​
Model Calling

Compiler makes it incredibly simple to integrate various LLM providers into your app with a unified API.

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Supported Providers

Compiler currently supports:
  • OpenAI: GPT-4o, GPT-4o Mini, O1, O1 Mini, O3 Mini
  • Anthropic: Claude 3.5 Sonnet, Claude 3.5 Haiku, Claude 3.5 Opus
  • Google: Gemini Flash, Gemini Flash Lite, Gemini 1.5 Flash, Gemini 1.5 Pro
  • Perplexity: Sonar, Sonar Pro, Sonar Reasoning
  • DeepSeek: DeepSeek Chat, DeepSeek Reasoner

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Setting Up Your Client

The first step is to initialize the CompilerClient with your app ID: 
import CompilerSwiftAI

// Initialize with default configuration (OpenAI GPT-4o)
let client = CompilerClient(
    appID: UUID(uuidString: "YOUR_COMPILER_APP_ID")!
)

// Or with a specific model configuration
let client = CompilerClient(
    appID: UUID(uuidString: "YOUR_COMPILER_APP_ID")!,
    configuration: CompilerClient.Configuration(
        streamingChat: .google(.flash)
    )
)

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Basic Usage

Making a call to an LLM is as simple as: 
import CompilerSwiftAI

let client = CompilerClient(appID: UUID(uuidString: "YOUR_COMPILER_APP_ID")!)

// Make a simple call to OpenAI's GPT-4o
let response = try await client.makeModelCall(
    using: .openAI(.gpt4o),
    messages: [
        Message(role: .user, content: "Explain quantum computing in simple terms")
    ]
)

print(response.text)

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Switching Between Providers

One of the biggest advantages of Compiler is the ability to easily switch between different LLM providers: 
// Using OpenAI
let openAIResponse = try await client.makeModelCall(
    using: .openAI(.gpt4o),
    messages: messages
)

// Using Anthropic
let anthropicResponse = try await client.makeModelCall(
    using: .anthropic(.claudeOpus),
    messages: messages
)

// Using Google
let googleResponse = try await client.makeModelCall(
    using: .google(.flash),
    messages: messages
)

// Using Perplexity
let perplexityResponse = try await client.makeModelCall(
    using: .perplexity(.sonarPro),
    messages: messages
)

// Using DeepSeek
let deepseekResponse = try await client.makeModelCall(
    using: .deepseek(.reasoner),
    messages: messages
)

​
Streaming Responses

For a more interactive experience, you can stream responses: 
try await client.makeStreamingModelCall(
    using: .openAI(.gpt4o),
    messages: [
        Message(role: .user, content: "Write a short story about a robot learning to paint")
    ],
    onUpdate: { partialResponse in
        // Update your UI with each chunk of the response
        print(partialResponse.text, terminator: "")
    },
    onComplete: { finalResponse in
        // Handle the complete response
        print("\nFinal response received!")
    }
)

​
Advanced Options

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Setting Temperature

Control the creativity of the model: 
// When making a direct call
let response = try await client.makeModelCall(
    using: .openAI(.gpt4o, temperature: 0.7),  // Higher for more creative, lower for more deterministic
    messages: messages
)

// Or when configuring the client
let client = CompilerClient(
    appID: UUID(uuidString: "YOUR_COMPILER_APP_ID")!,
    configuration: CompilerClient.Configuration(
        streamingChat: .anthropic(.claudeSonnet, temperature: 0.7)
    )
)

​
Maximum Tokens

Limit the length of the response: 
// When making a direct call
let response = try await client.makeModelCall(
    using: .anthropic(.claudeSonnet, maxTokens: 500),  // Limit response to 500 tokens
    messages: messages
)

// Or when configuring the client
let client = CompilerClient(
    appID: UUID(uuidString: "YOUR_COMPILER_APP_ID")!,
    configuration: CompilerClient.Configuration(
        streamingChat: .google(.flash, maxTokens: 500)
    )
)

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System Messages

Set the behavior of the model with system messages: 
let response = try await client.makeModelCall(
    using: .openAI(.gpt4o),
    messages: [
        Message(role: .system, content: "You are a helpful assistant that specializes in explaining complex topics in simple terms."),
        Message(role: .user, content: "Explain how blockchain works")
    ]
)

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Updating Client Configuration

You can update the client’s configuration at any time: 
// Update the streaming chat configuration
await client.updateStreamingChat { config in
    // Switch to a different model
    config = .anthropic(.claudeHaiku)
}

// Create an immutable streaming session with current configuration
let streamingSession = await client.makeStreamingSession()

