CrossIntelligence
Provides unified access to Apple Intelligence and external LLMS for MAUI and .NET apps
Stars: 67
CrossIntelligence is a powerful tool for data analysis and visualization. It allows users to easily connect and analyze data from multiple sources, providing valuable insights and trends. With a user-friendly interface and customizable features, CrossIntelligence is suitable for both beginners and advanced users in various industries such as marketing, finance, and research.
README:
A library to provide access to Apple Intelligence and other LLMs for .NET and MAUI applications.
You can install the CrossIntelligence library via NuGet:
dotnet add package CrossIntelligenceTo use the CrossIntelligence library, you need to create an instance of the IntelligenceSession class and call its methods.
using CrossIntelligence;
var session = new IntelligenceSession();
var response = await session.RespondAsync("What is the meaning of life?");
Console.WriteLine(response);- [x] Access to Apple Intelligence System Language Model
- [x] Access to OpenAI Models
- [x] Prompt text input
- [x] Text output
- [x] System instructions
- [x] Chat functionality (session history)
- [x] Tool/function support
- [x] Structured output
You can define a structured output by creating a class with properties that match the expected output format. The library will automatically deserialize the response into your class.
class NonPlayerCharacter
{
public required string Name { get; set; }
public required int Age { get; set; }
public required string Occupation { get; set; }
}
var session = new IntelligenceSession();
var response = await session.RespondAsync<NonPlayerCharacter>("Generate a random NPC with a name, age, and occupation.");
Console.WriteLine($"Name: {response.Name}, Age: {response.Age}, Occupation: {response.Occupation}");You can define tools (functions) that the LLM can call to perform specific tasks. Define a class the inherits from IntelligenceTool and implement the ExecuteAsync method. Tools take arguments that are specified using a generic type parameter.
class AddPlayerTool : IntelligenceTool<NonPlayerCharacter>
{
private GameDatabase gameDatabase;
public AddPlayerTool(GameDatabase gameDatabase)
{
this.gameDatabase = gameDatabase;
}
public override string Name => "AddPlayer";
public override string Description => "Adds a new non-player character (NPC) to the game.";
public override async Task<string> ExecuteAsync(NonPlayerCharacter npc)
{
await gameDatabase.AddPlayerAsync(npc);
return $"Added NPC: {npc.Name}.";
}
}
var gameDatabase = new GameDatabase();
var session = new IntelligenceSession(tools: [new AddPlayerTool(gameDatabase)]);
var response = await session.RespondAsync("Add 3 new NPCs to the game.");
Console.WriteLine(response);You can use other models than the default system model by passing in the model parameter when creating the IntelligenceSession.
var session = new IntelligenceSession(model: IntelligenceModel.OpenAI("gpt-4.1", apiKey: "OPENAI_API_KEY"));
var response = await session.RespondAsync("What is the meaning of life?");
Console.WriteLine(response);If you'd like to contribute to the CrossIntelligence library, please fork the repository and submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
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