refactor(coordinator): 抽出 llm_pipeline 子模块
机械式行为保持拆分：从 coordinator.rs 搬出 LLM 润色/翻译/provider 构建/凭证读取管线 —— 流式与一次性润色、润色+翻译合并、各 ASR/LLM provider 凭证读取与 ActiveLLMProvider 构建、provider 分类器、中文字形偏好、词库 phrases/hotwords 读取。polish-translate marker 常量经 glob 重导出供父模块 tests 的 super:: 引用。 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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@@ -65,6 +65,7 @@ mod capsule;
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mod dictation;
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mod hotkey_supervisors;
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mod ime_insertion;
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mod llm_pipeline;
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mod qa;
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mod resources;
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mod voice_agent_hotkeys;
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@@ -74,6 +75,7 @@ mod voice_agent_hotkeys;
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pub(crate) use capsule::*;
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pub(crate) use hotkey_supervisors::*;
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pub(crate) use ime_insertion::*;
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pub(crate) use llm_pipeline::*;
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pub(crate) use voice_agent_hotkeys::*;
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#[cfg(test)]
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@@ -1094,16 +1096,6 @@ impl Coordinator {
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}
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}
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fn raw_style_pack_uses_llm(pack: &crate::types::StylePack) -> bool {
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!(pack.kind == crate::types::StylePackKind::Builtin
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&& pack.id == crate::types::BUILTIN_STYLE_PACK_RAW_ID
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&& pack.prompt == crate::types::StyleSystemPrompts::default().raw)
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}
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fn raw_mode_uses_llm(style_system_prompt: &str) -> bool {
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style_system_prompt != crate::types::StyleSystemPrompts::default().raw
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}
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// ─────────────────────────── hotkey bridging ───────────────────────────
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// ─────────────────────────── session lifecycle ───────────────────────────
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@@ -1382,71 +1374,6 @@ async fn build_local_qwen3(
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Ok(Arc::new(crate::asr::local::LocalQwenAsr::new(app, engine)))
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}
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/// `whisper` 是 OpenAI 原生；`siliconflow` / `zhipu` / `groq` 都暴露
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/// OpenAI 兼容的 `/audio/transcriptions`，统一走 `WhisperBatchASR`。
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/// 新增 OpenAI 兼容 ASR 时只需在这里加一项。
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///
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/// 注：DashScope 的 Qwen3-ASR-Flash 不在此列——它用 MultiModalConversation
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/// (messages=[{content:[{audio:...}]}]) 协议，不是 Whisper multipart，需要
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/// 单独 ASR 客户端，留给 V2。
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fn is_whisper_compatible_provider(id: &str) -> bool {
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matches!(
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id,
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"whisper" | "siliconflow" | "zhipu" | "groq" | "openrouter"
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)
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}
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/// 该 provider 的请求体编码方式。OpenRouter 的 `/audio/transcriptions` 是
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/// `application/json` + base64 音频（issue #582），其余兼容厂商沿用 multipart。
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fn whisper_request_format(provider_id: &str) -> crate::asr::whisper::AsrRequestFormat {
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match provider_id {
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"openrouter" => crate::asr::whisper::AsrRequestFormat::OpenRouterJson,
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_ => crate::asr::whisper::AsrRequestFormat::Multipart,
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}
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}
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/// 该 provider 的 `/audio/transcriptions` 是否支持 `response_format=verbose_json`
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/// 并返回带 `no_speech_prob` / `avg_logprob` / `compression_ratio` 的 segments，
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/// 用于幻听过滤。
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///
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/// - `whisper`（OpenAI）/ `groq`：原生 Whisper，完整支持，过滤有效。
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/// - `siliconflow`：模型是 SenseVoice / TeleSpeech，文档无 `response_format`，
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/// 发送 verbose_json 可能被拒，**保持关闭**走旧的 `json`。
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/// - `zhipu`（GLM-ASR）：虽接受 verbose_json，但不产出上述指标，过滤是空转；
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/// 为最小化行为变更，这里也**保持关闭**，仅对确证有收益的 whisper/groq 开启。
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fn whisper_supports_verbose_json(provider_id: &str) -> bool {
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matches!(provider_id, "whisper" | "groq")
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}
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fn is_bailian_provider(id: &str) -> bool {
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id == crate::asr::bailian::PROVIDER_ID
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}
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fn is_mimo_provider(id: &str) -> bool {
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id == crate::asr::mimo::PROVIDER_ID
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}
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fn apply_chinese_script_preference(text: &str, pref: ChineseScriptPreference) -> String {
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if text.is_empty() {
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return String::new();
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}
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let config = match pref {
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ChineseScriptPreference::Simplified => Some(BuiltinConfig::T2s),
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ChineseScriptPreference::Traditional => Some(BuiltinConfig::S2t),
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ChineseScriptPreference::Auto => None,
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};
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let Some(config) = config else {
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return text.to_string();
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};
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match OpenCC::from_config(config) {
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Ok(converter) => converter.convert(text),
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Err(err) => {
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log::warn!("[coord] OpenCC init failed, skip script conversion: {err}");
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text.to_string()
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}
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}
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}
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enum QaAsrStart {
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Volcengine {
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asr: Arc<VolcengineStreamingASR>,
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@@ -1614,456 +1541,6 @@ async fn build_qa_asr_start(inner: &Arc<Inner>, active_asr: &str) -> Result<QaAs
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}
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}
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/// 润色文本；失败时返回原文 + 失败原因，调用方据此弹错误胶囊 + 写历史 error_code。
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/// 之前固定返回 String，调用方拿不到失败信号 → 用户感知"为什么风格设置没生效"。issue #57。
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/// 流式润色的三态结果。让上层（dictation pipeline）能区分「已经流出去了」、
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/// 「降级到一次性」和「真失败了走 raw 兜底」三种 case。
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pub enum StreamingPolishOutcome {
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/// 流式润色成功，`String` 是已经一边流一边交给 `on_delta` 的全部文本（用于写
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/// history、做词条命中统计）。调用方不应再 `inserter.insert(&text)`，因为字符
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/// 已经通过键盘事件落到光标处。
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Streamed(String),
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/// 当前配置不支持流式：用户没开 streaming_insert / Gemini provider / Codex
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/// provider / Raw 模式 / 翻译模式 / 不是 macOS。调用方应回到现有的
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/// `polish_or_passthrough` 一次性路径，跟历史行为完全一致。
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UnsupportedFallback,
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/// 流式过程中失败（HTTP / 解析 / 空流等）。`String` 是失败原因，调用方应当
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/// 走 raw 兜底（同 `polish_or_passthrough` 失败分支的语义）。
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Failed(String),
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}
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/// 流式润色入口。在不支持流式的所有 case 都返回 `UnsupportedFallback`，让调用方
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/// 透明降级。不修改任何持久化 / 焦点 / 光标状态。
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///
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/// `on_delta` 每收到一个 SSE chunk 就被调用一次（同步），调用方负责把 chunk 实际
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/// 模拟键盘事件落到光标 —— 见 `coordinator/dictation.rs` 的流式分支。
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/// `should_cancel` 用户取消时返回 true，立即 break SSE 读循环避免烧 quota。
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pub async fn polish_or_passthrough_streaming<F, C>(
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raw: &RawTranscript,
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mode: PolishMode,
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hotwords: &[String],
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style_system_prompt: &str,
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working_languages: &[String],
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chinese_script_preference: ChineseScriptPreference,
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output_language_preference: OutputLanguagePreference,
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llm_thinking_enabled: bool,
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front_app: Option<&str>,
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prior_turns: &[(String, String)],
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on_delta: F,
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should_cancel: C,
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) -> StreamingPolishOutcome
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where
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F: Fn(&str) + Send + Sync,
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C: Fn() -> bool + Send + Sync,
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{
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if mode == PolishMode::Raw && !raw_mode_uses_llm(style_system_prompt) {
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log::info!("[coord] streaming polish skipped: mode=Raw, fall back to one-shot");
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return StreamingPolishOutcome::UnsupportedFallback;
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}
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let active_llm = CredentialsVault::get_active_llm();
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if active_llm == "gemini" {
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log::info!(
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"[coord] streaming polish skipped: active LLM provider=gemini (v1 not implemented), fall back to one-shot"
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);
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return StreamingPolishOutcome::UnsupportedFallback;
