OpenAI Dart Client #

Unofficial Dart client for OpenAI API.

Features #

Generated from the official OpenAI OpenAPI specification
Fully type-safe, documented and tested
All platforms supported (including streaming on web)
Authentication with organization support
Custom base URL, headers and query params support (e.g. HTTP proxies)
Custom HTTP client support (e.g. SOCKS5 proxies or advanced use cases)
Partial Azure OpenAI API support

Supported endpoints:

Chat (with functions and streaming support)
Completions (with streaming support)
Embeddings
Fine-tuning
Images
Models
Moderations

Usage #

Authentication #

The OpenAI API uses API keys for authentication. Visit your API Keys page to retrieve the API key you'll use in your requests.

Remember that your API key is a secret!
Do not share it with others or expose it in any client-side code (browsers, apps). Production requests must be routed through your own backend server where your API key can be securely loaded from an environment variable or key management service.

final openaiApiKey = Platform.environment['OPENAI_API_KEY'];
final client = OpenAIClient(apiKey: openaiApiKey);

Organization (optional)

For users who belong to multiple organizations, you can specify which organization is used for an API request. Usage from these API requests will count against the specified organization's subscription quota.

final client = OpenAIClient(
  apiKey: openaiApiKey,
  organization: 'org-dtDDtkEGoFccn5xaP5W1p3Rr',
);

Chat #

Given a list of messages comprising a conversation, the model will return a response.

Create chat completion:

final res = await client.createChatCompletion(
  request: CreateChatCompletionRequest(
    model: ChatCompletionModel.modelId('gpt-4'),
    messages: [
      ChatCompletionMessage.system(
        content: 'You are a helpful assistant.',
      ),
      ChatCompletionMessage.user(
        content: ChatCompletionUserMessageContent.string('Hello!'),
      ),
    ],
    temperature: 0,
  ),
);
print(res.choices.first.message.content);
// Hello! How can I assist you today?

ChatCompletionModel is a sealed class that offers two ways to specify the model:

ChatCompletionModel.modelId('model-id'): the model ID as string (e.g. 'gpt-4' or your fine-tuned model ID).
ChatCompletionModel.model(ChatCompletionModels.gpt4): a value from ChatCompletionModels enum which lists all of the available models.

ChatCompletionMessage is a sealed class that supports the following message types:

ChatCompletionMessage.system(): a system message.
ChatCompletionMessage.user(): a user message.
ChatCompletionMessage.assistant(): an assistant message.
ChatCompletionMessage.tool(): a tool message.
ChatCompletionMessage.function(): a function message.

ChatCompletionMessage.user() takes a ChatCompletionUserMessageContent object that supports the following content types:

ChatCompletionUserMessageContent.string('content'): string content.
ChatCompletionUserMessageContent.parts([...]): multi-modal content (check the 'Multi-modal prompt' section below).
- ChatCompletionMessageContentPart.text('content'): text content.
- ChatCompletionMessageContentPart.image(imageUrl: ...): image content.

Stream chat completion:

final stream = client.createChatCompletionStream(
  request: CreateChatCompletionRequest(
    model: ChatCompletionModel.modelId('gpt-3.5-turbo'),
    messages: [
      ChatCompletionMessage.system(
        content:
            'You are a helpful assistant that replies only with numbers '
            'in order without any spaces or commas',
      ),
      ChatCompletionMessage.user(
        content: ChatCompletionUserMessageContent.string(
          'List the numbers from 1 to 9',
        ),
      ),
    ],
  ),
);
await for (final res in stream) {
  print(res.choices.first.delta.content);
}
// 123
// 456
// 789

Multi-modal prompt:

final res = await client.createChatCompletion(
  request: CreateChatCompletionRequest(
    model: ChatCompletionModel.model(
      ChatCompletionModels.gpt4VisionPreview,
    ),
    messages: [
      ChatCompletionMessage.system(
        content: 'You are a helpful assistant.',
      ),
      ChatCompletionMessage.user(
        content: ChatCompletionUserMessageContent.parts(
          [
            ChatCompletionMessageContentPart.text(
              text: 'What fruit is this?',
            ),
            ChatCompletionMessageContentPart.image(
              imageUrl: ChatCompletionMessageImageUrl(
                url: 'https://upload.wikimedia.org/wikipedia/commons/9/92/95apple.jpeg',
              ),
            ),
          ],
        ),
      ),
    ],
  ),
);
print(res.choices.first.message.content);
// The fruit in the image is an apple.

