苹果的 AI 不是另一个 ChatGPT——Apple Intelligence 的差异化路径与开发者实践

import AppIntents

// 1. 定义 App Entity——让系统知道"笔记"是什么
struct NoteEntity: AppEntity {
    static var typeDisplayRepresentation = TypeDisplayRepresentation(name: "笔记")
    static var defaultQuery = NoteQuery()

    var id: UUID
    var title: String
    var content: String

    var displayRepresentation: DisplayRepresentation {
        DisplayRepresentation(title: "\(title)")
    }
}

// 2. 定义 App Intent——让 Siri 能执行"创建笔记"动作
struct CreateNoteIntent: AppIntent {
    static var title: LocalizedStringResource = "在 QuickNote 中创建笔记"
    static var description = IntentDescription("用指定标题和内容创建一条新笔记")
    static var openAppWhenRun: Bool = false

    @Parameter(title: "标题")
    var noteTitle: String

    @Parameter(title: "内容")
    var noteContent: String

    @MainActor
    func perform() async throws -> some IntentResult & ReturnsValue<NoteEntity> {
        // 实际项目中这里调用你的数据层
        let note = NoteEntity(
            id: UUID(),
            title: noteTitle,
            content: noteContent
        )
        // 保存到本地数据库或文件系统...
        print("✅ 笔记已创建: \(note.title) — \(note.content)")
        return .result(value: note)
    }
}

// 3. 定义 Entity Query——让 Spotlight / Siri 能搜索你的笔记
struct NoteQuery: EntityQuery {
    func entities(for identifiers: [UUID]) async throws -> [NoteEntity] {
        // 从数据库按 ID 查询，这里用模拟数据
        return identifiers.map { id in
            NoteEntity(id: id, title: "模拟笔记", content: "模拟内容")
        }
    }

    func suggestedEntities() async throws -> [NoteEntity] {
        // 返回最近笔记列表
        return [
            NoteEntity(id: UUID(), title: "AI 学习计划", content: "研究 Apple Intelligence"),
            NoteEntity(id: UUID(), title: "周末待办", content: "买菜、跑步")
        ]
    }
}

struct QuickNoteShortcuts: AppShortcutsProvider {
    static var appShortcuts: [AppShortcut] {
        AppShortcut(
            intent: CreateNoteIntent(),
            phrases: [
                "在\(.appName)里创建笔记",
                "用\(.appName)写一条关于\(.parameterName)的笔记"
            ],
            shortTitle: "创建笔记",
            systemImageName: "note.text"
        )
    }
}

# 1. 用 coremltools 将 Hugging Face 模型转换为 Core ML 格式
pip install coremltools transformers

# 2. Python 转换脚本（以 DistilBERT 文本分类为例）
python3 -c "
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import coremltools as ct

model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased-finetuned-sst-2-english')
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased-finetuned-sst-2-english')

# 创建 traced model
import torch
sample_input = tokenizer('This movie is great', return_tensors='pt')
traced_model = torch.jit.trace(model, (sample_input['input_ids'], sample_input['attention_mask']))

# 转换为 Core ML
mlmodel = ct.convert(
    traced_model,
    inputs=[
        ct.TensorType(name='input_ids', shape=ct.Shape(1, ct.RangeDim(1, 512))),
        ct.TensorType(name='attention_mask', shape=ct.Shape(1, ct.RangeDim(1, 512)))
    ],
    minimum_deployment_target=ct.target.iOS17
)
mlmodel.save('DistilBERTSentiment.mlpackage')
print('✅ 模型已保存')
"

import CoreML

func classifySentiment(text: String) -> String {
    guard let model = try? DistilBERTSentiment(configuration: MLModelConfiguration()) else {
        return "模型加载失败"
    }

    let tokenizer = NLTokenizer(unit: .word)
    tokenizer.string = text
    // 实际项目中应使用与训练一致的 tokenizer
    // 这里简化为 word-level 分词 + padding
    let tokens = tokenizer.enumerateTokens(in: text.startIndex..<text.endIndex) { _, _ in true }
        .compactMap { range in String(text[range]) }

    let inputIds = tokens.map { word -> Int32 in
        // 简化映射，实际应查 BERT vocab 表
        return Int32(abs(word.hashValue % 30000))
    }
    let paddedIds = inputIds + Array(repeating: 0, count: max(0, 512 - inputIds.count))
    let attentionMask = inputIds.map { _ in Int32(1) } + Array(repeating: 0, count: max(0, 512 - inputIds.count))

    let input = DistilBERTSentimentInput(
        input_ids: MLMultiArray(shape: [1, 512], dataType: .int32, initializingWith: paddedIds),
        attention_mask: MLMultiArray(shape: [1, 512], dataType: .int32, initializingWith: attentionMask)
    )

    guard let prediction = try? model.prediction(input: input) else {
        return "推理失败"
    }
    return prediction.label // "POSITIVE" 或 "NEGATIVE"
}

import SwiftUI

struct NoteEditorView: View {
    @State private var noteText = ""

    var body: some View {
        TextEditor(text: $noteText)
            .writingToolsBehavior(.automatic) // iOS 18+ 自动启用系统写作工具
            .frame(minHeight: 200)
            .padding()

        // 如果你想限制可用能力（比如只允许摘要，不允许重写）：
        // .writingToolsBehavior(.limited)
    }
}