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}
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let provider = match build_active_llm_provider(llm_thinking_enabled) {
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Ok(p) => p,
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Err(e) => {
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log::error!("[coord] streaming polish: build provider failed: {e}");
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return StreamingPolishOutcome::Failed(e.to_string());
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}
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};
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if !provider.supports_streaming_polish() {
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log::info!(
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"[coord] streaming polish skipped: provider does not support streaming (likely codex OAuth), fall back to one-shot"
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);
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return StreamingPolishOutcome::UnsupportedFallback;
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}
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log::info!(
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"[coord] streaming polish START: provider=openai-compatible mode={:?} raw_chars={} prior_turns={}",
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mode,
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raw.text.chars().count(),
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prior_turns.len()
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);
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match provider
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.polish_streaming(
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&raw.text,
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mode,
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hotwords,
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style_system_prompt,
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working_languages,
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chinese_script_preference,
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output_language_preference,
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front_app,
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prior_turns,
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on_delta,
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should_cancel,
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)
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.await
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{
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Ok(text) => {
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log::info!(
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"[coord] streaming polish OK: final_chars={}",
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text.chars().count()
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);
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StreamingPolishOutcome::Streamed(text)
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}
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Err(e) => {
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let reason = e.to_string();
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log::error!("[coord] streaming polish FAILED: {reason}");
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StreamingPolishOutcome::Failed(reason)
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}
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}
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}
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async fn polish_or_passthrough(
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raw: &RawTranscript,
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mode: PolishMode,
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hotwords: &[String],
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style_system_prompt: &str,
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working_languages: &[String],
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chinese_script_preference: ChineseScriptPreference,
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output_language_preference: OutputLanguagePreference,
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llm_thinking_enabled: bool,
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front_app: Option<&str>,
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prior_turns: &[(String, String)],
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) -> (String, Option<String>) {
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if mode == PolishMode::Raw && !raw_mode_uses_llm(style_system_prompt) {
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return (raw.text.clone(), None);
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}
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match polish_text(
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&raw.text,
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mode,
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hotwords,
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style_system_prompt,
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working_languages,
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chinese_script_preference,
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output_language_preference,
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llm_thinking_enabled,
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front_app,
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prior_turns,
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)
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.await
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{
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Ok(s) => (s, None),
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Err(e) => {
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let reason = e.to_string();
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log::error!("[coord] polish failed, falling back to raw: {reason}");
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(raw.text.clone(), Some(reason))
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}
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}
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}
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async fn polish_text(
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raw: &str,
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mode: PolishMode,
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hotwords: &[String],
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style_system_prompt: &str,
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working_languages: &[String],
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chinese_script_preference: ChineseScriptPreference,
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output_language_preference: OutputLanguagePreference,
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llm_thinking_enabled: bool,
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front_app: Option<&str>,
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prior_turns: &[(String, String)],
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) -> anyhow::Result<String> {
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// 谷歌 Gemini 分支：所有 LLM provider 共用 ark.* 凭据槽，唯独 Gemini 走原生
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// generateContent / 自带 thinkingConfig 控制；其余 provider 走 OpenAI
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// 兼容协议，并在该路径里按 provider/channel 下发对应的思考开关。
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let active_llm = CredentialsVault::get_active_llm();
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if active_llm == "gemini" {
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let (api_key, model, base_url) = read_gemini_credentials()?;
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let provider = GeminiProvider::new(
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GeminiConfig::new(api_key, model, base_url).with_thinking_enabled(llm_thinking_enabled),
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);
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return Ok(provider
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.polish(
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raw,
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mode,
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hotwords,
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style_system_prompt,
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working_languages,
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chinese_script_preference,
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output_language_preference,
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front_app,
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prior_turns,
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)
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.await?);
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}
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let provider = build_active_llm_provider(llm_thinking_enabled)?;
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Ok(provider
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.polish(
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raw,
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mode,
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hotwords,
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style_system_prompt,
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working_languages,
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chinese_script_preference,
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output_language_preference,
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front_app,
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prior_turns,
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)
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.await?)
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}
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/// 专用翻译（仅翻译、不润色、单轮）。现作为"润色+翻译"合成调用解析失败时的兜底——
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/// 模型没按两段格式输出时，退回这里拿一段干净译文，而不是把畸形输出当译文插入。
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async fn translate_text(
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raw: &str,
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target_language: &str,
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working_languages: &[String],
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chinese_script_preference: ChineseScriptPreference,
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output_language_preference: OutputLanguagePreference,
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llm_thinking_enabled: bool,
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front_app: Option<&str>,
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) -> anyhow::Result<String> {
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// 见 polish_text 顶部注释——同样的 Gemini / OpenAI-compatible 路由逻辑。
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let active_llm = CredentialsVault::get_active_llm();
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if active_llm == "gemini" {
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let (api_key, model, base_url) = read_gemini_credentials()?;
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let provider = GeminiProvider::new(
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GeminiConfig::new(api_key, model, base_url).with_thinking_enabled(llm_thinking_enabled),
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||||
);
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||||
return Ok(provider
|
||||
.translate_to(
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raw,
|
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target_language,
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working_languages,
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chinese_script_preference,
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output_language_preference,
|
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front_app,
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)
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.await?);
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}
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let provider = build_active_llm_provider(llm_thinking_enabled)?;
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Ok(provider
|
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.translate_to(
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raw,
|
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target_language,
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||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
front_app,
|
||||
)
|
||||
.await?)