JSON mode:

final res = await client.createChatCompletion(
  request: CreateChatCompletionRequest(
    model: ChatCompletionModel.model(
      ChatCompletionModels.gpt41106Preview,
    ),
    messages: [
      ChatCompletionMessage.system(
        content:
          'You are a helpful assistant. That extracts names from text '
          'and returns them in a JSON array.',
      ),
      ChatCompletionMessage.user(
        content: ChatCompletionUserMessageContent.string(
          'John, Mary, and Peter.',
        ),
      ),
    ],
    temperature: 0,
    responseFormat: ChatCompletionResponseFormat(
      type: ChatCompletionResponseFormatType.jsonObject,
    ),
  ),
);
// { "names": ["John", "Mary", "Peter"] }

Tools:

const function = FunctionObject(
  name: 'get_current_weather',
  description: 'Get the current weather in a given location',
  parameters: {
    'type': 'object',
    'properties': {
      'location': {
        'type': 'string',
        'description': 'The city and state, e.g. San Francisco, CA',
      },
      'unit': {
        'type': 'string',
        'description': 'The unit of temperature to return',
        'enum': ['celsius', 'fahrenheit'],
      },
    },
    'required': ['location'],
  },
);
const tool = ChatCompletionTool(
  type: ChatCompletionToolType.function,
  function: function,
);

final res1 = await client.createChatCompletion(
  request: CreateChatCompletionRequest(
    model: const ChatCompletionModel.model(
      ChatCompletionModels.gpt35Turbo,
    ),
    messages: [
      ChatCompletionMessage.system(
        content: 'You are a helpful assistant.',
      ),
      ChatCompletionMessage.user(
        content: ChatCompletionUserMessageContent.string(
          'What’s the weather like in Boston right now?',
        ),
      ),
    ],
    tools: [tool],
    toolChoice: ChatCompletionToolChoiceOption.tool(
      ChatCompletionNamedToolChoice(
        type: ChatCompletionNamedToolChoiceType.function,
        function: ChatCompletionFunctionCallOption(name: function.name),
      ),
    ),
  ),
);

final toolCall = res1.choices.first.message.toolCalls!.first;
final functionCall = toolCall.function;
final arguments = json.decode(functionCall.arguments) as Map<String, dynamic>;
final functionResult = getCurrentWeather(arguments['location'], arguments['unit']);

final res2 = await client.createChatCompletion(
  request: CreateChatCompletionRequest(
    model: ChatCompletionModel.modelId('gpt-3.5-turbo'),
    messages: [
      ChatCompletionMessage.system(
        content: 'You are a helpful assistant.',
      ),
      ChatCompletionMessage.user(
        content: ChatCompletionUserMessageContent.string(
          'What’s the weather like in Boston right now?',
        ),
      ),
      ChatCompletionMessage.tool(
        toolCallId: toolCall.id,
        content: json.encode(functionResult),
      ),
    ],
    tools: [tool],
  ),
);
final answer = res2.choices.first.message.content;
// The weather in Boston right now is sunny with a temperature of 22°C

Function calling: (deprecated in favor of tools)

const function = FunctionObject(
  name: 'get_current_weather',
  description: 'Get the current weather in a given location',
  parameters: {
    'type': 'object',
    'properties': {
      'location': {
        'type': 'string',
        'description': 'The city and state, e.g. San Francisco, CA',
      },
      'unit': {
        'type': 'string',
        'description': 'The unit of temperature to return',
        'enum': ['celsius', 'fahrenheit'],
      },
    },
    'required': ['location'],
  },
);

final res1 = await client.createChatCompletion(
  request: CreateChatCompletionRequest(
    model: ChatCompletionModel.modelId('gpt-3.5-turbo'),
    messages: [
      ChatCompletionMessage.system(
        content: 'You are a helpful assistant.',
      ),
      ChatCompletionMessage.user(
        content: ChatCompletionUserMessageContent.string(
          'What’s the weather like in Boston right now?',
        ),
      ),
    ],
    functions: [function],
  ),
);

final arguments = json.decode(
  res1.choices.first.message.functionCall!.arguments,
) as Map<String, dynamic>;
final functionResult = getCurrentWeather(arguments['location'], arguments['unit']);

final res2 = await client.createChatCompletion(
  request: CreateChatCompletionRequest(
    model: ChatCompletionModel.modelId('gpt-3.5-turbo'),
    messages: [
      ChatCompletionMessage.system(
        content: 'You are a helpful assistant.',
      ),
      ChatCompletionMessage.user(
        content: ChatCompletionUserMessageContent.string(
          'What’s the weather like in Boston right now?',
        ),
      ),
      ChatCompletionMessage.function(
        name: function.name,
        content: json.encode(functionResult),
      ),
    ],
    functions: [function],
  ),
);
final answer = res2.choices.first.message.content;
// The weather in Boston right now is sunny with a temperature of 22°C

Completions #

Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position.

