Bridge LlmEngine::generate_title across the Tauri boundary. Same shape
as the existing extract_content_tags_cmd — `is_loaded` short-circuit so
the frontend can detect the "no model" case without firing a synchronous
inference, then spawn_blocking + PowerAssertion guard for the heavy work.
- src-tauri/src/commands/llm.rs: new generate_title_cmd that wraps
`state.llm_engine.generate_title(transcript)`. Returns
Result<String, String> — the engine's own InvalidJson / Inference /
PromptTooLong errors are stringified at the boundary, same pattern
as extract_content_tags_cmd at line 408.
- src-tauri/src/lib.rs: register the command in invoke_handler!,
immediately after extract_content_tags_cmd in the LLM block.
Verified: `cargo check -p kon` passes after a `rm -rf target/.../tauri-*`
to clear stale OUT_DIR paths from the project's pre-rename location
(transcription-app used to live at ~/CORBEL-Projects/kon/). No code
behaviour change from that cleanup — Cargo just needed the cache rebuilt.
The frontend wiring follows in the next commit.
Bridges LlmEngine::extract_content_tags to the frontend with the same
spawn_blocking + PowerAssertion guard the cleanup_text command uses.
Returns a ContentTags object serialised to camelCase JSON. Errors
surface as readable strings so the frontend toast shows actionable
text on the rare grammar-bypass path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two new Settings → AI knobs that compose cleanly with what already
shipped (aiTier, LLM model, translator prompt framing).
**B.1 #15 — Named cleanup presets.** LlmPromptPreset enum
(Default / Email / Notes / Code) appends a short context hint onto
the CLEANUP_PROMPT just before generation. Presets shape tone and
structure ("email paragraph", "bulleted meeting notes", "preserve
technical terms") without licensing the content-editing the
translator-not-editor framing forbids. cleanup_transcript_text_cmd
now takes `preset: Option<String>` which runs through the new
LlmPromptPreset::parse (normalises aliases like "meeting-notes",
collapses unknown values to Default).
**A.1 #28 — Sequential-GPU guard.** New LocalEngine::unload drops
the backend + model_id so a subsequent load actually reclaims VRAM.
load_llm_model, load_model, and load_parakeet_model Tauri commands
grow an optional `concurrent: bool` argument. When concurrent is
Some(false), loading LLM first unloads whisper+parakeet, and vice
versa — prevents VRAM OOM on tight-VRAM setups. Default is the
previous parallel behaviour so nothing changes for multi-GB cards.
Transcribe-in-progress paths (transcribe_pcm, transcribe_file, live)
pass None, so mid-dictation model loads don't accidentally tear
down the LLM.
Settings UI (AI section):
- Cleanup preset segmented button + descriptive copy for each option.
- GPU concurrency segmented button with explicit trade-off text
("faster transitions vs fits in tight VRAM").
Frontend wiring:
- settings.llmPromptPreset flows from DictationPage's
cleanupTranscriptIfEnabled into the Tauri command.
- settings.aiGpuConcurrency flows from both DictationPage (auto-load
on record) and SettingsPage (manual load/unload buttons) as
`concurrent: "parallel" === true` to the load commands.
Tests: three new preset cases in crates/ai-formatting/src/llm_client.rs
(parse aliases, suffix non-empty for non-default, default suffix
empty). All 139 existing lib tests still pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The brief's pain point is opaque load failures: llama-cpp-2's errors
bubble up as raw C++ strings ("cudaMalloc failed: out of memory",
"invalid gguf magic"). A user seeing that has no path to recovery.
New backend command test_llm_model runs a staged diagnostic:
1. Model not downloaded → `not-downloaded` + download hint.
2. File size ≤90% of expected → `incomplete` (stalled download)
+ re-download hint. Matters because llama-cpp-2 can segfault
on truncated GGUF rather than returning cleanly.
3. Requested model already loaded → `ready`, no side effects.
4. Otherwise attempt a real load. On failure, classify_llm_load_error
maps the raw string to one of:
- load-failed-vram (OOM / cudaMalloc / allocation)
- load-failed-corrupt (GGUF magic / unsupported format)
- load-failed-permission (permission denied / access denied)
- load-failed-other (catch-all)
Each category has a prewritten actionable hint pointing at the
specific Settings surface (tier picker, re-download, file perms).
classify_llm_load_error is pure-string and unit-tested — 8 cases
covering the main categories plus edge cases (OOM alias, Windows
"Access is denied", unknown errors). Ordered narrow-to-broad so
overlap doesn't misclassify.
Settings UI gets a "Test" button in the AI section's action row,
visible whenever the model is downloaded (both downloaded-idle and
loaded states). Shows inline hint below the status line when the
test surfaces one. Refreshes both local and global LLM status after
the test since a successful test implicitly loads the model.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds src-tauri/src/commands/power.rs exposing a PowerAssertion RAII
guard that macOS uses to pin NSProcessInfo.beginActivityWithOptions
around long-running work. Wired into:
- run_live_session (entire live-dictation lifetime)
- cleanup_transcript_text_cmd's spawn_blocking body (LLM run)
Non-macOS targets get a no-op guard so callers don't have to #cfg
the call sites. The actual Objective-C bridge to NSProcessInfo is
stubbed (begin_activity returns Err so the guard logs a warning
instead of silently pretending); the stub doesn't regress recording
or LLM behaviour on macOS — it just means App Nap is not yet
suppressed, which matches today's behaviour. Full objc2 integration
is a follow-up that can introduce objc2 cleanly in its own commit.
Matches Whispering #549/#559 pain-pattern; acceptance text ("10
minute background recording completes unattended") is satisfied
once the bridge is finished, and nothing regresses today.
Co-authored-by: jars <jakejars@users.noreply.github.com>
kon-llm now owns a real LlamaBackend + LlamaModel, with three Qwen3 tiers
(1.7B Q4, 4B-Instruct-2507 Q4, 14B Q5) selectable per hardware. Downloads
are resumable with SHA-256 verification and stored under ~/.kon/models/llm.
Engine exposes three high-level surfaces — all greedy/temp-0, GBNF-constrained
where output shape matters:
- cleanup_text (prompt-injection-hardened system prompt; profile terms
appended as "preserve these spellings" suffix)
- decompose_task (3–7 micro-steps, constrained JSON array)
- extract_tasks (optional-array; empty when no explicit commitments)
post_process_segments now takes an Option<&LlmEngine> and, when loaded and
format_mode != Raw, joins segments → cleanup → replaces segments with the
cleaned text (first segment span). Rule-based path still runs first; LLM
errors log and keep rule-based output.
Tauri commands: recommend_llm_tier, check_llm_model, download_llm_model,
load_llm_model, unload_llm_model, delete_llm_model, get_llm_status,
cleanup_transcript_text_cmd, extract_tasks_from_transcript_cmd,
decompose_and_store (LLM-backed subtasks).
Settings: AI tier toggle (off / cleanup / tasks), model picker with
downloaded/loaded status, download progress events via
kon:llm-download-progress.
Dictation: ensureLlmModelLoaded on mount, cleanupTranscriptIfEnabled after
stop when tier != off and format_mode != Raw, LLM task extraction when
tier=tasks (regex fallback on failure).
Interim: both llama-cpp-sys-2 and whisper-rs-sys statically link their own
ggml, so src-tauri/build.rs emits -Wl,--allow-multiple-definition on Linux.
Replace with a system-ggml shared-lib setup as a follow-up.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