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}
|
||||
|
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/// "润色+翻译"单次调用的两段哨兵。模型按 `SRC\n源文\nTGT\n译文` 输出，解析器据此切分。
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/// 这两个串必须与 build_polish_translate_system_prompt 写给模型的完全一致。
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const POLISH_TRANSLATE_SRC_MARKER: &str = "[[OPENLESS_POLISHED_SOURCE]]";
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const POLISH_TRANSLATE_TGT_MARKER: &str = "[[OPENLESS_TRANSLATION]]";
|
||||
|
||||
/// 合成"先润色源文、再翻译"的系统提示词：在原翻译 prompt 之上追加"额外输出润色后源文"
|
||||
/// 与严格两段格式（覆盖原 prompt 末尾的"只输出译文"）。译文仍是要插入用户光标的主产物，
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/// 故完整保留原翻译规则；润色后的源文只作对话上下文用，轻量清理即可。
|
||||
fn build_polish_translate_system_prompt(target_language: &str) -> String {
|
||||
let base = crate::polish::prompts::translate_system_prompt(target_language);
|
||||
format!(
|
||||
"{base}\n\n\
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||||
# 额外输出：润色后的源文（仅用于对话上下文，不展示给用户）\n\
|
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在译文之前，先把上面的原始转写**按它本来的语言**润色一遍：去掉口癖（嗯 / 那个 / um）、\
|
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补必要标点、纠正明显的识别错误，但**不翻译、不改写风格、不增删意思**。\n\n\
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# 输出格式（覆盖上面\u{201C}只输出译文\u{201D}的说明，严格遵守）\n\
|
||||
严格按下面两段输出，两个标记必须原样出现、各占一行，标记之外不要有任何多余文字：\n\
|
||||
{src}\n\
|
||||
（这里放润色后的源文，保持原语言）\n\
|
||||
{tgt}\n\
|
||||
（这里放翻译成\u{300C}{lang}\u{300D}的译文）",
|
||||
base = base,
|
||||
src = POLISH_TRANSLATE_SRC_MARKER,
|
||||
tgt = POLISH_TRANSLATE_TGT_MARKER,
|
||||
lang = target_language,
|
||||
)
|
||||
}
|
||||
|
||||
/// 解析"润色+翻译"单次调用输出 → Some((润色后源文, 译文))。
|
||||
/// 找到译文标记且译文非空 → Some((源文, 译文))：源文标记缺失 / 源文段为空时源文为 None，
|
||||
/// 译文取标记之后的干净正文。**没有译文标记、或译文段为空（模型截断 / 只吐了标记）→ None**，
|
||||
/// 表示没拿到可信译文，交由调用方退回专用翻译——避免把空串当"成功译文"插进光标而丢字。
|
||||
fn split_polish_translate_output(raw: &str) -> Option<(Option<String>, String)> {
|
||||
let tgt_idx = raw.find(POLISH_TRANSLATE_TGT_MARKER)?;
|
||||
let translation = raw[tgt_idx + POLISH_TRANSLATE_TGT_MARKER.len()..]
|
||||
.trim()
|
||||
.to_string();
|
||||
if translation.is_empty() {
|
||||
return None;
|
||||
}
|
||||
let before_tgt = &raw[..tgt_idx];
|
||||
let source = before_tgt
|
||||
.find(POLISH_TRANSLATE_SRC_MARKER)
|
||||
.map(|i| {
|
||||
before_tgt[i + POLISH_TRANSLATE_SRC_MARKER.len()..]
|
||||
.trim()
|
||||
.to_string()
|
||||
})
|
||||
.filter(|s| !s.is_empty());
|
||||
Some((source, translation))
|
||||
}
|
||||
|
||||
/// 翻译路径——单次 LLM 调用同时润色源文 + 翻译。和 polish 一样失败时返回原文 + 失败原因，
|
||||
/// 避免"不丢字"约定被违反（CLAUDE.md）。返回 (要插入的译文, 润色后源文供上下文用, 失败原因)。
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
async fn polish_and_translate_or_passthrough(
|
||||
raw: &RawTranscript,
|
||||
target_language: &str,
|
||||
mode: PolishMode,
|
||||
hotwords: &[String],
|
||||
working_languages: &[String],
|
||||
chinese_script_preference: ChineseScriptPreference,
|
||||
output_language_preference: OutputLanguagePreference,
|
||||
llm_thinking_enabled: bool,
|
||||
front_app: Option<&str>,
|
||||
prior_turns: &[(String, String)],
|
||||
) -> (String, Option<String>, Option<String>) {
|
||||
let system_prompt = build_polish_translate_system_prompt(target_language);
|
||||
match polish_text(
|
||||
&raw.text,
|
||||
mode,
|
||||
hotwords,
|
||||
&system_prompt,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
llm_thinking_enabled,
|
||||
front_app,
|
||||
prior_turns,
|
||||
)
|
||||
.await
|
||||
{
|
||||
Ok(out) => match split_polish_translate_output(&out) {
|
||||
Some((source, translation)) => (translation, source, None),
|
||||
None => {
|
||||
// 模型没按两段格式输出：退回专用翻译拿一段干净译文，避免把畸形输出插进光标。
|
||||
// 此时无可信源文，这条翻译历史不参与后续普通润色上下文。
|
||||
log::warn!(
|
||||
"[coord] polish+translate output missing markers; falling back to plain translate"
|
||||
);
|
||||
match translate_text(
|
||||
&raw.text,
|
||||
target_language,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
llm_thinking_enabled,
|
||||
front_app,
|
||||
)
|
||||
.await
|
||||
{
|
||||
Ok(translation) => (translation, None, None),
|
||||
Err(e) => {
|
||||
let reason = e.to_string();
|
||||
log::error!("[coord] fallback translate failed, using raw: {reason}");
|
||||
(raw.text.clone(), None, Some(reason))
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
Err(e) => {
|
||||
let reason = e.to_string();
|
||||
log::error!("[coord] polish+translate failed, falling back to raw: {reason}");
|
||||
(raw.text.clone(), None, Some(reason))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn read_whisper_credentials() -> (String, String, String) {
|
||||
let api_key = CredentialsVault::get(CredentialAccount::AsrApiKey)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let base_url = CredentialsVault::get(CredentialAccount::AsrEndpoint)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let model = CredentialsVault::get(CredentialAccount::AsrModel)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.is_empty())
|
||||
.unwrap_or_else(|| "whisper-1".to_string());
|
||||
(api_key, base_url, model)
|
||||
}
|
||||
|
||||
fn read_mimo_credentials() -> (String, String, String) {
|
||||
let api_key = CredentialsVault::get(CredentialAccount::AsrApiKey)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let base_url = CredentialsVault::get(CredentialAccount::AsrEndpoint)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| crate::asr::mimo::DEFAULT_ENDPOINT.to_string());
|
||||
let model = CredentialsVault::get(CredentialAccount::AsrModel)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| crate::asr::mimo::DEFAULT_MODEL.to_string());
|
||||
(api_key, base_url, model)
|
||||
}
|
||||
|
||||
fn read_bailian_credentials() -> BailianCredentials {
|
||||
let api_key = CredentialsVault::get(CredentialAccount::AsrApiKey)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let endpoint = CredentialsVault::get(CredentialAccount::AsrEndpoint)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| crate::asr::bailian::DEFAULT_ENDPOINT.to_string());
|
||||
let model = CredentialsVault::get(CredentialAccount::AsrModel)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| crate::asr::bailian::DEFAULT_MODEL.to_string());
|
||||
let vocabulary_id = CredentialsVault::get(CredentialAccount::AsrVocabularyId)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.trim().is_empty());
|
||||
BailianCredentials {
|
||||
api_key,
|
||||
endpoint,
|
||||
model,
|
||||
vocabulary_id,
|
||||
}
|
||||
}
|
||||
|
||||
fn read_volc_credentials() -> VolcengineCredentials {
|
||||
let app_id = CredentialsVault::get(CredentialAccount::VolcengineAppKey)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let access_token = CredentialsVault::get(CredentialAccount::VolcengineAccessKey)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let resource_id = CredentialsVault::get(CredentialAccount::VolcengineResourceId)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.is_empty())
|
||||
.unwrap_or_else(|| VolcengineCredentials::default_resource_id().to_string());
|
||||
VolcengineCredentials {
|
||||
app_id,
|
||||
access_token,
|
||||
resource_id,
|
||||
}
|
||||
}
|
||||
|
||||
fn enabled_hotwords(inner: &Arc<Inner>) -> Vec<DictionaryHotword> {
|
||||
inner
|
||||
.vocab
|
||||
.list()
|
||||
.unwrap_or_default()
|
||||
.into_iter()
|
||||
.map(|e| DictionaryHotword {
|
||||
phrase: e.phrase,
|
||||
enabled: e.enabled,
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
// ─────────────────────────── QA session lifecycle ───────────────────────────
|
||||
|
||||
/// 划词语音问答会话（issue #118）。
|
||||
@@ -2717,120 +2194,6 @@ fn cancel_qa_session(inner: &Arc<Inner>) {
|
||||
log::info!("[coord] QA session cancelled (was {phase:?})");
|
||||
}
|
||||
|
||||
async fn answer_chat_dispatch<F, C>(
|
||||
messages: &[crate::types::QaChatMessage],
|
||||
working_languages: &[String],
|
||||
chinese_script_preference: ChineseScriptPreference,
|
||||
output_language_preference: OutputLanguagePreference,
|
||||
llm_thinking_enabled: bool,
|
||||
front_app: Option<&str>,
|
||||
on_delta: F,
|
||||
should_cancel: C,
|
||||
) -> anyhow::Result<String>
|
||||
where
|
||||
F: Fn(&str) + Send + Sync,
|
||||
C: Fn() -> bool + Send + Sync,
|
||||
{
|
||||
// 见 polish_text 顶部注释——同样的 Gemini / OpenAI-compatible 路由逻辑，
|
||||
// QA 流式回答走 Gemini 原生 :streamGenerateContent?alt=sse。
|
||||
let active_llm = CredentialsVault::get_active_llm();
|
||||
if active_llm == "gemini" {
|
||||
let (api_key, model, base_url) = read_gemini_credentials()?;
|
||||
let provider = GeminiProvider::new(
|
||||
GeminiConfig::new(api_key, model, base_url).with_thinking_enabled(llm_thinking_enabled),
|
||||
);
|
||||
return Ok(provider
|
||||
.answer_chat_streaming(
|
||||
messages,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
front_app,
|
||||
on_delta,
|
||||
should_cancel,
|
||||
)
|
||||
.await?);
|
||||
}
|
||||
|
||||
let provider = build_active_llm_provider(llm_thinking_enabled)?;
|
||||
Ok(provider
|
||||
.answer_chat_streaming(
|
||||
messages,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
front_app,
|
||||
on_delta,
|
||||
should_cancel,
|
||||
)
|
||||
.await?)