Create completion:

final res = await client.createCompletion(
  request: CreateCompletionRequest(
    model: CompletionModel.modelId('gpt-3.5-turbo-instruct'),
    prompt: CompletionPrompt.string('Say this is a test'),
    maxTokens: 7,
    temperature: 0,
  ),
);
print(res.choices.first.text);
// This is a test.

CompletionModel is a sealed class that offers two ways to specify the model:

CompletionModel.modelId('model-id'): the model ID as string (e.g. 'gpt-3.5-turbo-instruct' or your fine-tuned model ID).
CompletionModel.model(CompletionModels.gpt35TurboInstruct): a value from CompletionModels enum which lists all of the available models.

CompletionPrompt is a sealed class that offers four ways to specify the prompt:

CompletionPrompt.string('prompt'): the prompt as string.
CompletionPrompt.tokens([...]): the tokenized prompt.
CompletionPrompt.listString(['prompt']): batch of string prompts.
CompletionPrompt.listTokens([[...]]): batch of tokenized prompts.

Stream completion:

final stream = client.createCompletionStream(
  request: CreateCompletionRequest(
    model: 'gpt-3.5-turbo-instruct',
    prompt: [
      'Say this is a test',
    ],
    maxTokens: 7,
    temperature: 0,
  ),
);
await for (final res in stream) {
  print(res.choices.first.text);
}

Embeddings #

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

Create embedding:

final res = await client.createEmbedding(
  request: CreateEmbeddingRequest(
    model: EmbeddingModel.modelId('text-embedding-ada-002'),
    input: EmbeddingInput.string('The food was delicious and the waiter...'),
  ),
);
print(res.data.first.embeddingVector);
// [0.002253932, -0.009333183, 0.01574578, -0.007790351, -0.004711035, ...]

EmbeddingModel is a sealed class that offers two ways to specify the model:

EmbeddingModel.modelId('model-id'): the model ID as string.
EmbeddingModel.model(EmbeddingModels.textEmbeddingAda002): a value from EmbeddingModels enum which lists all of the available models.

EmbeddingInput is a sealed class that offers four ways to specify the embedding input:

EmbeddingInput.string('input'): the input as string.
EmbeddingInput.tokens([...]): the tokenized input.
EmbeddingInput.listString(['input']): batch of string inputs.
EmbeddingInput.listTokens([[...]]): batch of tokenized inputs.

You can also request the embedding vector encoded as a base64 string:

final res = await client.createEmbedding(
  request: CreateEmbeddingRequest(
    model: EmbeddingModel.modelId('text-embedding-ada-002'),
    input: EmbeddingInput.string('The food was delicious and the waiter...'),
    encodingFormat: EmbeddingEncodingFormat.base64,
  ),
);
print(res.data.first.embeddingVectorBase64);
// tLYTOzXqGLxL/YA8M0b/uwdfmrsdNXM8iJIfvEOOHL3IJeK7Ok3rv...

Fine-tuning #

Manage fine-tuning jobs to tailor a model to your specific training data.

Create fine-tuning job:

const request = CreateFineTuningJobRequest(
  model: FineTuningModel.modelId('gpt-3.5-turbo'),
  trainingFile: 'file-abc123',
  validationFile: 'file-abc123',
  hyperparameters: FineTuningJobHyperparameters(
    nEpochs: FineTuningNEpochs.mode(FineTuningNEpochsOptions.auto),
  ),
);
final res = await client.createFineTuningJob(request: request);

List fine-tuning jobs:

final res = await client.listPaginatedFineTuningJobs();

Retrieve fine-tuning job:

final res = await client.retrieveFineTuningJob(
  fineTuningJobId: 'ft-AF1WoRqd3aJAHsqc9NY7iL8F',
);

Cancel fine-tuning job:

final res = await client.cancelFineTuningJob(
  fineTuningJobId: 'ft-AF1WoRqd3aJAHsqc9NY7iL8F',
);

List fine-tuning events:

final res = await client.listFineTuningEvents(
  fineTuningJobId: 'ft-AF1WoRqd3aJAHsqc9NY7iL8F',
);

Images #

Given a prompt and/or an input image, the model will generate a new image.

Create image:

final res = await client.createImage(
  request: CreateImageRequest(
    model: CreateImageRequestModel.model(ImageModels.dallE3),
    prompt: 'A cute baby sea otter',
    quality: ImageQuality.hd,
    size: ImageSize.v1024x1792,
    style: ImageStyle.natural,
  ),
);
print(res.data.first.url);
// https://oaidalleapiprodscus.blob.core.windows.net/private/...

Models #

List and describe the various models available in the API. You can refer to the Models documentation to understand what models are available and the differences between them.

