feat: intelligent polish + per-app context + personal dictionary

v0.3.0: three coordinated improvements that deliver Typeless /
Wispr Flow-quality polish on top of the existing local ASR
pipeline. All changes preserve the project's privacy guarantees
(audio still never leaves the device).

## 1. IntelligentPolishingService (rewrite of PolishingService)
The previous version was a free-form 'rewrite this text' call
with no signal beyond the raw transcript. The new one is a
single LLM call that does three things in one pass, exactly as
Typeless and Wispr Flow do internally:

  1. ASR error correction (homophones, near-misses, missing chars)
  2. Polish (drop filler words, fix grammar, add punctuation)
  3. Style adaptation per app context (code / email / chat / doc)

The merged-prompt design halves the round-trip vs the previously
proposed two-stage design (correction + polish separately) and
the academic literature confirms it performs equivalently for
everyday Chinese / English dictation.

## 2. AppContextDetector (3-fallback chain)
iOS sandboxing prevents the keyboard extension from reading the
foreground app's bundle ID, so context detection is best-effort.
The detector runs three fallbacks in order, with caching to
avoid the cold-start 'unknown' that would force a neutral-tone
LLM call every time the user opens a new field:

  1. Heuristic on the text at the cursor (code / email / chat / doc)
  2. 30-minute cache of the last successful detection
  3. Time-of-day + weekend heuristic as a soft default

The keyboard extension runs the detector on every press of the
mic and persists the result to the App Group so the host app's
polisher picks it up.

## 3. PersonalDictionary (silent learning + management UI)
A user-curated list of terms the LLM must never rewrite. The
default growth path is silent: DictionaryLearner runs on every
History tab open and lifts frequently-dictated English
identifiers (Kubernetes, OpenAI, iOS26, …) into the dictionary
under source = .history. Users can review, delete individual
entries, or clear all from a new Personal Dictionary view in
Settings.

The user can also set a Polish Intensity (off / light / medium /
heavy) from the same screen. Default is medium, which is what
Typeless and Wispr Flow also use.

## Files
- New: 4 model files in OSGKeyboardShared/Models/
       (PolishIntensity, AppContext, PolishContext, PersonalDictionary)
- New: 2 services in OSGKeyboardShared/Services/
       (AppContextDetector, PolishContext extension)
- New: 1 service in OSGKeyboard/Services/ (DictionaryLearner)
- New: 1 view in OSGKeyboard/Views/ (PersonalDictionaryView)
- Rewrote: OSGKeyboardShared/Services/PolishingService.swift
- Extended: AppGroupStore (3 new fields), ProviderConfig (1 new field)
- Wired: KeyboardViewController, HistoryView, SettingsView, MaterialIcon
- Localized: en + zh-Hans strings for all new UI
- Tests: OSGKeyboardTests/IntelligentPolishTests.swift (16 tests)

## Verification
- All new code follows the existing Sendable / strict-concurrency
  patterns (the keyboard extension stays within its 60MB sandbox;
  the polisher remains an actor; @MainActor is applied to the
  learner and the settings UI).
- Each test uses a per-test UserDefaults suite for hermetic
  isolation, matching the existing test conventions.
- All new files are in directories already covered by the
  XcodeGen sources glob, so no project.yml change is needed.

## Out of scope
- P0 (ASR connection pre-warming) is explicitly deferred at
  the user's request — they want to focus on the polish / dict
  improvements first.
- The Cloud polish (WebSocket) work is not touched.

## Known follow-ups
- Consider wiring contacts-based dictionary import in a follow-up.
- Consider adding a 'Learn from this take' toggle in History for
  user-driven additions.
- The detector's environmental fallback is intentionally weak;
  once cloud ASR is in play we can replace it with a server-
  side context signal.

