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Dart SDK for openAI Apis (GPT-3 & DALL-E), integrate easily the power of OpenAI's state-of-the-art AI models into their Dart applications.

NEW: ChatGPT & Whisper APIs are added to the library and can be used directly. #

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An open-source Client package that allows developers to easily integrate the power of OpenAI's state-of-the-art AI models into their Dart/Flutter applications.

This library provides simple and intuitive methods for making requests to OpenAI's various APIs, including the GPT-3 language model, DALL-E image generation, and more.

The package is designed to be lightweight and easy to use, so you can focus on building your application, rather than worrying about the complexities and errors caused by dealing with HTTP requests.

OpenAI does not have any official Dart library.

Thanks: #

Thanks to the contributors & sponsors of this project that it exists and is still maintained:

Consider helping this project you too.

✨ Key Features #

  • Easy to use methods that reflect exactly the OpenAI documentation, with additional functionalities that make it better to use with Dart Programming Language.
  • Authorize just once, use it anywhere and at any time in your application.
  • Developer-friendly.
  • Stream functionality for completions API & fine-tune events API.
  • Ready examples/snippets for almost everything implmented in the package at /example folder.

👑 Code Progress (100 %) #

💫 Testing Progress (100 %) #

📜 Full Documentation: #

For the full documentation about all members this library offers, check here.

🟢 Usage #

Authentication #

API key #

The OpenAI API uses API keys for authentication. you can get your account API key by visiting API keys of your account.

We highly recommend loading your secret key at runtime from a .env file, you can use the envied package or any other package that does the same job.

// .env
// lib/env/env.dart
import 'package:envied/envied.dart';
part 'env.g.dart';

@Envied(path: ".env")
abstract class Env {
  @EnviedField(varName: 'OPEN_AI_API_KEY') // the .env variable.
  static const apiKey = _Env.apiKey;
// lib/main.dart
void main() {
 OpenAI.apiKey = Env.apiKey; // Initializes the package with that API key, all methods now are ready for use.
 // ..

if no apiKey is set, and you tried to access OpenAI.instance, a MissingApiKeyException will be thrown even before making the actual request.

if the apiKey is set, but it is invalid when making requests, a RequestFailedException will be thrown in your app, check the error handling section for more info.

Setting an organization #

if you belong to a specific organization, you can pass its id to OpenAI.organization like this:

 OpenAI.organization = "ORGANIZATION ID";

If you don't belong actually to any organization, you can just ignore this section, or set it to null.

Learn More From Here.

Settings a default request timeout #

The package make use if the http to make requests, this one have a default timeout of 30 seconds, this means that any requests that takes more than 30 seconds will be cancelled, and a exception will be thrown, to chenge that you will need to set your own default timeout:

OpenAI.requestsTimeOut = Duration(seconds: 60); // 60 seconds.

And now, the time consuming methods will wait for 60 seconds to get a response before throwing an exception.

Setting your own base url #

You can change the base url used in the package to your own, this can be helpful if you want to proxy the requests to the OpenAI API, or if you want to use your own server as a proxy to the OpenAI API.

OpenAI.baseUrl = "https://api.openai.com/v1"; // the default one.

Enable debugging and logs #

You can make the package logs the operations flows and steps by setting the showLogs:

OpenAI.showLogs = true;

This will only log the requests steps such when the request started and finished, when the decoding started...

But if you want to log raw responses that are returned from the API (JSON, RAW...), you can set the showResponsesLogs:

OpenAI.showResponsesLogs = true;

This will log the raw responses that are returned from the API, such when the request is successful, or when it failed. (This don't include the stream responses).

Models #

List Models #

Lists the currently available models, and provides information about each one such as the owner and availability.

List<OpenAIModelModel> models = await OpenAI.instance.model.list();
OpenAIModelModel firstModel = models.first;
print(firstModel.id); // ...
print(firstModel.permission); // ...

Retrieve model #

Retrieves a single model by its id and gets additional pieces of information about it.

OpenAIModelModel model = await OpenAI.instance.model.retrieve("text-davinci-003");

print(model.ownedBy); // ...

If the model id you provided does not exist or isn't available for your account, a RequestFailedException will be thrown, check Error Handling section.

Learn More From Here.

Delete fine tuned models #

OpenAI offers fine tuning feature, which you can make use of it with this package here.

However, if it happen that you want to delete a fine tuned model, you can use the delete() method:

bool isDeleted = await OpenAI.instance.model.delete("fine-tune-id");

print(isDeleted); // ...

