googleai_dart 0.1.0+2

Dart Client for the Google AI API (Gemini Pro, Gemini Pro Vision, embeddings, etc.).

Google AI Dart Client

Unofficial Dart client for Google AI for Developers (Gemini API v1).

Note: The official google_generative_ai now has feature parity with this package (except for the Model info endpoints). We plan to deprecate this package in the near future.

Features

• Generated from the official Google AI OpenAPI specification
• Fully type-safe, documented and tested
• All platforms supported
• Custom base URL, headers and query params support (e.g. HTTP proxies)
• Custom HTTP client support (e.g. SOCKS5 proxies or advanced use cases)

Supported endpoints:

• Generate content (with streaming and tuned model support)
• Embed content (with batch support)
• Count tokens
• Models info
• Operations

Table of contents

• Usage
  • Authentication
  • Generate content
    • Text-only input
    • Text-and-image input
    • Multi-turn conversations (chat)
    • Streaming generated content
    • Tuned model
  • Count tokens
  • Embedding
  • Model info
    • List models
    • Get model
• Advance Usage
  • Default HTTP client
  • Custom HTTP client
  • Using a proxy
    • HTTP proxy
    • SOCKS5 proxy
• Acknowledgements
• License

Usage

Refer to the documentation for more information about the API.

Authentication

The Google AI API uses API keys for authentication. Visit Google AI Studio dashboard 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 googleaiApiKey = Platform.environment['GOOGLEAI_API_KEY'];
final client = GoogleAIClient(apiKey: googleaiApiKey);

Generate content

Text-only input

Use the generateContent method to generate a response from the model given an input message.

final res = await client.generateContent(
  modelId: 'gemini-pro',
  request: const GenerateContentRequest(
    contents: [
      Content(
        parts: [
          Part(text: 'Write a story about a magic backpack.'),
        ],
      ),
    ],
    generationConfig: GenerationConfig(
      temperature: 0.8,
    ),
  ),
);
print(res.candidates?.first.content?.parts?.first.text);
// In a quaint little town nestled amidst rolling hills, there lived a...

Text-and-image input

If the input contains both text and image, use the gemini-pro-vision model.

final res = await client.generateContent(
  modelId: 'gemini-pro-vision',
  request: GenerateContentRequest(
    contents: [
      Content(
        parts: [
          const Part(text: 'What is this picture?'),
          Part(
            inlineData: Blob(
              mimeType: 'image/png',
              data: base64.encode(
                await File('./test/assets/1.png').readAsBytes(),
              ),
            ),
          ),
        ],
      ),
    ],
  ),
);
print(res.candidates?.first.content?.parts?.first.text);
// The picture shows some scones with blueberries and flowers...

Multi-turn conversations (chat)

Using Gemini, you can build freeform conversations across multiple turns.

The gemini-pro-vision model (for text-and-image input) is not yet optimized for multi-turn conversations. Make sure to use gemini-pro and text-only input for chat use cases.

final res = await client.generateContent(
  modelId: 'gemini-pro',
  request: const GenerateContentRequest(
    contents: [
      Content(
        role: 'user',
        parts: [
          Part(
            text: 'Write the first line of a story about a magic backpack.',
          ),
        ],
      ),
      Content(
        role: 'model',
        parts: [
          Part(
            text:
                'In the bustling city of Meadow brook, lived a young girl named Sophie. '
                'She was a bright and curious soul with an imaginative mind.',
          ),
        ],
      ),
      Content(
        role: 'user',
        parts: [
          Part(
            text: 'Can you set it in a quiet village in 1600s France?',
          ),
        ],
      ),
    ],
  ),
);
print(res.candidates?.first.content?.parts?.first.text);
// In the heart of a tranquil village nestled amidst the rolling hills of 17th century France...

Streaming generated content

By default, generateContent returns a response after completing the entire generation process. You can achieve faster interactions by not waiting for the entire result, and instead use streamGenerateContent to handle partial results as they become available.

final stream = client.streamGenerateContent(
  modelId: 'gemini-pro',
  request: const GenerateContentRequest(
    contents: [
      Content(
        parts: [
          Part(text: 'Write a story about a magic backpack.'),
        ],
      ),
    ],
    generationConfig: GenerationConfig(
      temperature: 0.8,
    ),
  ),
);

stream.listen((final res) P
  print(res.candidates?.first.content?.parts?.first.text);
  // In a quaint little town nestled amidst rolling hills, there lived a...
)

Tuned model

Use the generateContentTunedModel method to generate content using a tuned model:

final res = await client.generateContentTunedModel(
  tunedModelId: 'my-tuned-model',
  request: GenerateContentRequest(
    //...
   ),
);

Count tokens

When using long prompts, it might be useful to count tokens before sending any content to the model.

final res = await client.countTokens(
  modelId: 'gemini-pro',
  request: const CountTokensRequest(
    contents: [
      Content(
        parts: [
          Part(
            text: 'Write a story about a magic backpack.',
          ),
        ],
      ),
    ],
  ),
);
print(res.totalTokens);
// 8

Embedding

Use the embedding-001 model with either embedContents or batchEmbedContents:

final res = await client.embedContent(
  modelId: 'embedding-001',
  request: const EmbedContentRequest(
    content: Content(
      parts: [
        Part(text: 'Write a story about a magic backpack.'),
      ],
    ),
  ),
);
print(res.embedding?.values);
// [0.008624583, -0.030451821, -0.042496547, ...]

Model info

List models

Use the listModels method to list all the models available through the API, including both the Gemini and PaLM family models.

final res = await client.listModels();
print(res.models);
// [Model(name: models/gemini-pro, displayName: Gemini Pro, description: The best model...

Get model

Use the getModel method to return information about that model such as version, display name, input token limit, etc.

final res = await client.getModel(modelId: 'gemini-pro');
print(res);
// Model(name: models/gemini-pro, displayName: Gemini Pro, description: The best model...

Operations

The following methods are available to manage operations:

• listOperations()
• deleteOperation(operationId: operationId)
• listTunedModelOperations(tunedModelId: tunedModelId)
• getTunedModelOperation(tunedModelId: tunedModelId, operationId: operationId)
• cancelTunedModelOperation(tunedModelId: tunedModelId, operationId: operationId)

Advance Usage

Default HTTP client

By default, the client uses https://api.mistral.ai/v1 as the baseUrl and the following implementations 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 = GoogleAIClient(
  apiKey: 'GOOGLEAI_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 = GoogleAIClient(
  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 = GoogleAIClient(
  client: httpClient,
);

Acknowledgements

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

License

Google AI Dart Client is licensed under the MIT License.