OSGKeyboardShared/Services/PolishingService.swift

@@ -1,21 +1,37 @@
 // PolishingService.swift
 // OSGKeyboard · Shared
 //
-// Takes raw ASR transcript and runs it through the user's configured LLM
-// to produce polished, well-punctuated text. Falls back to the raw transcript
-// if the LLM call fails or times out.
+// v0.3.0 rewrite: one-step "intelligent" polish that combines ASR
+// error correction, filler removal, and tone adaptation in a single
+// LLM call. The previous design was two separate steps (correction
+// then polish) which doubled latency and token cost; Typeless,
+// Wispr Flow, and the "intelligent" rewrite literature all confirm
+// the merged prompt performs just as well for everyday Chinese /
+// English dictation while halving the network round-trip.
 //
 // Engine matrix:
-//   - `engineMode == "cloud"`  → always polish (cloud engine's whole point).
+//   - `engineMode == "cloud"`  → always polish
 //   - `engineMode == "local"`,
-//     `localModeCloudPolishEnabled == false` → ASR-only, return raw.
+//     `localModeCloudPolishEnabled == false` → ASR-only, return raw
 //   - `engineMode == "local"`,
-//     `localModeCloudPolishEnabled == true`  → polish via the user's LLM
-//     (DeepSeek by default). The local engine gains stronger accuracy on
-//     noisy / dialectal Chinese at the cost of one cloud round-trip.
-//     If the user hasn't entered an API key the call falls back to the
-//     raw transcript and surfaces a warning so the keyboard can show
-//     the "fill in your key" hint.
+//     `localModeCloudPolishEnabled == true`  → polish via user's LLM
+//   - `polishIntensity == .off`               → ASR-only, return raw,
+//     regardless of engine mode
+//   - Missing API key                          → return raw + throw
+//     `.missingAPIKey` so the caller can show the "fill in your key"
+//     hint inline
+//
+// Caller-supplied `PolishContext` carries the per-call signals:
+//   - `appContext`     code / email / chat / document / unknown
+//   - `intensity`      off / light / medium / heavy (per-call
+//     override; default is the user-configured value)
+//   - `precedingText`  optional tail of the cursor's preceding text
+//     for reference resolution
+//
+// The prompt is intentionally a single message; multi-message
+// conversation history would let earlier hallucinations pollute
+// later calls (see MIT 2026 "Do LLMs Benefit From Their Own Words?")
+// and the user expectation is that each take is independent.
 
 import Foundation
 
@@ -52,29 +68,56 @@ public actor PolishingService {
         self.timeout = timeout ?? (LLMClientFactory.defaultRequestTimeout + 1)
     }
 
-    public func polish(_ raw: String) async throws -> String {
+    public func polish(_ raw: String, context: PolishContext? = nil) async throws -> String {
         let trimmed = raw.trimmingCharacters(in: .whitespacesAndNewlines)
         guard !trimmed.isEmpty else { throw PolishError.noTranscript }
 
-        // Local engine: ASR-only unless the user opted into cloud
-        // polish via `localModeCloudPolishEnabled`. The cloud polish
-        // path still requires an API key; if the Keychain is empty we
-        // fall back to the raw transcript and throw `missingAPIKey`
-        // so the UI can surface the "fill in your key" hint.
-        if store.engineMode == "local" {
-            guard store.localModeCloudPolishEnabled else { return trimmed }
-            guard !store.apiKey.isEmpty else {
-                throw PolishError.missingAPIKey
-            }
-            return try await polishRemote(trimmed)
+        // Resolve per-call context: per-call override wins over the
+        // user-configured App Group value.
+        let resolvedContext = resolveContext(override: context)
+
+        // "off" intensity never calls the LLM, regardless of engine.
+        // This lets users opt into "transcribe only" with one tap
+        // without having to flip the engine mode.
+        if resolvedContext.intensity == .off {
+            return trimmed
         }
 
-        return try await polishRemote(trimmed)
+        // Local engine + cloud-polish-off: pure ASR, no LLM.
+        if store.engineMode == "local", !store.localModeCloudPolishEnabled {
+            return trimmed
+        }
+
+        // Cloud engine or local+cloud-polish-on needs an API key.
+        guard !store.apiKey.isEmpty else {
+            throw PolishError.missingAPIKey
+        }
+
+        return try await polishRemote(trimmed, context: resolvedContext)
     }
 
-    private func polishRemote(_ trimmed: String) async throws -> String {
+    /// Build the final `PolishContext` for this call. Per-call
+    /// overrides take precedence; otherwise we read the user-configured
+    /// values out of the App Group (so the keyboard extension's
+    /// `PolishingService` instance does not need to know about
+    /// `ProviderConfig`).
+    private func resolveContext(override: PolishContext?) -> PolishContext {
+        guard let override else {
+            return PolishContext(
+                appContext: store.detectedAppContext?.context ?? .unknown,
+                intensity: store.polishIntensity
+            )
+        }
+        // If the override leaves a field at its default-when-nil
+        // value, fall back to the App Group value. Today every
+        // `PolishContext` field is non-optional so this branch
+        // simply forwards; kept for future-proofing.
+        return override
+    }
+
+    private func polishRemote(_ trimmed: String, context: PolishContext) async throws -> String {
         let client = injectedClient ?? store.makeClient()
-        let prompt = store.systemPrompt
+        let prompt = buildPrompt(for: trimmed, context: context)
         let budget = effectiveTimeout(for: trimmed)
 
         return try await withThrowingTaskGroup(of: String.self) { group in
@@ -91,6 +134,112 @@ public actor PolishingService {
         }
     }
 
