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link2026
@@ -135,6 +135,13 @@ struct HealthExportService {
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throw ServiceError.generationFailed("模型未输出任何内容")
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}
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// —— 追加确定性趋势段(不经 LLM,零编造) ——
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let trendBlock = Self.trendSection(snapshot.trends)
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if !trendBlock.isEmpty {
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generated += trendBlock
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continuation.yield(.token(TokenChunk(text: trendBlock, decodeRate: 0)))
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}
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// —— Phase 4: 持久化 ——
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let export = HealthExport(
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prompt: prompt,
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@@ -170,6 +177,146 @@ struct HealthExportService {
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}
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}
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/// 多轮导出页的单轮问答。只回答,不入库。
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func answer(question: String,
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conversation: [HealthExportDialogueTurn],
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in modelContext: ModelContext) -> AsyncThrowingStream<TokenChunk, Error> {
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AsyncThrowingStream { continuation in
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let task = Task { @MainActor in
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do {
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do {
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try await AIRuntime.shared.prepare()
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} catch {
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throw ServiceError.modelNotReady
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}
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let snapshot = Self.retrieveDialogueSnapshot(ctx: modelContext)
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let dataJSON = Self.serializeData(snapshot: snapshot)
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let transcript = HealthExportDialogueTurn.transcript(from: conversation)
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let prompt = HealthExportPrompts.dialogueAnswer(
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latestQuestion: question,
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transcript: transcript,
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dataJSON: dataJSON
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)
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var displayed = ""
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var rawAccum = ""
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let stream = await AIRuntime.shared.generate(prompt: prompt, maxTokens: 480)
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for try await chunk in stream {
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try Task.checkCancellation()
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rawAccum += chunk.text
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let clean = Self.stripThinkBlocks(rawAccum)
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if clean.count > displayed.count, clean.hasPrefix(displayed) {
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let delta = String(clean.dropFirst(displayed.count))
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displayed = clean
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continuation.yield(TokenChunk(text: delta, decodeRate: chunk.decodeRate))
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} else if clean != displayed {
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displayed = clean
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}
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}
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guard !displayed.trimmingCharacters(in: .whitespacesAndNewlines).isEmpty else {
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throw ServiceError.generationFailed("模型未输出任何内容")
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}
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continuation.finish()
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} catch is CancellationError {
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continuation.finish(throwing: ServiceError.cancelled)
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} catch let e as ServiceError {
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continuation.finish(throwing: e)
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} catch {
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continuation.finish(throwing: ServiceError.generationFailed("\(error)"))
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}
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}
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continuation.onTermination = { _ in task.cancel() }
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}
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}
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/// 多轮导出页的最终报告生成。保存为现有 HealthExport 历史。
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func export(conversation: [HealthExportDialogueTurn],
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in modelContext: ModelContext) -> AsyncThrowingStream<Event, Error> {
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AsyncThrowingStream { continuation in
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let task = Task { @MainActor in
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do {
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do {
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try await AIRuntime.shared.prepare()
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} catch {
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throw ServiceError.modelNotReady
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}
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continuation.yield(.phaseChanged(.retrieving))
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let snapshot = Self.retrieveDialogueSnapshot(ctx: modelContext)
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let dataJSON = Self.serializeData(snapshot: snapshot)
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let transcript = HealthExportDialogueTurn.transcript(from: conversation)
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try Task.checkCancellation()
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continuation.yield(.phaseChanged(.generating))
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let genPrompt = HealthExportPrompts.dialogueReportGeneration(
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transcript: transcript,
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dataJSON: dataJSON
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)
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var generated = ""
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var rawAccum = ""
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var lastRate: Double = 0
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let stream = await AIRuntime.shared.generate(prompt: genPrompt, maxTokens: 1200)
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for try await chunk in stream {
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try Task.checkCancellation()
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if chunk.decodeRate > 0 { lastRate = chunk.decodeRate }
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rawAccum += chunk.text
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let clean = Self.stripThinkBlocks(rawAccum)
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if clean.count > generated.count, clean.hasPrefix(generated) {
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let delta = String(clean.dropFirst(generated.count))
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generated = clean
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continuation.yield(.token(TokenChunk(text: delta, decodeRate: chunk.decodeRate)))
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} else if clean != generated {
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generated = clean
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}
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}
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guard !generated.trimmingCharacters(in: .whitespacesAndNewlines).isEmpty else {
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throw ServiceError.generationFailed("模型未输出任何内容")
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}
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// —— 追加确定性趋势段(不经 LLM,零编造) ——
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let trendBlock = Self.trendSection(snapshot.trends)
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if !trendBlock.isEmpty {
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generated += trendBlock
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continuation.yield(.token(TokenChunk(text: trendBlock, decodeRate: 0)))
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}
