foundation-models-on-device
Official苹果FoundationModels框架用于设备上的LLM——文本生成、使用@Generable进行引导生成、工具调用,以及在iOS 26+中的快照流。
What this skill does
When applied, it prepends a system prompt before your request is sent — no extra calls and no change to how you are billed beyond the added tokens.
---
name: foundation-models-on-device
description: 苹果FoundationModels框架用于设备上的LLM——文本生成、使用@Generable进行引导生成、工具调用,以及在iOS 26+中的快照流。
---
# FoundationModels:设备端 LLM(iOS 26)
使用 FoundationModels 框架将苹果的设备端语言模型集成到应用中的模式。涵盖文本生成、使用 `@Generable` 的结构化输出、自定义工具调用以及快照流式传输——全部在设备端运行,以保护隐私并支持离线使用。
## 何时启用
* 使用 Apple Intelligence 在设备端构建 AI 功能
* 无需依赖云端即可生成或总结文本
* 从自然语言输入中提取结构化数据
* 为特定领域的 AI 操作实现自定义工具调用
* 流式传输结构化响应以实现实时 UI 更新
* 需要保护隐私的 AI(数据不离开设备)
## 核心模式 — 可用性检查
在创建会话之前,始终检查模型可用性:
```swift
struct GenerativeView: View {
private var model = SystemLanguageModel.default
var body: some View {
switch model.availability {
case .available:
ContentView()
case .unavailable(.deviceNotEligible):
Text("Device not eligible for Apple Intelligence")
case .unavailable(.appleIntelligenceNotEnabled):
Text("Please enable Apple Intelligence in Settings")
case .unavailable(.modelNotReady):
Text("Model is downloading or not ready")
case .unavailable(let other):
Text("Model unavailable: \(other)")
}
}
}
```
## 核心模式 — 基础会话
```swift
// Single-turn: create a new session each time
let session = LanguageModelSession()
let response = try await session.respond(to: "What's a good month to visit Paris?")
print(response.content)
// Multi-turn: reuse session for conversation context
let session = LanguageModelSession(instructions: """
You are a cooking assistant.
Provide recipe suggestions based on ingredients.
Keep suggestions brief and practical.
""")
let first = try await session.respond(to: "I have chicken and rice")
let followUp = try await session.respond(to: "What about a vegetarian option?")
```
指令的关键点:
* 定义模型的角色("你是一位导师")
* 指定要做什么("帮助提取日历事件")
* 设置风格偏好("尽可能简短地回答")
* 添加安全措施("对于危险请求,回复'我无法提供帮助'")
## 核心模式 — 使用 @Generable 进行引导式生成
生成结构化的 Swift 类型,而不是原始字符串:
### 1. 定义可生成类型
```swift
@Generable(description: "Basic profile information about a cat")
struct CatProfiUse this skill
Add a "skill" field with the skill’s ID to your chat completion request. It is applied server-side before your prompt is sent — no extra calls.
{
"model": "gpt-4o-mini",
"skill": "imp-05e5e33e-18f7-42e3-937a-9e0cfbd637e6",
"messages": [{ "role": "user", "content": "…" }]
}Install the skill, enable it in your dashboard and (optionally) limit it to specific models. It then applies automatically to every matching request — with no "skill" field to send each time.
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