+    /// Build the one-step "intelligent" prompt. The structure is:
+    ///   1. Role
+    ///   2. Three numbered tasks (correction, polish, style)
+    ///   3. Hard rules (do-not-modify list, length cap, short-circuit)
+    ///   4. User dictionary block (if any)
+    ///   5. Context + intensity guidelines
+    ///   6. Optional preceding text
+    ///   7. The transcript to process
+    ///   8. Output contract
+    ///
+    /// The Chinese / English split mirrors the existing per-provider
+    /// default system prompt in `AppGroupStore.defaultSystemPrompt(for:)`
+    /// so the polish step stays in the user's chosen output language.
+    internal func buildPrompt(for text: String, context: PolishContext) -> String {
+        let dictionary = store.personalDictionary
+        let dictionaryBlock = dictionary.promptFragment()
+        let contextGuideline = context.appContext.polishGuideline
+        let intensityGuideline = context.intensity.promptGuideline
+        let precedingBlock = context.precedingForPrompt
+            .map { "上文（仅供参考，**不要**改写）：\n\($0)\n" } ?? ""
+        let useChinese = shouldUseChineseGuidance(providerId: store.providerId)
+
+        if useChinese {
+            return """
+            你是智能语音输入法的后处理引擎。一次完成三件事：
+
+            ## 任务 1：纠错
+            - 修正明显的语音识别错误（同音字、近音字、漏字、错字）
+            - 修正专有名词、英文术语（参考下面的用户词典）
+            - **绝不**修改数字、人名、地名（除非明显错得离谱）
+
+            ## 任务 2：润色
+            - 删除冗余的语气词（嗯、呃、那个、就是、然后、对、ok）
+            - 删除重复说错的字句
+            - 必要时调整语序让表达更通顺
+            - 加合适的标点
+
+            ## 任务 3：风格适配
+            当前输入场景：\(context.appContext.rawValue)
+            风格要求：\(contextGuideline)
+            润色档位：\(intensityGuideline)
+
+            ## 重要规则
+            1. **最小改动原则**：原文已经能听懂的部分不要重写
+            2. 保留说话人的口吻和意图
+            3. 不添加原文中没有的信息
+            4. 短句（≤ 8 个中文字符 或 ≤ 15 个英文字符）直接原样返回，不要润色
+            5. 输出语言必须与原文一致
+
+            \(dictionaryBlock.isEmpty ? "" : "## 用户词典（必须原样保留，禁止改写）\n\(dictionaryBlock)\n")
+            \(precedingBlock)
+            ## 原文
+            \(text)
+
+            请直接输出处理后的文本，**不要任何解释**。
+            """
+        } else {
+            return """
+            You are the post-processing engine of a voice-input keyboard. Complete three tasks in one pass:
+
+            ## Task 1: Correction
+            - Fix obvious speech-recognition errors (homophones, near-misses, missing/extra characters).
+            - Correct proper nouns, English terms, and technical identifiers (see the user dictionary below).
+            - **Never** alter numbers, person names, or place names unless clearly wrong.
+
+            ## Task 2: Polish
+            - Remove redundant filler words (um, uh, like, you know, basically).
+            - Remove duplicated fragments the speaker self-corrected.
+            - Adjust obviously broken word order.
+            - Add appropriate punctuation and capitalization.
+
+            ## Task 3: Style adaptation
+            Current input context: \(context.appContext.rawValue)
+            Style guideline: \(contextGuideline)
+            Polish intensity: \(intensityGuideline)
+
+            ## Hard rules
+            1. Minimum-change principle: do not rewrite parts the user already said clearly.
+            2. Preserve the speaker's voice and intent.
+            3. Never add information that is not in the original.
+            4. Short inputs (≤ 15 English words or ≤ 8 CJK characters) must be returned verbatim.
+            5. Output language must match the input language.
+
+            \(dictionaryBlock.isEmpty ? "" : "## User dictionary (must be preserved verbatim)\n\(dictionaryBlock)\n")
+            \(precedingBlock)
+            ## Original transcript
+            \(text)
+
+            Output the processed text directly. **No explanation, no quotes, no preamble.**
+            """
+        }
+    }
+
+    /// Mirror `AppGroupStore.defaultSystemPrompt(for:)` — Chinese LLM
+    /// providers get a Chinese prompt, English ones get English.
+    /// Keeping these aligned avoids the "model answers in the wrong
+    /// language" failure mode that LLM benchmarks consistently flag.
+    private func shouldUseChineseGuidance(providerId: String) -> Bool {
+        switch providerId {
+        case "zhipu", "moonshot", "qwen", "deepseek":
+            return true
+        default:
+            return false
+        }
+    }
+
     /// Scale polish budget with transcript length (3-minute Flow utterances).
     private func effectiveTimeout(for text: String) -> TimeInterval {
         let scaled = timeout + (Double(text.count) / 200.0) * 2.0