Completions #

Create completion #

Creates a predicted completion based on the provided model, prompt & other properties asynchronously.

OpenAICompletionModel completion = await OpenAI.instance.completion.create(
  model: "text-davinci-003",
  prompt: "Dart is a program",
  maxTokens: 20,
  temperature: 0.5,
  n: 1,
  stop: ["\n"],
  echo: true,
  seed: 42,
  bestOf: 2,

print(completion.choices.first.text); // ...
print(completion.systemFingerprint); // ...
print(completion.id); // ...

if the request failed (as an example, if you did pass an invalid model...), a RequestFailedException will be thrown, check Error Handling section.

Create Completion Stream #

In addition to calling the OpenAI.instance.completion.create() which is a Future (asynchronous) and will not return an actual value until the full completion is generated, you can get a Stream of them as they happen to be generated:

Stream<OpenAIStreamCompletionModel> completionStream = OpenAI.instance.completion.createStream(
  model: "text-davinci-003",
  prompt: "Github is ",
  maxTokens: 100,
  temperature: 0.5,
  topP: 1,
  seed: 42,
  stop: '###',
  n: 2,

completionStream.listen((event) {
  final firstCompletionChoice = event.choices.first;

  print(firstCompletionChoice.index); // ...
  print(firstCompletionChoice.text); // ...

Useful: Check also the createStreamText() method

Learn More From Here.

Chat (ChatGPT) #

Create chat completion #

Creates a predicted completion for a chat message(s), from the provided properties:

// the system message that will be sent to the request.
final systemMessage = OpenAIChatCompletionChoiceMessageModel(
  content: [
      "return any message you are given as JSON.",
  role: OpenAIChatMessageRole.assistant,

  // the user message that will be sent to the request.
 final userMessage = OpenAIChatCompletionChoiceMessageModel(
   content: [
       "Hello, I am a chatbot created by OpenAI. How are you today?",

     //! image url contents are allowed only for models with image support such gpt-4.
   role: OpenAIChatMessageRole.user,

// all messages to be sent.
final requestMessages = [

// the actual request.
OpenAIChatCompletionModel chatCompletion = await OpenAI.instance.chat.create(
  model: "gpt-3.5-turbo-1106",
  responseFormat: {"type": "json_object"},
  seed: 6,
  messages: requestMessages,
  temperature: 0.2,
  maxTokens: 500,

print(chatCompletion.choices.first.message); // ...
print(chatCompletion.systemFingerprint); // ...
print(chatCompletion.usage.promptTokens); // ...
print(chatCompletion.id); // ...

Create a chat completion stream #

In addition to calling OpenAI.instance.chat.create() which is a Future (asynchronous) and will resolve only after the whole chat is generated, you can get a Stream of them as they happen to be generated:

// The user message to be sent to the request.
final userMessage = OpenAIChatCompletionChoiceMessageModel(
  content: [
      "Hello my friend!",
  role: OpenAIChatMessageRole.user,

// The request to be sent.
final chatStream = OpenAI.instance.chat.createStream(
  model: "gpt-3.5-turbo",
  messages: [
  seed: 423,
  n: 2,

// Listen to the stream.
  (streamChatCompletion) {
    final content = streamChatCompletion.choices.first.delta.content;
  onDone: () {

Tools ( new implementation of functions calling) #

The chat API offer the tools feature which allows for calling functions from the chat API, this feature is implemented in the package, and can be used like the following, please note that this is just a showcase, and you should handle the edge cases in your app such when there is no tool call, or when the tool call is not the one you sent, etc...:

 OpenAI.apiKey = Env.apiKey;

// The function to be called by the tool.
void sumNumbers(int number1, int number2) {
  print("Your sum answer is ${number1 + number2}");

// The tool object that wilm be sent to the API.
final sumNumbersTool = OpenAIToolModel(
    type: "function",
  function: OpenAIFunctionModel.withParameters(
    name: "sumOfTwoNumbers",
    parameters: [
        name: "number1",
        description: "The first number to add",
        name: "number2",
        description: "The second number to add",

  // The user text message that will be sent to the API.
final userMessage = OpenAIChatCompletionChoiceMessageModel(
    content: [
        "What is the sum of 9996 and 3?",
    role: OpenAIChatMessageRole.user,

  // The actual call.
final chat = await OpenAI.instance.chat.create(
    model: "gpt-3.5-turbo",
    messages: [userMessage],
    tools: [sumNumbersTool],