|
||||
}
|
||||
|
||||
/// 读 Gemini 凭据。所有 LLM provider 共用 ark.* 槽位（persistence 没做 per-provider
|
||||
/// 隔离），所以这里也是从 `ArkApiKey` / `ArkModelId` / `ArkEndpoint` 三个槽读，
|
||||
/// 但回退默认值改成谷歌的：base_url 默认 `https://generativelanguage.googleapis.com/v1beta`，
|
||||
/// 模型默认 `gemini-2.5-flash`。Settings.tsx::onLlmProviderChange 在用户切到 gemini
|
||||
/// 时会强制把 endpoint/model 覆盖为这两个默认值，所以 99% 情况下槽里读出来就是
|
||||
/// 这两个；这里的 `unwrap_or_else` 是给极端情况兜底（如旧版本切换 bug 留下的脏数据）。
|
||||
///
|
||||
/// base_url 末尾去掉 `/`，让 `llm_gemini::generate_content_url` 拼接稳定。
|
||||
/// 不去 `/chat/completions` 后缀——OpenAI 兼容路径才会有那个后缀，原生 Gemini 不会。
|
||||
fn read_gemini_credentials() -> anyhow::Result<(String, String, String)> {
|
||||
let api_key = CredentialsVault::get(CredentialAccount::ArkApiKey)?.unwrap_or_default();
|
||||
let model = CredentialsVault::get(CredentialAccount::ArkModelId)?
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| "gemini-2.5-flash".to_string());
|
||||
let base_url = CredentialsVault::get(CredentialAccount::ArkEndpoint)?
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| "https://generativelanguage.googleapis.com/v1beta".to_string());
|
||||
if api_key.trim().is_empty() {
|
||||
anyhow::bail!("API Key 为空");
|
||||
}
|
||||
let base_url = base_url.trim_end_matches('/').to_string();
|
||||
Ok((api_key, model, base_url))
|
||||
}
|
||||
|
||||
fn build_active_llm_provider(llm_thinking_enabled: bool) -> anyhow::Result<ActiveLLMProvider> {
|
||||
let active = CredentialsVault::get_active_llm();
|
||||
let model =
|
||||
CredentialsVault::get(CredentialAccount::ArkModelId)?.filter(|s| !s.trim().is_empty());
|
||||
if active == CODEX_OAUTH_PROVIDER_ID {
|
||||
let config =
|
||||
CodexOAuthConfig::new(model.unwrap_or_else(|| CODEX_DEFAULT_MODEL.to_string()))
|
||||
.with_thinking_enabled(llm_thinking_enabled);
|
||||
return Ok(ActiveLLMProvider::Codex(CodexOAuthLLMProvider::new(config)));
|
||||
}
|
||||
|
||||
let api_key = CredentialsVault::get(CredentialAccount::ArkApiKey)?.unwrap_or_default();
|
||||
let model = model.unwrap_or_else(|| "deepseek-v3-2".to_string());
|
||||
let endpoint = resolve_ark_endpoint(&api_key)?;
|
||||
let base_url = endpoint
|
||||
.trim_end_matches("/chat/completions")
|
||||
.trim_end_matches('/')
|
||||
.to_string();
|
||||
let config = OpenAICompatibleConfig::new(active, "OpenLess LLM", base_url, api_key, model)
|
||||
.with_thinking_enabled(llm_thinking_enabled);
|
||||
Ok(ActiveLLMProvider::OpenAI(OpenAICompatibleLLMProvider::new(
|
||||
config,
|
||||
)))
|
||||
}
|
||||
|
||||
fn resolve_ark_endpoint(api_key: &str) -> anyhow::Result<String> {
|
||||
let endpoint = CredentialsVault::get(CredentialAccount::ArkEndpoint)?.filter(|s| !s.is_empty());
|
||||
resolve_ark_endpoint_with_policy(api_key, endpoint)
|
||||
}
|
||||
|
||||
fn resolve_ark_endpoint_with_policy(
|
||||
api_key: &str,
|
||||
endpoint: Option<String>,
|
||||
) -> anyhow::Result<String> {
|
||||
if api_key.trim().is_empty() && endpoint.is_none() {
|
||||
anyhow::bail!("API Key 为空");
|
||||
}
|
||||
Ok(endpoint
|
||||
.unwrap_or_else(|| "https://ark.cn-beijing.volces.com/api/v3/chat/completions".to_string()))
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::dictation::abort_recording_with_error;
|
||||
@@ -3682,17 +3045,6 @@ mod tests {
|
||||
}
|
||||
}
|
||||
|
||||
fn enabled_phrases(inner: &Arc<Inner>) -> Vec<String> {
|
||||
inner
|
||||
.vocab
|
||||
.list()
|
||||
.unwrap_or_default()
|
||||
.into_iter()
|
||||
.filter(|e| e.enabled)
|
||||
.map(|e| e.phrase)
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Coordinator 全局超时保护：防止 ASR await_final_result() 永远挂起。
|
||||
/// 设置为 15 秒（比 ASR 的 12 秒 FINAL_RESULT_TIMEOUT 稍长），
|
||||
/// 只在 ASR 超时机制失效时作为最后的防线触发。
|
||||
|
||||
@@ -0,0 +1,660 @@
|
||||
//! LLM 润色 / 翻译 / provider 构建 / 凭证读取 管线。
|
||||
//!
