Peekaboo MCP

import Commander import Foundation import SharedExampleUtils import Tachikoma /// Simple getting started example demonstrating basic Tachikoma usage @main struct TachikomaBasics: AsyncParsableCommand { static let commandDescription = CommandDescription( commandName: "tachikoma-basics", abstract: "🎓 Learn the basics of using Tachikoma for AI interactions", discussion: """ This example demonstrates the fundamental concepts of using Tachikoma: - Setting up AI model providers - Making basic requests - Handling responses and errors - Provider-agnostic code patterns Examples: tachikoma-basics "Hello, AI!" tachikoma-basics --provider openai "Write a haiku" tachikoma-basics --list-providers """) @Argument(help: "The message to send to the AI") var message: String? @Option(name: .shortAndLong, help: "Specific provider to use (openai, anthropic, ollama, grok)") var provider: String? @Flag(name: .long, help: "List all available providers and exit") var listProviders: Bool = false @Flag(name: .shortAndLong, help: "Show detailed information about the process") var verbose: Bool = false func run() async throws { TerminalOutput.header("🎓 Tachikoma Basics") if self.listProviders { try self.listAvailableProviders() return } guard let message else { TerminalOutput.print("❌ Please provide a message or use --list-providers", color: .red) return } try await self.demonstrateBasicUsage(message: message) } /// List available providers and their status private func listAvailableProviders() throws { TerminalOutput.print("🔍 Scanning for available AI providers...\n", color: .cyan) // Show environment-based detection let detectedProviders = ProviderDetector.detectAvailableProviders() TerminalOutput.print("Detected providers: \(detectedProviders.joined(separator: ", "))", color: .green) // Try to create the model provider do { let modelProvider = try AIConfiguration.fromEnvironment() let availableModels = modelProvider.availableModels() TerminalOutput.print("\n📋 Available models (\(availableModels.count) total):", color: .bold) let groupedModels = self.groupModelsByProvider(availableModels) for (provider, models) in groupedModels.sorted(by: { $0.key < $1.key }) { TerminalOutput.providerHeader(provider) for model in models.sorted() { TerminalOutput.print(" • \(model)", color: .white) } print("") } } catch { TerminalOutput.print("❌ Failed to initialize providers: \(error)", color: .red) ConfigurationHelper.printSetupInstructions() } } /// Group models by their provider private func groupModelsByProvider(_ models: [String]) -> [String: [String]] { var grouped: [String: [String]] = [:] for model in models { let provider = self.detectProviderFromModel(model) if grouped[provider] == nil { grouped[provider] = [] } grouped[provider]?.append(model) } return grouped } /// Detect provider name from model string private func detectProviderFromModel(_ model: String) -> String { let lowercased = model.lowercased() if lowercased.contains("gpt") || lowercased.contains("o3") || lowercased.contains("o4") { return "OpenAI" } else if lowercased.contains("claude") { return "Anthropic" } else if lowercased.contains("llama") || lowercased.contains("llava") { return "Ollama" } else if lowercased.contains("grok") { return "Grok" } else { return "Unknown" } } /// Demonstrate basic Tachikoma usage patterns private func demonstrateBasicUsage(message: String) async throws { if self.verbose { TerminalOutput.print("🔧 Setting up Tachikoma...", color: .yellow) } // Step 1: Create the model provider // AIConfiguration.fromEnvironment() automatically detects API keys and sets up providers let modelProvider: AIModelProvider do { modelProvider = try AIConfiguration.fromEnvironment() if self.verbose { TerminalOutput.print("✅ Successfully initialized AIModelProvider", color: .green) } } catch { TerminalOutput.print("❌ Failed to set up providers: \(error)", color: .red) TerminalOutput.print("\n💡 Make sure you have API keys configured:", color: .yellow) ConfigurationHelper.printSetupInstructions() return } // Step 2: Select which model to use // This demonstrates Tachikoma's provider-agnostic approach let selectedModel = try selectModel(from: modelProvider) if self.verbose { TerminalOutput.print("🎯 Selected model: \(selectedModel)", color: .cyan) } // Step 3: Get the model instance // Same interface works for OpenAI, Anthropic, Ollama, or Grok let model = try modelProvider.getModel(selectedModel) if self.verbose { TerminalOutput.print("📡 Creating request...", color: .yellow) } // Step 4: Create a request // ModelRequest provides a unified interface across all providers let request = ModelRequest( messages: [Message.user(content: .text(message))], // Simple text message tools: nil, // No function calling for