// ! This handling is only for showcase and not completed as edge cases will not be handled that you should handle in your app.

final message = chat.choices.first.message;

// Wether the message has a tool call.
  if (message.haveToolCalls) {
  final call = message.toolCalls!.first;

    // Wether the tool call is the one we sent.
  if (call.function.name == "sumOfTwoNumbers") {
      // decode the arguments from the tool call.
    final decodedArgs = jsonDecode(call.function.arguments);

    final number1 = decodedArgs["number1"];
    final number2 = decodedArgs["number2"];

    // Call the function with the arguments.
    sumNumbers(number1, number2);

Learn more from here.

Edits #

Create edit #

Creates an edited version of the given prompt based on the used model.

OpenAIEditModel edit = await OpenAI.instance.edit.create(
  model: "text-davinci-edit-001";
  instruction: "remote all '!'from input text",
  input: "Hello!!, I! need to be ! somethi!ng"
  n: 1,
  temperature: 0.8,

// Prints the choices.
for (int index = 0; index < edit.choices.length; index++) {

Learn More From Here.

Images #

Create image #

Generates a new image based on a prompt given.

 OpenAIImageModel image = await OpenAI.instance.image.create(
  prompt: 'an astronaut on the sea',
  n: 1,
  size: OpenAIImageSize.size1024,
  responseFormat: OpenAIImageResponseFormat.url,

// Printing the output to the console.
for (int index = 0; index < image.data.length; index++) {
  final currentItem = image.data[index];

Create image edit #

Creates an edited or extended image given an original image and a prompt.

OpenAIImageModel imageEdits = await OpenAI.instance.image.edit(
  prompt: 'mask the image with color red',
  image: File(/* IMAGE PATH HERE */),
  mask: File(/* MASK PATH HERE */),
  n: 1,
  size: OpenAIImageSize.size1024,
  responseFormat: OpenAIImageResponseFormat.b64Json,

for (int index = 0; index < imageEdits.data.length; index++) {
  final currentItem = imageEdits.data[index];

Create image variation #

Creates a variation of a given image.

// Creates the Image Variation
final imageVariations = await OpenAI.instance.image.variation(
  model: "dall-e-2",
  image: File("dart.png"),
  n: 4,
  size: OpenAIImageSize.size512,
  responseFormat: OpenAIImageResponseFormat.url,

 // Prints the output to the console.
for (var index = 0; index < imageVariations.data.length; index++) {
  final currentItem = imageVariations.data[index];

Learn More From Here.

Embeddings #

Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.

Create embeddings #

final embedding = await OpenAI.instance.embedding.create(
  model: "text-embedding-ada-002",
  input: "This is a sample text",

for (int index = 0; index < embedding.data.length; index++) {
  final currentItem = embedding.data[index];

Learn More From Here.

Audio #

Create Speech #

For creating a speech from a text, you can use the createSpeech() method directly by providing the required params:

// The speech request.
File speechFile = await OpenAI.instance.audio.createSpeech(
  model: "tts-1",
  input: "Say my name is Anas",
  voice: "nova",
  responseFormat: OpenAIAudioSpeechResponseFormat.mp3,
  outputDirectory: await Directory("speechOutput").create(),
  outputFileName: "anas",

// The file result.

Note: the outputDirectory and outputFileName are helpers for this method, you can use them to save the audio file to a specific directory with a specific name, with the file extension being extracted from the responseFormat. if you don't want to use them, just ignore it, and the audio file will be saved to the default directory of your app, with the output file name.

The example snippet above will place a generated anas.mp3 in the speechOutput directory in your project.

Create transcription #

For transcribing an audio File, you can use the createTranscription() method directly by providing the file property:

OpenAIAudioModel transcription = OpenAI.instance.audio.createTranscription(
  file: File(/* THE FILE PATH*/),
  model: "whisper-1",
  responseFormat: OpenAIAudioResponseFormat.json,

// print the transcription.

Create translation #

to get access to the translation API, and translate an audio file to english, you can use the createTranslation() method, by providing the `file`` property:

OpenAIAudioModel translation = await OpenAI.instance.audio.createTranslation(
  file: File(/* THE FILE PATH*/),
  model: "whisper-1",
  responseFormat: OpenAIAudioResponseFormat.text,

// print the translation.

Learn more from here.

Files #

Files are used to upload documents that can be used with features like Fine-tuning.