|
||||
//! 从 `coordinator.rs` 机械拆出（行为保持，仅可见性提升）：流式/一次性润色、
|
||||
//! 润色+翻译合并、各 ASR/LLM provider 凭证读取与 ActiveLLMProvider 构建、
|
||||
//! provider 分类器与中文字形偏好、词库 phrases/hotwords 读取。
|
||||
//! polish-translate marker 常量原样保留（父模块 tests 经 super:: 引用）。
|
||||
|
||||
use super::*;
|
||||
|
||||
pub(crate) fn raw_style_pack_uses_llm(pack: &crate::types::StylePack) -> bool {
|
||||
!(pack.kind == crate::types::StylePackKind::Builtin
|
||||
&& pack.id == crate::types::BUILTIN_STYLE_PACK_RAW_ID
|
||||
&& pack.prompt == crate::types::StyleSystemPrompts::default().raw)
|
||||
}
|
||||
|
||||
pub(crate) fn raw_mode_uses_llm(style_system_prompt: &str) -> bool {
|
||||
style_system_prompt != crate::types::StyleSystemPrompts::default().raw
|
||||
}
|
||||
|
||||
/// `whisper` 是 OpenAI 原生；`siliconflow` / `zhipu` / `groq` 都暴露
|
||||
/// OpenAI 兼容的 `/audio/transcriptions`，统一走 `WhisperBatchASR`。
|
||||
/// 新增 OpenAI 兼容 ASR 时只需在这里加一项。
|
||||
///
|
||||
/// 注：DashScope 的 Qwen3-ASR-Flash 不在此列——它用 MultiModalConversation
|
||||
/// (messages=[{content:[{audio:...}]}]) 协议，不是 Whisper multipart，需要
|
||||
/// 单独 ASR 客户端，留给 V2。
|
||||
pub(crate) fn is_whisper_compatible_provider(id: &str) -> bool {
|
||||
matches!(
|
||||
id,
|
||||
"whisper" | "siliconflow" | "zhipu" | "groq" | "openrouter"
|
||||
)
|
||||
}
|
||||
|
||||
/// 该 provider 的请求体编码方式。OpenRouter 的 `/audio/transcriptions` 是
|
||||
/// `application/json` + base64 音频（issue #582），其余兼容厂商沿用 multipart。
|
||||
pub(crate) fn whisper_request_format(provider_id: &str) -> crate::asr::whisper::AsrRequestFormat {
|
||||
match provider_id {
|
||||
"openrouter" => crate::asr::whisper::AsrRequestFormat::OpenRouterJson,
|
||||
_ => crate::asr::whisper::AsrRequestFormat::Multipart,
|
||||
}
|
||||
}
|
||||
|
||||
/// 该 provider 的 `/audio/transcriptions` 是否支持 `response_format=verbose_json`
|
||||
/// 并返回带 `no_speech_prob` / `avg_logprob` / `compression_ratio` 的 segments，
|
||||
/// 用于幻听过滤。
|
||||
///
|
||||
/// - `whisper`（OpenAI）/ `groq`：原生 Whisper，完整支持，过滤有效。
|
||||
/// - `siliconflow`：模型是 SenseVoice / TeleSpeech，文档无 `response_format`，
|
||||
/// 发送 verbose_json 可能被拒，**保持关闭**走旧的 `json`。
|
||||
/// - `zhipu`（GLM-ASR）：虽接受 verbose_json，但不产出上述指标，过滤是空转；
|
||||
/// 为最小化行为变更，这里也**保持关闭**，仅对确证有收益的 whisper/groq 开启。
|
||||
pub(crate) fn whisper_supports_verbose_json(provider_id: &str) -> bool {
|
||||
matches!(provider_id, "whisper" | "groq")
|
||||
}
|
||||
|
||||
pub(crate) fn is_bailian_provider(id: &str) -> bool {
|
||||
id == crate::asr::bailian::PROVIDER_ID
|
||||
}
|
||||
|
||||
pub(crate) fn is_mimo_provider(id: &str) -> bool {
|
||||
id == crate::asr::mimo::PROVIDER_ID
|
||||
}
|
||||
|
||||
pub(crate) fn apply_chinese_script_preference(text: &str, pref: ChineseScriptPreference) -> String {
|
||||
if text.is_empty() {
|
||||
return String::new();
|
||||
}
|
||||
let config = match pref {
|
||||
ChineseScriptPreference::Simplified => Some(BuiltinConfig::T2s),
|
||||
ChineseScriptPreference::Traditional => Some(BuiltinConfig::S2t),
|
||||
ChineseScriptPreference::Auto => None,
|
||||
};
|
||||
let Some(config) = config else {
|
||||
return text.to_string();
|
||||
};
|
||||
match OpenCC::from_config(config) {
|
||||
Ok(converter) => converter.convert(text),
|
||||
Err(err) => {
|
||||
log::warn!("[coord] OpenCC init failed, skip script conversion: {err}");
|
||||
text.to_string()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// 润色文本；失败时返回原文 + 失败原因，调用方据此弹错误胶囊 + 写历史 error_code。
|
||||
/// 之前固定返回 String，调用方拿不到失败信号 → 用户感知"为什么风格设置没生效"。issue #57。
|
||||
/// 流式润色的三态结果。让上层（dictation pipeline）能区分「已经流出去了」、
|
||||
/// 「降级到一次性」和「真失败了走 raw 兜底」三种 case。
|
||||
pub(crate) enum StreamingPolishOutcome {
|
||||
/// 流式润色成功，`String` 是已经一边流一边交给 `on_delta` 的全部文本（用于写
|
||||
/// history、做词条命中统计）。调用方不应再 `inserter.insert(&text)`，因为字符
|
||||
/// 已经通过键盘事件落到光标处。
|
||||
Streamed(String),
|
||||
/// 当前配置不支持流式：用户没开 streaming_insert / Gemini provider / Codex
|
||||
/// provider / Raw 模式 / 翻译模式 / 不是 macOS。调用方应回到现有的
|
||||
/// `polish_or_passthrough` 一次性路径，跟历史行为完全一致。
|
||||
UnsupportedFallback,
|
||||
/// 流式过程中失败（HTTP / 解析 / 空流等）。`String` 是失败原因，调用方应当
|
||||
/// 走 raw 兜底（同 `polish_or_passthrough` 失败分支的语义）。
|
||||
Failed(String),
|
||||
}
|
||||
|
||||
/// 流式润色入口。在不支持流式的所有 case 都返回 `UnsupportedFallback`，让调用方
|
||||
/// 透明降级。不修改任何持久化 / 焦点 / 光标状态。
|
||||
///
|
||||
/// `on_delta` 每收到一个 SSE chunk 就被调用一次（同步），调用方负责把 chunk 实际
|
||||
/// 模拟键盘事件落到光标 —— 见 `coordinator/dictation.rs` 的流式分支。
|
||||
/// `should_cancel` 用户取消时返回 true，立即 break SSE 读循环避免烧 quota。
|
||||
pub(crate) async fn polish_or_passthrough_streaming<F, C>(
|
||||
raw: &RawTranscript,
|
||||
mode: PolishMode,
|
||||
hotwords: &[String],
|
||||
style_system_prompt: &str,
|
||||
working_languages: &[String],
|
||||
chinese_script_preference: ChineseScriptPreference,
|
||||
output_language_preference: OutputLanguagePreference,
|
||||
llm_thinking_enabled: bool,
|
||||
front_app: Option<&str>,
|
||||
prior_turns: &[(String, String)],
|
||||
on_delta: F,
|
||||
should_cancel: C,
|
||||
) -> StreamingPolishOutcome
|
||||
where
|
||||
F: Fn(&str) + Send + Sync,
|
||||
C: Fn() -> bool + Send + Sync,
|
||||
{
|
||||
if mode == PolishMode::Raw && !raw_mode_uses_llm(style_system_prompt) {
|
||||
log::info!("[coord] streaming polish skipped: mode=Raw, fall back to one-shot");
|
||||
return StreamingPolishOutcome::UnsupportedFallback;
|
||||
}
|
||||
let active_llm = CredentialsVault::get_active_llm();
|
||||
if active_llm == "gemini" {
