Major quality pass on top of Phase 2. Five substantive changes plus
cross-cutting touches across audio, hotkey, transcription, and Tauri
command layers.
Transcription quality
- Long-audio chunking in commands/transcription.rs: Parakeet and large
file transcription now chunk-and-recompose with overlap trimming, so
the live-path chunking advantage extends to file-based workflows.
- Stateful live speech gate in commands/live.rs on top of the earlier
duplicate-boundary filtering — distinguishes start-of-speech from
mid-speech and holds state across chunks.
Auto-learning corrections
- New crates/ai-formatting/src/correction_learning.rs: extracts user
text corrections from viewer edits and proposes additions to the
active profile's vocabulary.
- src-tauri/src/commands/profiles.rs bridge for frontend-driven
confirmation of learned terms.
- src/routes/viewer/+page.svelte hooks the learning path into the
segment-edit flow so corrections feed profile_terms without a
separate 'train this profile' UX.
Transcript profile provenance
- Migration v8 (crates/storage/src/migrations.rs) adds profile_id to
transcripts, defaulting to DEFAULT_PROFILE_ID so existing rows stay
valid.
- crates/storage/src/database.rs: TranscriptRow + CRUD carry profile_id.
- src-tauri/src/commands/transcripts.rs: add_transcript accepts and
persists profile_id.
- DictationPage.svelte + FilesPage.svelte send activeProfileId on
capture so learned corrections are attributed to the right profile.
Cleanup prompt contract
- crates/ai-formatting/src/llm_client.rs hardened: the CLEANUP_PROMPT
now specifies concrete do/do-not rules, ready for a real model-backed
cleanup pass. The llm_client is still a stub — kon-llm remains unwired
— but the prompt shape is final.
Cross-cutting polish
- Minor touches in audio (capture/decode/resample), hotkey (lib/linux/stub),
core, transcription (concurrency/model_manager/local_engine/whisper_rs),
and the rest of src-tauri/src/commands/*: error-path tightening, log
clarity, TS-migration follow-ups (@ts-nocheck additions for incremental
typing).
Verified locally: npm run check, cargo test -p kon-ai-formatting,
cargo test -p kon-storage, cargo test -p kon --lib commands::live::tests,
cargo check — all green.
Scope boundary: kon-llm crate is still a stub; task extraction remains
rule-based. Bundled local-LLM runtime is the next clean step and is not
in this commit.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
72 lines
1.9 KiB
Rust
72 lines
1.9 KiB
Rust
use std::sync::{Arc, Mutex};
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struct LlmState {
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loaded: bool,
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}
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/// Shared handle to the LLM engine. Cheap to clone (Arc).
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/// Phase 3 will replace the stub body with a real llama-cpp-2 model.
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#[derive(Clone)]
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pub struct LlmEngine {
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state: Arc<Mutex<LlmState>>,
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}
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impl LlmEngine {
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pub fn new() -> Self {
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Self {
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state: Arc::new(Mutex::new(LlmState { loaded: false })),
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}
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}
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pub fn is_loaded(&self) -> bool {
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self.state.lock().unwrap().loaded
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}
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/// Break a task description into 3-7 physical micro-steps.
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/// Returns Err if no model is loaded — the caller surfaces this to the UI.
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pub fn decompose_task(&self, _task_text: &str) -> Result<Vec<String>, String> {
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if !self.is_loaded() {
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return Err("Download an AI model in Settings to break down tasks.".to_string());
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}
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// Phase 3: call llama-cpp-2 with GBNF-constrained prompt here.
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Err("LLM not yet wired.".to_string())
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}
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}
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impl Default for LlmEngine {
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fn default() -> Self {
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Self::new()
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn decompose_returns_error_when_not_loaded() {
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let engine = LlmEngine::new();
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assert!(!engine.is_loaded());
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let result = engine.decompose_task("Write a blog post");
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assert!(result.is_err());
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assert!(
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result.unwrap_err().contains("Download an AI model"),
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"error message should tell user to download a model"
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);
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}
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#[test]
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fn default_creates_unloaded_engine() {
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let engine = LlmEngine::default();
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assert!(!engine.is_loaded());
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}
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#[test]
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fn engine_is_clone_and_shares_state() {
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let engine = LlmEngine::new();
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let clone = engine.clone();
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// Both point to the same Arc — neither is loaded
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assert!(!clone.is_loaded());
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}
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}
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