this basic example settings: ModelSettings(maxTokens: 300), // Limit response length ) if self.verbose { TerminalOutput.print( "🚀 Sending request to \(self.detectProviderFromModel(selectedModel))...", color: .yellow) } // Step 5: Send the request and measure performance let startTime = Date() do { // The same getResponse() call works with any provider let response = try await model.getResponse(request: request) let endTime = Date() let duration = endTime.timeIntervalSince(startTime) // Display the results self.displayResponse( message: message, response: response, model: selectedModel, duration: duration) } catch { TerminalOutput.print("❌ Request failed: \(error)", color: .red) if self.verbose { TerminalOutput.print("\n🔍 Debugging information:", color: .yellow) TerminalOutput.print("Model: \(selectedModel)", color: .dim) TerminalOutput.print("Error type: \(type(of: error))", color: .dim) } } } /// Select a model based on user preference or auto-detection private func selectModel(from modelProvider: AIModelProvider) throws -> String { let availableModels = modelProvider.availableModels() if availableModels.isEmpty { throw NSError(domain: "TachikomaBasics", code: 1, userInfo: [ NSLocalizedDescriptionKey: "No models available. Please configure API keys.", ]) } // If user specified a provider, find the best model for it if let requestedProvider = provider { let recommended = ProviderDetector.recommendedModels() // Try to use the recommended model for this provider if let recommendedModel = recommended[requestedProvider.capitalized], availableModels.contains(recommendedModel) { return recommendedModel } else { // Find any model from the requested provider let providerModels = availableModels.filter { model in self.detectProviderFromModel(model).lowercased() == requestedProvider.lowercased() } if let firstModel = providerModels.first { return firstModel } else { TerminalOutput.print( "⚠️ Provider '\(requestedProvider)' not available. Using default.", color: .yellow) } } } // Auto-select the best available model // Prioritized by quality and general capabilities let preferredOrder = ["claude-opus-4-20250514", "gpt-4.1", "llama3.3", "grok-4"] for preferred in preferredOrder { if availableModels.contains(preferred) { return preferred } } // Fallback to first available return availableModels.first! } /// Display the response in a formatted way private func displayResponse(message: String, response: ModelResponse, model: String, duration: TimeInterval) { let provider = self.detectProviderFromModel(model) let emoji = TerminalOutput.providerEmoji(provider) TerminalOutput.separator("═") TerminalOutput.print("💬 Your message: \(message)", color: .cyan) TerminalOutput.separator("─") TerminalOutput.print("\(emoji) \(provider) response:", color: .bold) TerminalOutput.separator("─") // Extract text content from response // ModelResponse.content is an array of AssistantContent items let textContent = response.content.compactMap { item in if case let .outputText(text) = item { return text } return nil }.joined() if !textContent.isEmpty { TerminalOutput.print(textContent, color: .white) } else { TerminalOutput.print("(No text content in response)", color: .dim) } TerminalOutput.separator("─") // Show statistics let tokenCount = PerformanceMeasurement.estimateTokenCount(textContent) let stats = [ "⏱️ Duration: \(String(format: "%.2fs", duration))", "🔤 Tokens: ~\(tokenCount)", "👻 Model: \(model)", ] TerminalOutput.print(stats.joined(separator: " | "), color: .dim) // Cost estimation if available if let cost = PerformanceMeasurement.estimateCost( provider: model, inputTokens: PerformanceMeasurement.estimateTokenCount(message), outputTokens: tokenCount) { TerminalOutput.print("💰 Estimated cost: $\(String(format: "%.4f", cost))", color: .green) } else { TerminalOutput.print("💰 Cost: Free (local model)", color: .green) } TerminalOutput.separator("═") if self.verbose { TerminalOutput.print("\n🎓 Key concepts demonstrated:", color: .yellow) TerminalOutput.print( "1. ✅ Environment-based configuration (AIConfiguration.fromEnvironment())", color: .dim) TerminalOutput.print("2. ✅ Provider-agnostic model access (modelProvider.getModel())", color: .dim) TerminalOutput.print("3. ✅ Unified request/response format across all providers", color: .dim) TerminalOutput.print("4. ✅ Error handling and graceful degradation", color: .dim) TerminalOutput.print("\n💡 Next steps:", color: .cyan) TerminalOutput.print("• Try: tachikoma-comparison \"Your question\" (side-by-side comparison)", color: .dim) TerminalOutput.print("• Try: tachikoma-streaming \"Tell me a story\" (real-time responses)", color: .dim) TerminalOutput.print("• Try: tachikoma-agent --help (AI agents with tool calling)", color: .dim) } } }