|
||||
log::info!(
|
||||
"[coord] streaming polish skipped: active LLM provider=gemini (v1 not implemented), fall back to one-shot"
|
||||
);
|
||||
return StreamingPolishOutcome::UnsupportedFallback;
|
||||
}
|
||||
let provider = match build_active_llm_provider(llm_thinking_enabled) {
|
||||
Ok(p) => p,
|
||||
Err(e) => {
|
||||
log::error!("[coord] streaming polish: build provider failed: {e}");
|
||||
return StreamingPolishOutcome::Failed(e.to_string());
|
||||
}
|
||||
};
|
||||
if !provider.supports_streaming_polish() {
|
||||
log::info!(
|
||||
"[coord] streaming polish skipped: provider does not support streaming (likely codex OAuth), fall back to one-shot"
|
||||
);
|
||||
return StreamingPolishOutcome::UnsupportedFallback;
|
||||
}
|
||||
log::info!(
|
||||
"[coord] streaming polish START: provider=openai-compatible mode={:?} raw_chars={} prior_turns={}",
|
||||
mode,
|
||||
raw.text.chars().count(),
|
||||
prior_turns.len()
|
||||
);
|
||||
match provider
|
||||
.polish_streaming(
|
||||
&raw.text,
|
||||
mode,
|
||||
hotwords,
|
||||
style_system_prompt,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
front_app,
|
||||
prior_turns,
|
||||
on_delta,
|
||||
should_cancel,
|
||||
)
|
||||
.await
|
||||
{
|
||||
Ok(text) => {
|
||||
log::info!(
|
||||
"[coord] streaming polish OK: final_chars={}",
|
||||
text.chars().count()
|
||||
);
|
||||
StreamingPolishOutcome::Streamed(text)
|
||||
}
|
||||
Err(e) => {
|
||||
let reason = e.to_string();
|
||||
log::error!("[coord] streaming polish FAILED: {reason}");
|
||||
StreamingPolishOutcome::Failed(reason)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) async fn polish_or_passthrough(
|
||||
raw: &RawTranscript,
|
||||
mode: PolishMode,
|
||||
hotwords: &[String],
|
||||
style_system_prompt: &str,
|
||||
working_languages: &[String],
|
||||
chinese_script_preference: ChineseScriptPreference,
|
||||
output_language_preference: OutputLanguagePreference,
|
||||
llm_thinking_enabled: bool,
|
||||
front_app: Option<&str>,
|
||||
prior_turns: &[(String, String)],
|
||||
) -> (String, Option<String>) {
|
||||
if mode == PolishMode::Raw && !raw_mode_uses_llm(style_system_prompt) {
|
||||
return (raw.text.clone(), None);
|
||||
}
|
||||
match polish_text(
|
||||
&raw.text,
|
||||
mode,
|
||||
hotwords,
|
||||
style_system_prompt,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
llm_thinking_enabled,
|
||||
front_app,
|
||||
prior_turns,
|
||||
)
|
||||
.await
|
||||
{
|
||||
Ok(s) => (s, None),
|
||||
Err(e) => {
|
||||
let reason = e.to_string();
|
||||
log::error!("[coord] polish failed, falling back to raw: {reason}");
|
||||
(raw.text.clone(), Some(reason))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) async fn polish_text(
|
||||
raw: &str,
|
||||
mode: PolishMode,
|
||||
hotwords: &[String],
|
||||
style_system_prompt: &str,
|
||||
working_languages: &[String],
|
||||
chinese_script_preference: ChineseScriptPreference,
|
||||
output_language_preference: OutputLanguagePreference,
|
||||
llm_thinking_enabled: bool,
|
||||
front_app: Option<&str>,
|
||||
prior_turns: &[(String, String)],
|
||||
) -> anyhow::Result<String> {
|
||||
// 谷歌 Gemini 分支：所有 LLM provider 共用 ark.* 凭据槽，唯独 Gemini 走原生
|
||||
// generateContent / 自带 thinkingConfig 控制；其余 provider 走 OpenAI
|
||||
// 兼容协议，并在该路径里按 provider/channel 下发对应的思考开关。
|
||||
let active_llm = CredentialsVault::get_active_llm();
|
||||
if active_llm == "gemini" {
|
||||
let (api_key, model, base_url) = read_gemini_credentials()?;
|
||||
let provider = GeminiProvider::new(
|
||||
GeminiConfig::new(api_key, model, base_url).with_thinking_enabled(llm_thinking_enabled),
|
||||
);
|
||||
return Ok(provider
|
||||
.polish(
|
||||
raw,
|
||||
mode,
|
||||
hotwords,
|
||||
style_system_prompt,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
front_app,
|
||||
prior_turns,
|
||||
)
|
||||
.await?);
|
||||
}
|
||||
|
||||
let provider = build_active_llm_provider(llm_thinking_enabled)?;
|
||||
Ok(provider
|
||||
.polish(
|
||||
raw,
|
||||
mode,
|
||||
hotwords,
|
||||
style_system_prompt,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
front_app,
|
||||
prior_turns,
|
||||
)
|
||||
.await?)
|
||||
}
|
||||
|
||||
/// 专用翻译（仅翻译、不润色、单轮）。现作为"润色+翻译"合成调用解析失败时的兜底——
|
||||
/// 模型没按两段格式输出时，退回这里拿一段干净译文，而不是把畸形输出当译文插入。
|
||||
pub(crate) async fn translate_text(
|
||||
raw: &str,
|
||||
target_language: &str,
|
||||
working_languages: &[String],
|
||||
chinese_script_preference: ChineseScriptPreference,
|
||||
output_language_preference: OutputLanguagePreference,
|
||||
llm_thinking_enabled: bool,
|
||||
front_app: Option<&str>,
|
||||
) -> anyhow::Result<String> {
|
||||
// 见 polish_text 顶部注释——同样的 Gemini / OpenAI-compatible 路由逻辑。
|
||||
let active_llm = CredentialsVault::get_active_llm();
|
||||
if active_llm == "gemini" {
|
||||
let (api_key, model, base_url) = read_gemini_credentials()?;
|
||||
let provider = GeminiProvider::new(
|
||||
GeminiConfig::new(api_key, model, base_url).with_thinking_enabled(llm_thinking_enabled),
|
||||
);
|
||||
return Ok(provider
|
||||
.translate_to(
|
||||
raw,
|
||||
target_language,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
front_app,
|
||||
)
|
||||
.await?);
|
||||
}
|
||||
|
||||
let provider = build_active_llm_provider(llm_thinking_enabled)?;
|
||||
Ok(provider
|
||||
.translate_to(
|
||||
raw,
|
||||
target_language,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
front_app,
|
||||
)
|
||||
.await?)