List files #

Get a list of all the uploaded files o-to your OpenAI account.

List<OpenAIFileModel> files = await OpenAI.instance.file.list();

print(files.first.fileName); // ...
print(files.first.id); // ...

Upload file #

Upload a file that contains document(s) to be used across various endpoints/features. Currently, the size of all the files uploaded by one organization can be up to 1 GB. Please contact us if you need to increase the storage limit.

OpenAIFileModel uploadedFile = await OpenAI.instance.file.upload(
 file: File("/* FILE PATH HERE */"),
 purpose: "fine-tuning",

print(uploadedFile.id); // ...

Delete file #

Deletes an existent file by it's id.

bool isFileDeleted = await OpenAI.instance.file.delete("/* FILE ID */");


Retrieve file #

Fetches for a single file by it's id and returns informations about it.

OpenAIFileModel file = await OpenAI.instance.file.retrieve("FILE ID");

Retrieve file content #

Fetches for a single file content by it's id.

dynamic fileContent  = await OpenAI.instance.file.retrieveContent("FILE ID");


Learn More From Here.

Fine Tunes #

Create fine-tune #

Creates a job that fine-tunes a specified model from a given dataset, and returns a fine-tuned object about the enqueued job.

OpenAIFineTuneModel fineTune = await OpenAI.instance.fineTune.create(
 trainingFile: "FILE ID",

print(fineTune.status); // ...

List fine-tunes #

List your organization's fine-tuning jobs.

List<OpenAIFineTuneModel> fineTunes = await OpenAI.instance.fineTune.list();

print(fineTunes.first); // ...

Retrieve fine-tune #

Retrieves a fine-tune by its id.

OpenAIFineTuneModel fineTune = await OpenAI.instance.fineTune.retrieve("FINE TUNE ID");

print(fineTune.id); // ...

Cancel fine-tune #

Cancels a fine-tune job by its id, and returns it.

OpenAIFineTuneModel cancelledFineTune = await OpenAI.instance.fineTune.cancel("FINE TUNE ID");

print(cancelledFineTune.status); // ...

List fine-tune events #

Lists a single fine-tune progress events by it's id.

 List<OpenAIFineTuneEventModel> events = await OpenAI.instance.fineTune.listEvents("FINE TUNE ID");

 print(events.first.message); // ...

Listen to fine-tune events Stream #

Streams all events of a fine-tune job by its id, as they happen.

This is a long-running operation that will not return until the fine-tune job is terminated.

The stream will emit an event every time a new event is available.

Stream<OpenAIFineTuneEventStreamModel> eventsStream = OpenAI.instance.fineTune.listEventsStream("FINE TUNE ID");

eventsStream.listen((event) {

Delete fine-tune #

Deletes a fine-tune job by its id.

 bool deleted = await OpenAI.instance.fineTune.delete("FINE TUNE ID");

print(deleted); // ...

Learn More From Here.

Moderations #

Create moderation #

Classifies if text violates OpenAI's Content Policy

OpenAIModerationModel moderation = await OpenAI.instance.moderation.create(
  input: "I want to kill him",

print(moderation.results); // ...
print(moderation.results.first.categories.hate); // ...

Learn More From Here.

Error Handling #

Any time an error happens from the OpenAI API ends (As Example: when you try to create an image variation from a non-image file.. , a RequestFailedException will be thrown automatically inside your Flutter / Dart app, you can use a try-catch to catch that error, and make an action based on it:

try {

// This will throw an error.
 final errorVariation = await OpenAI.instance.image.variation(
  image: File(/*PATH OF NON-IMAGE FILE*/),
} on RequestFailedException catch(e) {

Want To Help ? #

Please, Just remember that any kind of help related to these tasks are welcome, for the sake of the community.

  • Writing documentation: if you see any class, property, method.. that you know what does and it is undocumented, please take from your time 2 minutes and help another developer that doesn't.
  • Code Refactoring: I know this is my job not yours :), but if you can and want, you're more than welcome.
  • Reviewing code: if it happens that there is a better way to make something happen in the SDK, please just let me know.
  • if you tried any sample of use cases, examples of yours and wanted to include it in the examples/, please go ahead.
  • Mention any updates if they exists in the API, Dart, a certain package, or even Flutter that relates to this package.
  • Donate to the project, it will help me to keep working on it, and make it better.
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Dart SDK for openAI Apis (GPT-3 & DALL-E), integrate easily the power of OpenAI's state-of-the-art AI models into their Dart applications.

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