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let export = HealthExport(
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prompt: transcript,
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content: generated,
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referencedIndicatorIDs: snapshot.indicators.map { Self.idString($0.persistentModelID) },
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referencedReportIDs: [],
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referencedSymptomIDs: [],
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referencedDiaryIDs: snapshot.diaries.map { Self.idString($0.persistentModelID) },
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inferredTimeFromDate: snapshot.fromDate,
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inferredTimeToDate: snapshot.toDate,
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inferredIntent: "dialogue_export",
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inferredLabelCN: "对话整理",
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modelTag: ModelKind.llm.rawValue,
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decodeRate: lastRate
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)
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modelContext.insert(export)
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do { try modelContext.save() } catch {
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print("[HealthExportService] save failed: \(error)")
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}
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continuation.yield(.phaseChanged(.completed))
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continuation.yield(.completed(persistentID: export.persistentModelID))
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continuation.finish()
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} catch is CancellationError {
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continuation.finish(throwing: ServiceError.cancelled)
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} catch let e as ServiceError {
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continuation.finish(throwing: e)
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} catch {
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continuation.finish(throwing: ServiceError.generationFailed("\(error)"))
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}
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}
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continuation.onTermination = { _ in task.cancel() }
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}
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}
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// MARK: - Phase 1: intent extraction
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struct Intent: Sendable {
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@@ -251,6 +398,8 @@ struct HealthExportService {
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var reports: [Report]
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var diaries: [DiaryEntry]
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var profile: UserProfile
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/// 相关指标的趋势行(确定性计算,不进 LLM)。空 → 不渲染「## 指标趋势」段。
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var trends: [ExportTrend] = []
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}
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/// 同步 SwiftData 查询。@MainActor。
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@@ -265,7 +414,8 @@ struct HealthExportService {
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predicate: #Predicate { $0.capturedAt >= fromDate && $0.capturedAt <= toDate },
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sortBy: [SortDescriptor(\.capturedAt, order: .reverse)]
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)
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var indicators = (try? ctx.fetch(indDesc)) ?? []
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let allInWindow = (try? ctx.fetch(indDesc)) ?? []
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var indicators = allInWindow
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if !intent.keywords.isEmpty {
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let filtered = indicators.filter { ind in
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intent.keywords.contains { kw in
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@@ -328,6 +478,14 @@ struct HealthExportService {
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// —— Profile(单例) ——
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let profile = UserProfileStore.loadOrCreate(in: ctx)
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// —— 趋势(确定性,不进 LLM) ——
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// 用全量 in-window 还原完整序列;裁剪后的 indicators 决定哪些 series 相关。
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let trends = ExportTrendBuilder.build(
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allInWindow: allInWindow,
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relevant: indicators,
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profile: profile
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)
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return Snapshot(
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fromDate: fromDate,
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toDate: toDate,
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@@ -335,8 +493,44 @@ struct HealthExportService {
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symptoms: symptoms,
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reports: reports,
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diaries: diaries,
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profile: profile,
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trends: trends
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)
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}
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/// 多轮导出使用全量指标 + 健康日记作为上下文。为控制 prompt 体积,日记正文在序列化阶段截断。
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static func retrieveDialogueSnapshot(ctx: ModelContext) -> Snapshot {
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let indicatorDesc = FetchDescriptor<Indicator>(
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sortBy: [SortDescriptor(\.capturedAt, order: .reverse)]
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)
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let diaryDesc = FetchDescriptor<DiaryEntry>(
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sortBy: [SortDescriptor(\.createdAt, order: .reverse)]
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)
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let indicators = (try? ctx.fetch(indicatorDesc)) ?? []
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let diaries = (try? ctx.fetch(diaryDesc)) ?? []
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let profile = UserProfileStore.loadOrCreate(in: ctx)
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let dates = indicators.map(\.capturedAt) + diaries.map(\.createdAt)
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let fromDate = dates.min() ?? Date()
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let toDate = dates.max() ?? Date()
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// 多轮导出用全量指标,全部视为相关。
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let trends = ExportTrendBuilder.build(
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allInWindow: indicators,
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relevant: indicators,
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profile: profile
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)
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return Snapshot(
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fromDate: fromDate,
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toDate: toDate,
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indicators: indicators,
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symptoms: [],
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reports: [],
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diaries: diaries,
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profile: profile,
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trends: trends
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)
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}
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// MARK: - Phase 3: serialize data for prompt
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@@ -480,6 +674,12 @@ struct HealthExportService {
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"""
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}
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/// 把趋势行拼成追加到 LLM 输出末尾的「## 指标趋势」段。空 → 返回空串(整段省略)。
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static func trendSection(_ trends: [ExportTrend]) -> String {
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guard !trends.isEmpty else { return "" }
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return "\n\n## 指标趋势\n" + trends.map { $0.line() }.joined(separator: "\n")
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}
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// MARK: - Helpers
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/// 把 SwiftData persistentModelID 编成稳定字符串。
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