|
||||
}
|
||||
|
||||
/// "润色+翻译"单次调用的两段哨兵。模型按 `SRC\n源文\nTGT\n译文` 输出，解析器据此切分。
|
||||
/// 这两个串必须与 build_polish_translate_system_prompt 写给模型的完全一致。
|
||||
pub(crate) const POLISH_TRANSLATE_SRC_MARKER: &str = "[[OPENLESS_POLISHED_SOURCE]]";
|
||||
pub(crate) const POLISH_TRANSLATE_TGT_MARKER: &str = "[[OPENLESS_TRANSLATION]]";
|
||||
|
||||
/// 合成"先润色源文、再翻译"的系统提示词：在原翻译 prompt 之上追加"额外输出润色后源文"
|
||||
/// 与严格两段格式（覆盖原 prompt 末尾的"只输出译文"）。译文仍是要插入用户光标的主产物，
|
||||
/// 故完整保留原翻译规则；润色后的源文只作对话上下文用，轻量清理即可。
|
||||
pub(crate) fn build_polish_translate_system_prompt(target_language: &str) -> String {
|
||||
let base = crate::polish::prompts::translate_system_prompt(target_language);
|
||||
format!(
|
||||
"{base}\n\n\
|
||||
# 额外输出：润色后的源文（仅用于对话上下文，不展示给用户）\n\
|
||||
在译文之前，先把上面的原始转写**按它本来的语言**润色一遍：去掉口癖（嗯 / 那个 / um）、\
|
||||
补必要标点、纠正明显的识别错误，但**不翻译、不改写风格、不增删意思**。\n\n\
|
||||
# 输出格式（覆盖上面\u{201C}只输出译文\u{201D}的说明，严格遵守）\n\
|
||||
严格按下面两段输出，两个标记必须原样出现、各占一行，标记之外不要有任何多余文字：\n\
|
||||
{src}\n\
|
||||
（这里放润色后的源文，保持原语言）\n\
|
||||
{tgt}\n\
|
||||
（这里放翻译成\u{300C}{lang}\u{300D}的译文）",
|
||||
base = base,
|
||||
src = POLISH_TRANSLATE_SRC_MARKER,
|
||||
tgt = POLISH_TRANSLATE_TGT_MARKER,
|
||||
lang = target_language,
|
||||
)
|
||||
}
|
||||
|
||||
/// 解析"润色+翻译"单次调用输出 → Some((润色后源文, 译文))。
|
||||
/// 找到译文标记且译文非空 → Some((源文, 译文))：源文标记缺失 / 源文段为空时源文为 None，
|
||||
/// 译文取标记之后的干净正文。**没有译文标记、或译文段为空（模型截断 / 只吐了标记）→ None**，
|
||||
/// 表示没拿到可信译文，交由调用方退回专用翻译——避免把空串当"成功译文"插进光标而丢字。
|
||||
pub(crate) fn split_polish_translate_output(raw: &str) -> Option<(Option<String>, String)> {
|
||||
let tgt_idx = raw.find(POLISH_TRANSLATE_TGT_MARKER)?;
|
||||
let translation = raw[tgt_idx + POLISH_TRANSLATE_TGT_MARKER.len()..]
|
||||
.trim()
|
||||
.to_string();
|
||||
if translation.is_empty() {
|
||||
return None;
|
||||
}
|
||||
let before_tgt = &raw[..tgt_idx];
|
||||
let source = before_tgt
|
||||
.find(POLISH_TRANSLATE_SRC_MARKER)
|
||||
.map(|i| {
|
||||
before_tgt[i + POLISH_TRANSLATE_SRC_MARKER.len()..]
|
||||
.trim()
|
||||
.to_string()
|
||||
})
|
||||
.filter(|s| !s.is_empty());
|
||||
Some((source, translation))
|
||||
}
|
||||
|
||||
/// 翻译路径——单次 LLM 调用同时润色源文 + 翻译。和 polish 一样失败时返回原文 + 失败原因，
|
||||
/// 避免"不丢字"约定被违反（CLAUDE.md）。返回 (要插入的译文, 润色后源文供上下文用, 失败原因)。
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub(crate) async fn polish_and_translate_or_passthrough(
|
||||
raw: &RawTranscript,
|
||||
target_language: &str,
|
||||
mode: PolishMode,
|
||||
hotwords: &[String],
|
||||
working_languages: &[String],
|
||||
chinese_script_preference: ChineseScriptPreference,
|
||||
output_language_preference: OutputLanguagePreference,
|
||||
llm_thinking_enabled: bool,
|
||||
front_app: Option<&str>,
|
||||
prior_turns: &[(String, String)],
|
||||
) -> (String, Option<String>, Option<String>) {
|
||||
let system_prompt = build_polish_translate_system_prompt(target_language);
|
||||
match polish_text(
|
||||
&raw.text,
|
||||
mode,
|
||||
hotwords,
|
||||
&system_prompt,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
llm_thinking_enabled,
|
||||
front_app,
|
||||
prior_turns,
|
||||
)
|
||||
.await
|
||||
{
|
||||
Ok(out) => match split_polish_translate_output(&out) {
|
||||
Some((source, translation)) => (translation, source, None),
|
||||
None => {
|
||||
// 模型没按两段格式输出：退回专用翻译拿一段干净译文，避免把畸形输出插进光标。
|
||||
// 此时无可信源文，这条翻译历史不参与后续普通润色上下文。
|
||||
log::warn!(
|
||||
"[coord] polish+translate output missing markers; falling back to plain translate"
|
||||
);
|
||||
match translate_text(
|
||||
&raw.text,
|
||||
target_language,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
llm_thinking_enabled,
|
||||
front_app,
|
||||
)
|
||||
.await
|
||||
{
|
||||
Ok(translation) => (translation, None, None),
|
||||
Err(e) => {
|
||||
let reason = e.to_string();
|
||||
log::error!("[coord] fallback translate failed, using raw: {reason}");
|
||||
(raw.text.clone(), None, Some(reason))
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
Err(e) => {
|
||||
let reason = e.to_string();
|
||||
log::error!("[coord] polish+translate failed, falling back to raw: {reason}");
|
||||
(raw.text.clone(), None, Some(reason))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) fn read_whisper_credentials() -> (String, String, String) {
|
||||
let api_key = CredentialsVault::get(CredentialAccount::AsrApiKey)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let base_url = CredentialsVault::get(CredentialAccount::AsrEndpoint)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let model = CredentialsVault::get(CredentialAccount::AsrModel)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.is_empty())
|
||||
.unwrap_or_else(|| "whisper-1".to_string());
|
||||
(api_key, base_url, model)
|
||||
}
|
||||
|
||||
pub(crate) fn read_mimo_credentials() -> (String, String, String) {
|
||||
let api_key = CredentialsVault::get(CredentialAccount::AsrApiKey)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let base_url = CredentialsVault::get(CredentialAccount::AsrEndpoint)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| crate::asr::mimo::DEFAULT_ENDPOINT.to_string());
|
||||
let model = CredentialsVault::get(CredentialAccount::AsrModel)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| crate::asr::mimo::DEFAULT_MODEL.to_string());
|
||||
(api_key, base_url, model)
|
||||
}
|
||||
|
||||
pub(crate) fn read_bailian_credentials() -> BailianCredentials {
|
||||
let api_key = CredentialsVault::get(CredentialAccount::AsrApiKey)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let endpoint = CredentialsVault::get(CredentialAccount::AsrEndpoint)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| crate::asr::bailian::DEFAULT_ENDPOINT.to_string());
|
||||
let model = CredentialsVault::get(CredentialAccount::AsrModel)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| crate::asr::bailian::DEFAULT_MODEL.to_string());
|
||||
let vocabulary_id = CredentialsVault::get(CredentialAccount::AsrVocabularyId)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.trim().is_empty());
|
||||
BailianCredentials {
|
||||
api_key,
|
||||
endpoint,
|
||||
model,
|
||||
