OpenAI Node API Library

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.

Installation

npm install --save openai
# or
yarn add openai

Usage

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: 'my api key', // defaults to process.env["OPENAI_API_KEY"]
});

async function main() {
  const completion = await openai.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'gpt-3.5-turbo',
  });

  console.log(completion.choices);
}

main();

Streaming Responses

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 part of stream) {
    process.stdout.write(part.choices[0]?.delta?.content || '');
  }
}

main();

If you need to cancel a stream, you can break from the loop or call stream.controller.abort().

Request & Response types

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: 'my api key', // defaults to process.env["OPENAI_API_KEY"]
});

async function main() {
  const params: OpenAI.Chat.CompletionCreateParams = {
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'gpt-3.5-turbo',
  };
  const completion: 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.

File Uploads

Request parameters that correspond to file uploads can be passed in many different forms:

• File (or an object with the same structure)
• a fetch Response (or an object with the same structure)
• an fs.ReadStream
• the return value of our toFile helper

import 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',
});

Handling errors

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 fineTune = await openai.fineTunes
    .create({ training_file: 'file-XGinujblHPwGLSztz8cPS8XY' })
    .catch((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:

Azure OpenAI

An example of using this library with Azure OpenAI can be found here.

Please note there are subtle differences in API shape & behavior between the Azure OpenAI API and the OpenAI API, so using this library with Azure OpenAI may result in incorrect types, which can lead to bugs.

See @azure/openai for an Azure-specific SDK provided by Microsoft.

Retries

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), 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,
});

Timeouts

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.

Auto-pagination

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 job of openai.fineTuning.jobs.list({ limit: 20 })) {
    allFineTuningJobs.push(job);
  }
  return allFineTuningJobs;
}

Alternatively, you can make request a single page at a time:

let page = await openai.fineTuning.jobs.list({ limit: 20 });
for (const job of page.data) {
  console.log(job);
}

// Convenience methods are provided for manually paginating:
while (page.hasNextPage()) {
  page = page.getNextPage();
  // ...
}

Advanced Usage

Accessing raw Response data (e.g., headers)

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: completions, 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(completions.choices);

Configuring an HTTP(S) Agent (e.g., for proxies)

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({
  baseURL: 'http://localhost:8080/test-api',
  httpAgent: new http.Agent({ keepAlive: false }),
})

Semantic Versioning

This package generally attempts to follow SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:

1. Changes that only affect static types, without breaking runtime behavior.
2. Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals).
3. Changes that we do not expect to impact the vast majority of users in practice.

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.

Requirements

The following runtimes are supported:

• Node.js 16 LTS or later (non-EOL) versions.
• Deno v1.28.0 or higher, using import OpenAI from "npm:openai". Deno Deploy is not yet supported.
• Cloudflare Workers.
• Vercel Edge Runtime.

If you are interested in other runtime environments, please open or upvote an issue on GitHub.