BaseMind.AI Dart SDK

The BaseMind.AI Dart SDK is a gRPC client library for connecting with the BaseMind.AI platform.

Installation

Add the dependency in your application's pubspec.yaml:

With Flutter:

flutter pub add basemind

With Dart:

dart pub add basemind

Usage

Before using the client you have to initialize it. The init function requires an apiKey that you can create using the BaseMind platform (visit https://basemind.ai):

import 'package:basemind/client.dart';

final client = BaseMindClient('<API_KEY>');

Once the client is initialized, you can use it to interact with the AI model(s) you configured in the BaseMind dashboard.

Prompt Request/Response

You can request an LLM prompt using the requestPrompt method, which expects a dictionary of string key/value pairs - correlating with any template variables defined in the dashboard (if any):

import 'package:basemind/client.dart';

final client = BaseMindClient('<API_KEY>');

Future<String> handlePromptRequest(Map<String, String> templateVariables) async {
	final response = await client.requestPrompt(templateVariables);

	return response.content;
}

Prompt Streaming

You can also stream a prompt response using the requestStream method:

import 'package:basemind/client.dart';

final client = BaseMindClient('<API_KEY>');

handlePromptStream(Map<String, String> templateVariables) {
  final stream = client.requestStream(templateVariables);
  stream.listen((response) {
    print(response.content);
  });
}

And you can of course use the requestStream method with async/await:

import 'package:basemind/client.dart';

final client = BaseMindClient('<API_KEY>');

Future<List<String>> handlePromptStream(Map<String, String> templateVariables) async {
  final stream = client.requestStream(templateVariables);

  var chunks = [];
  await for (var response in stream) {
    chunk.add(response.content);
  }

  return chunks;
}

Similarly to the requestPrompt method, requestStream expects a mapping of strings (if any template variables are defined in the dashboard).

Error Handling

All errors thrown by the client are subclasses of BaseMindException. Errors are thrown in the following cases:

1. The api key is empty (MissingAPIKeyException).
2. A server side or connectivity error occurred (APIGatewayException)
3. A required template variable was not provided in the mapping passed to the request (MissingPromptVariableException).

Options

You can pass an options object to the client:

import 'package:basemind/client.dart';

final options = ClientOptions(
   serverAddress: '127.0.0.1',
   serverPort: 8080,
   debug: true,
   logger: Logger('my-logger'),
   channelOptions:
   ChannelOptions(credentials: ChannelCredentials.insecure()),
);

final client = BaseMindClient('<API_KEY>', null, options);

• logger: an instance of logging.Logger to use for logging debug messages.
• debug: if set to true (default false) the client will log debug messages.
• serverAddress: the host of the BaseMind Gateway server to use.
• serverPort: the server port.
• channelOptions: gRPC channel options to use for connecting to the server.

Passing Prompt Config ID

The BaseMindClient constructor also accepts an optional promptConfigId parameter. This parameter is null by default which means the client will use the prompt configuration defined as default in the dashboard. You can also pass a specific prompt config ID to the client:

import 'package:basemind/client.dart';

final client = BaseMindClient('<API_KEY>', "c5f5d1fd-d25d-4ba2-b103-8c85f48a679d");

Note: you can have multiple client instances with different promptConfigId values set. This allows you to use multiple model configurations within a single application.

Local Development

Repository Structure:

root                        # repository root, holding all tooling configurations
├─── .github                # GitHub CI/CD and other configurations
├─── .idea                  # IDE configurations that are shared
├─── proto/gateway          # Git submodule that includes the protobuf schema
├─── bin                    # CLI commands
└─── lib                    # the Dart SDK code

Setup

1. Clone to repository to your local machine including the submodules.

   git clone --recurse-submodules https://github.com/basemind-ai/sdk-dart.git

2. Install TaskFile and the following prerequisites:

   • Python >= 3.11
   • IntelliJ (optional but recommended)

3. Execute the setup task with:

task setup

This will setup pre-commit and the other required dependencies.

Linting

To lint the project, execute the lint command:

task lint

Updating Dependencies

To update the dependencies, execute the update-dependencies command:

task update

This will update the dependencies in the pubspec file. It will also update the pre-commit hooks.

Generating gRPC Stubs

To generate the gRPC stubs, execute the generate command:

task generate

Contribution

The SDK is open source.

Pull requests are welcome - as long as they address an issue or substantially improve the SDK or the test app.

Note: Tests are mandatory for the SDK - untested code will not be merged.