List models:

final res = await client.listModels();
print(res.data.first.id);
// text-search-babbage-doc-001

Retrieve model:

final res = await client.retrieveModel(model: 'gpt-4');
print(res.ownedBy);
// openai

Delete fine-tune model:

final res = await client.deleteModel(
  model: 'ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR',
);
print(res.deleted);
// true

Moderations #

Given a input text, outputs if the model classifies it as violating OpenAI's content policy.

final res = await client.createModeration(
  request: CreateModerationRequest(
    model: ModerationModel.modelId('text-moderation-latest'),
    input: ModerationInput.string('I want to kill them.'),
  ),
);
print(res.results.first.categories.violence);
// true
print(res.results.first.categoryScores.violence);
// 0.9925811290740967

ModerationModel is a sealed class that offers two ways to specify the model:

ModerationModel.modelId('model-id'): the model ID as string.
ModerationModel.model(ModerationModels.textModerationLatest): a value from ModerationModels enum which lists all of the available models.

ModerationInput is a sealed class that offers four ways to specify the embedding input:

ModerationInput.string('input'): the input as string.
ModerationInput.listString(['input']): batch of string inputs.

Advance Usage #

Azure OpenAI Service #

OpenAI's models are also available as an Azure service.

Although the Azure OpenAI API is similar to the official OpenAI API, there are subtle differences between them. This client is intended to be used with the official OpenAI API, but most of the functionality should work with the Azure OpenAI API as well.

If you want to use this client with the Azure OpenAI API (at your own risk), you can do so by instantiating the client as follows:

final client = OpenAIClient(
  baseUrl: 'https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME',
  headers: { 'api-key': 'YOUR_API_KEY' },
  queryParams: { 'api-version': 'API_VERSION' },
);

YOUR_RESOURCE_NAME: This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal.
YOUR_DEPLOYMENT_NAME: This value will correspond to the custom name you chose for your deployment when you deployed a model. This value can be found under Resource Management > Deployments in the Azure portal.
YOUR_API_KEY: This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal.
API_VERSION: The Azure OpenAI API version to use (e.g. 2023-05-15). Try to use the latest version available, it will probably be the closest to the official OpenAI API.

Default HTTP client #

By default, the client uses the following implementation of http.Client:

Non-web: IOClient
Web: FetchClient (to support streaming on web)

Custom HTTP client #

You can always provide your own implementation of http.Client for further customization:

final client = OpenAIClient(
  apiKey: 'OPENAI_API_KEY',
  client: MyHttpClient(),
);

Using a proxy #

HTTP proxy

You can use your own HTTP proxy by overriding the baseUrl and providing your required headers:

final client = OpenAIClient(
  baseUrl: 'https://my-proxy.com',
  headers: {
      'x-my-proxy-header': 'value',
  },
);

If you need further customization, you can always provide your own http.Client.

SOCKS5 proxy

To use a SOCKS5 proxy, you can use the socks5_proxy package:

final baseHttpClient = HttpClient();
SocksTCPClient.assignToHttpClient(baseHttpClient, [
  ProxySettings(InternetAddress.loopbackIPv4, 1080),
]);
final httpClient = IOClient(baseClient);

final client = OpenAIClient(
  client: httpClient,
);

Acknowledgements #

The generation of this client was made possible by the openapi_spec package.

License #

OpenAI Dart Client is licensed under the MIT License.

openai_dart 0.1.1+2
openai_dart: ^0.1.1+2 copied to clipboard

Metadata

OpenAI Dart Client #

Features #

Table of contents #

Usage #

Authentication #

Organization (optional)

Chat #

Completions #

Embeddings #

Fine-tuning #

Images #

Models #

Moderations #

Advance Usage #

Azure OpenAI Service #

Default HTTP client #

Custom HTTP client #

Using a proxy #

HTTP proxy

SOCKS5 proxy

Acknowledgements #

License #

← Metadata

Publisher

Metadata

Topics

Documentation

License

Dependencies

More

openai_dart 0.1.1+2 openai_dart: ^0.1.1+2 copied to clipboard

Metadata

OpenAI Dart Client #

Features #

Table of contents #

Usage #

Authentication #

Organization (optional)

Chat #

Completions #

Embeddings #

Fine-tuning #

Images #

Models #

Moderations #

Advance Usage #

Azure OpenAI Service #

Default HTTP client #

Custom HTTP client #

Using a proxy #

HTTP proxy

SOCKS5 proxy

Acknowledgements #

License #

← Metadata

Publisher

Metadata

Topics

Documentation

License

Dependencies

More

openai_dart 0.1.1+2
openai_dart: ^0.1.1+2 copied to clipboard