vocabulary_id,
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) fn read_volc_credentials() -> VolcengineCredentials {
|
||||
let app_id = CredentialsVault::get(CredentialAccount::VolcengineAppKey)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let access_token = CredentialsVault::get(CredentialAccount::VolcengineAccessKey)
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or_default();
|
||||
let resource_id = CredentialsVault::get(CredentialAccount::VolcengineResourceId)
|
||||
.ok()
|
||||
.flatten()
|
||||
.filter(|s| !s.is_empty())
|
||||
.unwrap_or_else(|| VolcengineCredentials::default_resource_id().to_string());
|
||||
VolcengineCredentials {
|
||||
app_id,
|
||||
access_token,
|
||||
resource_id,
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) fn enabled_hotwords(inner: &Arc<Inner>) -> Vec<DictionaryHotword> {
|
||||
inner
|
||||
.vocab
|
||||
.list()
|
||||
.unwrap_or_default()
|
||||
.into_iter()
|
||||
.map(|e| DictionaryHotword {
|
||||
phrase: e.phrase,
|
||||
enabled: e.enabled,
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
pub(crate) async fn answer_chat_dispatch<F, C>(
|
||||
messages: &[crate::types::QaChatMessage],
|
||||
working_languages: &[String],
|
||||
chinese_script_preference: ChineseScriptPreference,
|
||||
output_language_preference: OutputLanguagePreference,
|
||||
llm_thinking_enabled: bool,
|
||||
front_app: Option<&str>,
|
||||
on_delta: F,
|
||||
should_cancel: C,
|
||||
) -> anyhow::Result<String>
|
||||
where
|
||||
F: Fn(&str) + Send + Sync,
|
||||
C: Fn() -> bool + Send + Sync,
|
||||
{
|
||||
// 见 polish_text 顶部注释——同样的 Gemini / OpenAI-compatible 路由逻辑，
|
||||
// QA 流式回答走 Gemini 原生 :streamGenerateContent?alt=sse。
|
||||
let active_llm = CredentialsVault::get_active_llm();
|
||||
if active_llm == "gemini" {
|
||||
let (api_key, model, base_url) = read_gemini_credentials()?;
|
||||
let provider = GeminiProvider::new(
|
||||
GeminiConfig::new(api_key, model, base_url).with_thinking_enabled(llm_thinking_enabled),
|
||||
);
|
||||
return Ok(provider
|
||||
.answer_chat_streaming(
|
||||
messages,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
front_app,
|
||||
on_delta,
|
||||
should_cancel,
|
||||
)
|
||||
.await?);
|
||||
}
|
||||
|
||||
let provider = build_active_llm_provider(llm_thinking_enabled)?;
|
||||
Ok(provider
|
||||
.answer_chat_streaming(
|
||||
messages,
|
||||
working_languages,
|
||||
chinese_script_preference,
|
||||
output_language_preference,
|
||||
front_app,
|
||||
on_delta,
|
||||
should_cancel,
|
||||
)
|
||||
.await?)
|
||||
}
|
||||
|
||||
/// 读 Gemini 凭据。所有 LLM provider 共用 ark.* 槽位（persistence 没做 per-provider
|
||||
/// 隔离），所以这里也是从 `ArkApiKey` / `ArkModelId` / `ArkEndpoint` 三个槽读，
|
||||
/// 但回退默认值改成谷歌的：base_url 默认 `https://generativelanguage.googleapis.com/v1beta`，
|
||||
/// 模型默认 `gemini-2.5-flash`。Settings.tsx::onLlmProviderChange 在用户切到 gemini
|
||||
/// 时会强制把 endpoint/model 覆盖为这两个默认值，所以 99% 情况下槽里读出来就是
|
||||
/// 这两个；这里的 `unwrap_or_else` 是给极端情况兜底（如旧版本切换 bug 留下的脏数据）。
|
||||
///
|
||||
/// base_url 末尾去掉 `/`，让 `llm_gemini::generate_content_url` 拼接稳定。
|
||||
/// 不去 `/chat/completions` 后缀——OpenAI 兼容路径才会有那个后缀，原生 Gemini 不会。
|
||||
pub(crate) fn read_gemini_credentials() -> anyhow::Result<(String, String, String)> {
|
||||
let api_key = CredentialsVault::get(CredentialAccount::ArkApiKey)?.unwrap_or_default();
|
||||
let model = CredentialsVault::get(CredentialAccount::ArkModelId)?
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| "gemini-2.5-flash".to_string());
|
||||
let base_url = CredentialsVault::get(CredentialAccount::ArkEndpoint)?
|
||||
.filter(|s| !s.trim().is_empty())
|
||||
.unwrap_or_else(|| "https://generativelanguage.googleapis.com/v1beta".to_string());
|
||||
if api_key.trim().is_empty() {
|
||||
anyhow::bail!("API Key 为空");
|
||||
}
|
||||
let base_url = base_url.trim_end_matches('/').to_string();
|
||||
Ok((api_key, model, base_url))
|
||||
}
|
||||
|
||||
pub(crate) fn build_active_llm_provider(
|
||||
llm_thinking_enabled: bool,
|
||||
) -> anyhow::Result<ActiveLLMProvider> {
|
||||
let active = CredentialsVault::get_active_llm();
|
||||
let model =
|
||||
CredentialsVault::get(CredentialAccount::ArkModelId)?.filter(|s| !s.trim().is_empty());
|
||||
if active == CODEX_OAUTH_PROVIDER_ID {
|
||||
let config =
|
||||
CodexOAuthConfig::new(model.unwrap_or_else(|| CODEX_DEFAULT_MODEL.to_string()))
|
||||
.with_thinking_enabled(llm_thinking_enabled);
|
||||
return Ok(ActiveLLMProvider::Codex(CodexOAuthLLMProvider::new(config)));
|
||||
}
|
||||
|
||||
let api_key = CredentialsVault::get(CredentialAccount::ArkApiKey)?.unwrap_or_default();
|
||||
let model = model.unwrap_or_else(|| "deepseek-v3-2".to_string());
|
||||
let endpoint = resolve_ark_endpoint(&api_key)?;
|
||||
let base_url = endpoint
|
||||
.trim_end_matches("/chat/completions")
|
||||
.trim_end_matches('/')
|
||||
.to_string();
|
||||
let config = OpenAICompatibleConfig::new(active, "OpenLess LLM", base_url, api_key, model)
|
||||
.with_thinking_enabled(llm_thinking_enabled);
|
||||
Ok(ActiveLLMProvider::OpenAI(OpenAICompatibleLLMProvider::new(
|
||||
config,
|
||||
)))
|
||||
}
|
||||
|
||||
pub(crate) fn resolve_ark_endpoint(api_key: &str) -> anyhow::Result<String> {
|
||||
let endpoint = CredentialsVault::get(CredentialAccount::ArkEndpoint)?.filter(|s| !s.is_empty());
|
||||
resolve_ark_endpoint_with_policy(api_key, endpoint)
|
||||
}
|
||||
|
||||
pub(crate) fn resolve_ark_endpoint_with_policy(
|
||||
api_key: &str,
|
||||
endpoint: Option<String>,
|
||||
) -> anyhow::Result<String> {
|
||||
if api_key.trim().is_empty() && endpoint.is_none() {
|
||||
anyhow::bail!("API Key 为空");
|
||||
}
|
||||
Ok(endpoint
|
||||
.unwrap_or_else(|| "https://ark.cn-beijing.volces.com/api/v3/chat/completions".to_string()))
|
||||
}
|
||||
|
||||
pub(crate) fn enabled_phrases(inner: &Arc<Inner>) -> Vec<String> {
|
||||
inner
|
||||
.vocab
|
||||
.list()
|
||||
.unwrap_or_default()
|
||||
.into_iter()
|
||||
.filter(|e| e.enabled)
|
||||
.map(|e| e.phrase)
|
||||
.collect()
|
||||
}
|
||||
Reference in new issue
Block a user