​
Integration with UI Components

Compiler provides ready-to-use UI components that work seamlessly with the model calling API. Here’s how to set up a complete chat interface: 
@MainActor
@Observable
class CompilerManager {
    var isAuthenticated = false
    var errorMessage: String?
    var isCheckingAuth = true
    
    let client: CompilerClient
    
    init() {
        // Initialize with Google Gemini Flash
        client = CompilerClient(
            appID: UUID(uuidString: "YOUR_COMPILER_APP_ID")!,
            configuration: CompilerClient.Configuration(
                streamingChat: .google(.flash)
            )
        )
        
        // Check auth state on init
        Task { @MainActor in
            do {
                isAuthenticated = try await client.attemptAutoLogin()
            } catch {
                errorMessage = error.localizedDescription
            }
            isCheckingAuth = false
        }
    }
}

struct ContentView: View {
    @State private var compiler = CompilerManager()
    @State private var currentNonce: String?
    
    var body: some View {
        VStack(spacing: 20) {
            if compiler.isCheckingAuth {
                ProgressView("Checking authentication...")
                    .progressViewStyle(.circular)
            } else if compiler.isAuthenticated {
                VStack(spacing: 20) {
                    Text("Chat")
                        .font(.title)
                        .fontWeight(.bold)
                    
                    // The ChatView automatically uses the model configured in the client
                    ChatView(client: compiler.client, inputType: .combined)
                        .userBubbleStyle(backgroundColor: .blue, textColor: .white)
                        .assistantBubbleStyle(backgroundColor: .clear, textColor: .black)
                        .inputFieldStyle(
                            backgroundColor: .gray.opacity(0.1),
                            textColor: .primary,
                            placeholder: "Message"
                        )
                        .inputButtonStyle(
                            size: 32,
                            sendTint: .blue
                        )
                        .frame(maxWidth: .infinity, maxHeight: .infinity)
                }
            } else {
                SignInWithAppleButton(
                    onRequest: { request in
                        let nonce = CompilerClient.randomNonceString()
                        currentNonce = nonce
                        
                        request.nonce = CompilerClient.sha256(nonce)
                        request.requestedScopes = [.fullName, .email]
                    },
                    onCompletion: { result in
                        Task {
                            do {
                                compiler.isAuthenticated = try await compiler.client.handleSignInWithApple(result, nonce: currentNonce)
                            } catch {
                                compiler.errorMessage = error.localizedDescription
                            }
                        }
                    }
                )
                .frame(width: 280, height: 45)
                .signInWithAppleButtonStyle(.black)
            }
            
            if let errorMessage = compiler.errorMessage {
                Text(errorMessage)
                    .foregroundColor(.red)
                    .padding()
            }
        }
        .padding()
        .frame(minWidth: 400, minHeight: 600)
        .frame(maxWidth: 800, maxHeight: .infinity)
    }
}

​
Error Handling

Proper error handling for model calls: 
do {
    let response = try await client.makeModelCall(
        using: .openAI(.gpt4o),
        messages: messages
    )
    print(response.text)
} catch CompilerError.rateLimitExceeded {
    print("Rate limit exceeded. Please try again later.")
} catch CompilerError.modelUnavailable {
    print("The requested model is currently unavailable.")
} catch CompilerError.invalidAPIKey {
    print("There's an issue with your API key configuration.")
} catch {
    print("An unexpected error occurred: \(error.localizedDescription)")
}

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Available Models

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OpenAI Models

public enum OpenAIModel: String, Codable {
    case gpt4o = "chatgpt-4o-latest"
    case gpt4oMini = "gpt-4o-mini"
    case o1 = "o1"
    case o1Mini = "o1-mini"
    case o3Mini = "o3-mini"
}

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Anthropic Models

public enum AnthropicModel: String, Codable {
    case claudeSonnet = "claude-3-5-sonnet-latest"
    case claudeHaiku = "claude-3-5-haiku-latest"
    case claudeOpus = "claude-3-5-opus-latest"
}

​
Google Models

public enum GeminiModel: String, Codable {
    case flash = "gemini-2.0-flash"
    case flashLitePreview = "gemini-2.0-flash-lite-preview-02-05"
    case flash15 = "gemini-1.5-flash"
    case flash15_8b = "gemini-1.5-flash-8b"
    case pro15 = "gemini-1.5-pro"
    case textEmbedding = "text-embedding-004"
}

​
Perplexity Models

public enum PerplexityModel: String, Codable {
    case sonarReasoning = "sonar-reasoning"
    case sonarPro = "sonar-pro"
    case sonar = "sonar"
}

​
DeepSeek Models

public enum DeepSeekModel: String, Codable {
    case chat = "deepseek-chat"
    case reasoner = "deepseek-reasoner"
}

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Next Steps

After implementing model calling, you might want to: