OpenFoundationModels

// Apple Foundation Models (Apple ecosystem only)
import FoundationModels

// OpenFoundationModels (works everywhere)
import OpenFoundationModels

// 🎯 100% API Compatible - No code changes required
let session = LanguageModelSession(
    model: SystemLanguageModel.default,
    tools: []
) {
    Instructions("You are a helpful assistant")
}

import OpenFoundationModels

// Apple's official API - works everywhere
let session = LanguageModelSession(
    model: SystemLanguageModel.default,
    tools: []
) {
    Instructions("You are a helpful assistant")
}

let response = try await session.respond {
    Prompt("Hello, OpenFoundationModels!")
}
print(response.content)

import OpenFoundationModels
import OpenFoundationModelsOpenAI

let provider = OpenAIProvider(apiKey: "your_key")
let session = LanguageModelSession(model: provider.gpt4o)
let response = try await session.respond {
    Prompt("Explain Swift concurrency")
}

// Package.swift
dependencies: [
    // Core framework
    .package(url: "https://github.com/1amageek/OpenFoundationModels.git", from: "1.0.0"),
    
    // Optional: OpenAI provider
    .package(url: "https://github.com/1amageek/OpenFoundationModels-OpenAI.git", from: "1.0.0")
]

import OpenFoundationModels

// Check model availability
let model = SystemLanguageModel.default
guard model.isAvailable else {
    print("Model not available")
    return
}

// Create session with instructions (Apple Official API)
// Instructions and tools are stored in Transcript as the first entry
let session = LanguageModelSession(
    model: model,
    tools: []
) {
    Instructions("You are a helpful assistant")
}

// Apple Official closure-based prompt
// Each prompt is added to the Transcript, maintaining full conversation history
let response = try await session.respond {
    Prompt("Tell me about Swift 6.1 new features")
}

print(response.content)

// Access the complete conversation history via Transcript
print("Total conversation entries: \(session.transcript.count)")

// Define your data structure with validation rules
@Generable
struct ProductReview {
    @Guide(description: "Product name", .pattern("^[A-Za-z0-9\\s]+$"))
    let productName: String
    
    @Guide(description: "Rating from 1 to 5", .range(1...5))
    let rating: Int
    
    @Guide(description: "Review between 50-500 chars", .count(50...500))
    let comment: String
    
    @Guide(description: "Recommendation level", .anyOf(["Highly Recommend", "Recommend", "Neutral", "Not Recommend"]))
    let recommendation: String
}

// LLM generates validated, type-safe data
let response = try await session.respond(
    generating: ProductReview.self
) {
    Prompt("Generate a review for iPhone 15 Pro")
}

// Direct property access - no JSON parsing needed!
print("Product: \(response.content.productName)")
print("Rating: \(response.content.rating)/5")
print("Comment: \(response.content.comment)")

// Apple Official streaming API
let stream = session.streamResponse {
    Prompt("Explain the history of Swift programming language in detail")
}

for try await partial in stream {
    print(partial.content, terminator: "")
    
    if partial.isComplete {
        print("\n--- Generation Complete ---")
        break
    }
}

@Generable
struct BlogPost {
    let title: String
    let content: String
    let tags: [String]
}

let stream = session.streamResponse(
    generating: BlogPost.self
) {
    Prompt("Write a blog post about Swift Concurrency")
}

for try await partial in stream {
    if let post = partial.content as? BlogPost {
        print("Title: \(post.title)")
        print("Progress: \(post.content.count) characters")
    }
    
    if partial.isComplete {
        print("Article generation complete!")
    }
}

// Define a tool that LLMs can call
struct WeatherTool: Tool {
    static let name = "get_weather"
    static let description = "Get current weather for a city"
    
    // Type-safe arguments
    @Generable
    struct Arguments {
        @Guide(description: "City name")
        let city: String
    }
    
    func call(arguments: Arguments) async throws -> ToolOutput {
        // Your actual API call here
        let weather = try await weatherAPI.fetch(city: arguments.city)
        return ToolOutput("Weather in \(arguments.city): \(weather)°C")
    }
}

// LLM decides when to call tools
let session = LanguageModelSession(
    model: SystemLanguageModel.default,
    tools: [WeatherTool()]
) {
    Instructions("You are a helpful assistant that can check weather")
}

let response = try await session.respond {
    Prompt("What's the weather in Tokyo and Paris?")
}
// LLM calls WeatherTool twice and combines results
// Output: "Tokyo is 22°C and sunny, while Paris is 15°C with clouds."

// Fine-tune generation behavior
let options = GenerationOptions(
    sampling: .greedy,                    // Deterministic output
    // sampling: .random(top: 50, seed: 42), // Top-K sampling
    // sampling: .random(probabilityThreshold: 0.9, seed: nil), // Top-P sampling
    temperature: 0.7,                     // Creativity level (0.0-1.0)
    maximumResponseTokens: 500            // Length limit
)

// Apply custom instructions
let session = LanguageModelSession(
    model: SystemLanguageModel.default,
    tools: []
) {
    Instructions("""
        You are a Swift expert.
        Use modern Swift 6.1+ features.
        Include error handling in all examples.
        """)
}

let response = try await session.respond(options: options) {
    Prompt("Write a networking function")
}

// Response Format for structured output
// ResponseFormat is automatically set when using respond(generating:)
@Generable
struct CodeReview {
    let summary: String
    let issues: [Issue]
    let rating: Int
}

// When using respond(generating:), ResponseFormat is automatically included
let review = try await session.respond(generating: CodeReview.self) {
    Prompt("Review this Swift code: \(code)")
}
// Internally sets: responseFormat: Transcript.ResponseFormat(type: CodeReview.self)

// Or explicitly with schema
let schema = GenerationSchema(
    type: CodeReview.self,
    description: "Code review structure",
    properties: [...]
)

let review = try await session.respond(to: prompt, schema: schema)
// Internally sets: responseFormat: Transcript.ResponseFormat(schema: schema)

import OpenFoundationModels
import OpenFoundationModelsOpenAI  // Or Anthropic, Ollama, etc.

// Same API, different providers - all using Transcript-based interface
let session = LanguageModelSession(
    model: OpenAIProvider(apiKey: key).gpt4o        // OpenAI
    // model: AnthropicProvider(apiKey: key).claude3  // Anthropic
    // model: OllamaProvider().llama3                 // Local
    // model: SystemLanguageModel.default             // Apple
)

// Write once, run with any provider
// Each provider receives the full Transcript and interprets it appropriately
let response = try await session.respond {
    Prompt("Explain quantum computing")
}

// All advanced features work with all providers
@Generable
struct Analysis {
    let summary: String
    let keyPoints: [String]
    let confidence: Double
}

let analysis = try await session.respond(
    generating: Analysis.self
) {
    Prompt("Analyze this code: \(codeSnippet)")
}

// Providers implement the simple LanguageModel protocol
// They receive Transcript and return responses - implementation details are provider-specific

// Build chatbots that work on any platform
let chatbot = LanguageModelSession(
    model: provider.model,
    tools: []
) {
    Instructions("You are a helpful chatbot assistant")
}
let response = try await chatbot.respond { 
    Prompt(userMessage) 
}

// Extract structured data from unstructured text
@Generable
struct Invoice {
    let invoiceNumber: String
    let totalAmount: Double
    let items: [LineItem]
}

let invoice = try await session.respond(generating: Invoice.self) {
    Prompt("Extract invoice data from: \(pdfText)")
}

// Analyze and categorize content
@Generable
struct ContentAnalysis {
    @Guide(description: "Sentiment", .anyOf(["positive", "neutral", "negative"]))
    let sentiment: String
    let topics: [String]
    let summary: String
}

// Generate code with validation
@Generable
struct SwiftFunction {
    @Guide(description: "Valid Swift function signature")
    let signature: String
    let implementation: String
    let tests: [String]
}

// Transcript manages the complete conversation
public struct Transcript {
    public enum Entry {
        case instructions(Instructions)  // System instructions & tool definitions
        case prompt(Prompt)              // User input with optional ResponseFormat
        case response(Response)          // Model output
        case toolCalls(ToolCalls)        // Tool invocations
        case toolOutput(ToolOutput)      // Tool results
    }
    
    // Prompt includes ResponseFormat for structured output
    public struct Prompt {
        var segments: [Segment]
        var options: GenerationOptions
        var responseFormat: ResponseFormat?  // Optional structured output format
    }
    
    // ResponseFormat defines expected output structure
    public struct ResponseFormat {
        public init(schema: GenerationSchema)      // From explicit schema
        public init<Content: Generable>(type: Content.Type)  // From Generable type
        public var name: String { get }
    }
}

// LanguageModelSession manages Transcript
let session = LanguageModelSession(model: model, tools: tools) {
    Instructions("You are a helpful assistant")
    // Automatically added as first Transcript.Entry
}

// Each interaction updates the Transcript
let response = try await session.respond {
    Prompt("Hello")  // Added to Transcript before sending to model
}
// Response is added to Transcript after generation

// Structured output with ResponseFormat
let response = try await session.respond(generating: ProductReview.self) {
    Prompt("Review the iPhone 15")
    // ResponseFormat(type: ProductReview.self) automatically added to Prompt
}

// LanguageModel receives complete context
protocol LanguageModel {
    // Receives full Transcript with all history, instructions, tools, and ResponseFormat
    func generate(transcript: Transcript, options: GenerationOptions?) async throws -> String
}