Ollama Dart Client

Unofficial Dart client for Ollama API.

Features

• Fully type-safe, documented and tested
• All platforms supported (including streaming on web)
• 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:

• Completions (with streaming support)
• Chat completions (with streaming and tool calling support)
• Embeddings
• Models
• Blobs
• Version

Table of contents

• Usage
  • Completions
    • Generate completion
    • Stream completion
  • Chat completions
    • Generate chat completion
    • Stream chat completion
    • Tool calling
  • Embeddings
    • Generate embedding
  • Models
    • Create model
    • List models
    • List running models
    • Show Model Information
    • Pull a Model
    • Push a Model
    • Check if a Blob Exists
  • Version
• 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.

Completions

Given a prompt, the model will generate a response.

Generate completion

final generated = await client.generateCompletion(
  request: GenerateCompletionRequest(
    model: 'mistral:latest',
    prompt: 'Why is the sky blue?',
  ),
);
print(generated.response);
// The sky appears blue because of a phenomenon called Rayleigh scattering...

Stream completion

final stream = client.generateCompletionStream(
  request: GenerateCompletionRequest(
    model: 'mistral:latest',
    prompt: 'Why is the sky blue?',
  ),
);
String text = '';
await for (final res in stream) {
  text += res.response?.trim() ?? '';
}
print(text);
// The sky appears blue because of a phenomenon called Rayleigh scattering...

Chat completions

Given a prompt, the model will generate a response in a chat format.

Generate chat completion

final res = await client.generateChatCompletion(
  request: GenerateChatCompletionRequest(
    model: defaultModel,
    messages: [
      Message(
        role: MessageRole.system,
        content: 'You are a helpful assistant.',
      ),
      Message(
        role: MessageRole.user,
        content: 'List the numbers from 1 to 9 in order.',
      ),
    ],
    keepAlive: 1,
  ),
);
print(res);
// Message(role: MessageRole.assistant, content: 123456789)

Stream chat completion

final stream = client.generateChatCompletionStream(
  request: GenerateChatCompletionRequest(
    model: defaultModel,
    messages: [
      Message(
        role: MessageRole.system,
        content: 'You are a helpful assistant.',
      ),
      Message(
        role: MessageRole.user,
        content: 'List the numbers from 1 to 9 in order.',
      ),
    ],
    keepAlive: 1,
  ),
);

String text = '';
await for (final res in stream) {
  text += (res.message?.content ?? '').trim();
}
print(text);
// 123456789

Tool calling

Tool calling allows a model to respond to a given prompt by generating output that matches a user-defined schema, that you can then use to call the tools in your code and return the result back to the model to complete the conversation.

Notes:

• Tool calling requires Ollama 0.2.8 or newer.
• Streaming tool calls is not supported at the moment.
• Not all models support tool calls. Check the Ollama catalogue for models that have the Tools tag (e.g. llama3.2).

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

const userMsg = Message(
  role: MessageRole.user,
  content: 'What’s the weather like in Barcelona in celsius?',
);

final res1 = await client.generateChatCompletion(
  request: GenerateChatCompletionRequest(
    model: 'llama3.2',
    messages: [userMsg],
    tools: [tool],
  ),
);

print(res1.message.toolCalls);
// [
//   ToolCall(
//     function:
//       ToolCallFunction(
//         name: get_current_weather,
//         arguments: {
//           location: Barcelona, ES,
//           unit: celsius
//         }
//       )
//   )
// ]

// Call your tool here. For this example, we'll just mock the response.
const toolResult = '{"location": "Barcelona, ES", "temperature": 20, "unit": "celsius"}';

// Submit the response of the tool call to the model
final res2 = await client.generateChatCompletion(
  request: GenerateChatCompletionRequest(
    model: 'llama3.2',
    messages: [
      userMsg,
      res1.message,
      Message(
        role: MessageRole.tool,
        content: toolResult,
      ),
    ],
  ),
);
print(res2.message.content);
// The current weather in Barcelona is 20°C.

Embeddings

Given a prompt, the model will generate an embedding representing the prompt.

Generate embedding

final generated = await client.generateEmbedding(
  request: GenerateEmbeddingRequest(
    model: 'mistral:latest',
    prompt: 'Here is an article about llamas...',
  ),
);
print(generated.embedding);
// [8.566641807556152, 5.315540313720703, ...]

Models

Create model

Creates a new local model using a modelfile.

await client.createModel(
  request: CreateModelRequest(
    model: 'mario',
    modelfile: 'FROM mistral:latest\nSYSTEM You are mario from Super Mario Bros.',
  ),
);

You can also stream the status of the model creation:

final stream = client.createModelStream(
  request: CreateModelRequest(
    model: 'mario',
    modelfile: 'FROM mistral:latest\nSYSTEM You are mario from Super Mario Bros.',
  ),
);
await for (final res in stream) {
  print(res.status);
}

List models

List models that are available locally.

final res = await client.listModels();
print(res.models);

List running models

Lists models currently loaded and their memory footprint.

final res = await client.listRunningModels();
print(res.models);

Show Model Information

Show details about a model including modelfile, template, parameters, license, and system prompt.

final res = await client.showModelInfo(
  request: ModelInfoRequest(model: 'mistral:latest'),
);
print(res);

Pull a Model

Download a model from the ollama library. Cancelled pulls are resumed from where they left off, and multiple calls will share the same download progress.

final res = await client.pullModel(
  request: PullModelRequest(model: 'yarn-llama3:13b-128k-q4_1'),
);
print(res.status);

You can also stream the pulling status:

final stream = client.pullModelStream(
  request: PullModelRequest(model: 'yarn-llama3:13b-128k-q4_1'),
);
await for (final res in stream) {
  print(res.status);
}

Push a Model

Upload a model to a model library.

Requires registering for ollama.ai and adding a public key first.

final res = await client.pushModel(
  request: PushModelRequest(model: 'mattw/pygmalion:latest'),
);
print(res.status);

You can also stream the pushing status:

final stream = client.pushModelStream(
  request: PushModelRequest(model: 'mattw/pygmalion:latest'),
);
await for (final res in stream) {
  print(res.status);
}

Check if a Blob Exists

Ensures that the file blob used for a FROM or ADAPTER field exists on the server. This is checking your Ollama server and not Ollama.ai.

await client.checkBlob(
  digest: 'sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2',
);

If the blob doesn't exist, an OllamaClientException exception will be thrown.

Version

Get the version of the Ollama server.

final res = await client.getVersion();
print(res.version);

Advance Usage

Default HTTP client

By default, the client uses http://localhost:11434/api 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 = OllamaClient(
  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 = OllamaClient(
  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 = OllamaClient(
  client: httpClient,
);

Acknowledgements

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

License

Ollama Dart Client is licensed under the MIT License.