-3.5-turbo' }, {
maxRetries: 5,
});
Requests time out after 10 minutes by default. You can configure this with a timeout option:
// Configure the default for all requests:
const openai = new OpenAI({
timeout: 20 * 1000, // 20 seconds (default is 10 minutes)
});
// Override per-request:
await openai.chat.completions.create({ messages: [{ role: 'user', content: 'How can I list all files in a directory using Python?' }], model: 'gpt-3.5-turbo' }, {
timeout: 5 * 1000,
});
On timeout, an APIConnectionTimeoutError is thrown.
Note that requests which time out will be retried twice by default.
List methods in the OpenAI API are paginated.
You can use for await … of syntax to iterate through items across all pages:
async function fetchAllFineTuningJobs(params) {
const allFineTuningJobs = [];
// Automatically fetches more pages as needed.
for await (const fineTuningJob of openai.fineTuning.jobs.list({ limit: 20 })) {
allFineTuningJobs.push(fineTuningJob);
}
return allFineTuningJobs;
}
Alternatively, you can make request a single page at a time:
let page = await openai.fineTuning.jobs.list({ limit: 20 });
for (const fineTuningJob of page.data) {
console.log(fineTuningJob);
}
// Convenience methods are provided for manually paginating:
while (page.hasNextPage()) {
page = page.getNextPage();
// ...
}
The "raw" Response returned by fetch() can be accessed through the .asResponse() method on the APIPromise type that all methods return.
You can also use the .withResponse() method to get the raw Response along with the parsed data.
const openai = new OpenAI();
const response = await openai.chat.completions
.create({ messages: [{ role: 'user', content: 'Say this is a test' }], model: 'gpt-3.5-turbo' })
.asResponse();
console.log(response.headers.get('X-My-Header'));
console.log(response.statusText); // access the underlying Response object
const { data: chatCompletion, response: raw } = await openai.chat.completions
.create({ messages: [{ role: 'user', content: 'Say this is a test' }], model: 'gpt-3.5-turbo' })
.withResponse();
console.log(raw.headers.get('X-My-Header'));
console.log(chatCompletion);
This library is typed for convenient access to the documented API. If you need to access undocumented endpoints, params, or response properties, the library can still be used.
To make requests to undocumented endpoints, you can use client.get, client.post, and other HTTP verbs.
Options on the client, such as retries, will be respected when making these requests.
await client.post('/some/path', {
body: { some_prop: 'foo' },
query: { some_query_arg: 'bar' },
});
To make requests using undocumented parameters, you may use // @ts-expect-error on the undocumented
parameter. This library doesn't validate at runtime that the request matches the type, so any extra values you
send will be sent as-is.
client.foo.create({
foo: 'my_param',
bar: 12,
// @ts-expect-error baz is not yet public
baz: 'undocumented option',
});
For requests with the GET verb, any extra params will be in the query, all other requests will send the
extra param in the body.
If you want to explicitly send an extra argument, you can do so with the query, body, and headers request
options.
To access undocumented response properties, you may access the response object with // @ts-expect-error on
the response object, or cast the response object to the requisite type. Like the request params, we do not
validate or strip extra properties from the response from the API.
By default, this library uses node-fetch in Node, and expects a global fetch function in other environments.
If you would prefer to use a global, web-standards-compliant fetch function even in a Node environment,
(for example, if you are running Node with --experimental-fetch or using NextJS which polyfills with undici),
add the following import before your first import from "OpenAI":
// Tell TypeScript and the package to use the global web fetch instead of node-fetch.
// Note, despite the name, this does not add any polyfills, but expects them to be provided if needed.
import 'openai/shims/web';
import OpenAI from 'openai';
To do the inverse, add import "openai/shims/node" (which does import polyfills).
This can also be useful if you are getting the wrong TypeScript types for Response (more details).
You may also provide a custom fetch function when instantiating the client,
which can be used to inspect or alter the Request or Response before/after each request:
import { fetch } from 'undici'; // as one example
import OpenAI from 'openai';
const client = new OpenAI({
fetch: async (url: RequestInfo, init?: RequestInit): Promise<Response> => {
console.log('About to make a request', url, init);
const response = await fetch(url, init);
console.log('Got response', response);
return response;
},
});
Note that if given a DEBUG=true environment variable, this library will log all requests and responses automatically.
This is intended for debugging purposes only and may change in the future without notice.
By default, this library uses a stable agent for all http/https requests to reuse TCP connections, eliminating many TCP & TLS handshakes and shaving around 100ms off most requests.
If you would like to disable or customize this behavior, for example to use the API behind a proxy, you can pass an httpAgent which is used for all requests (be they http or https), for example:
import http from 'http';
import { HttpsProxyAgent } from 'https-proxy-agent';
// Configure the default for all requests:
const openai = new OpenAI({
httpAgent: new HttpsProxyAgent(process.env.PROXY_URL),
});
// Override per-request:
await openai.models.list({
httpAgent: new http.Agent({ keepAlive: false }),
});
This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:
We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.
We are keen for your feedback; please open an issue with questions, bugs, or suggestions.
TypeScript >= 4.5 is supported.
The following runtimes are supported:
import OpenAI from "npm:openai"."node" environment ("jsdom" is not supported at this time).Note that React Native is not supported at this time.
If you are interested in other runtime environments, please open or upvote an issue on GitHub.
FAQs
The official TypeScript library for the OpenAI API
The npm package openai receives a total of 979,439 weekly downloads. As such, openai popularity was classified as popular.
We found that openai demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 5 open source maintainers collaborating on the project.
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