`extract_content_tags_cmd` was the only LLM command in `commands/llm.rs`
that did not call `ensure_main_window`. Every sibling (`load_llm_model`,
`unload_llm_model`, `delete_llm_model`, `test_llm_model`,
`cleanup_transcript_text_cmd`, `download_llm_model`) gates on it.
Without the guard a secondary-window webview could trigger a multi-
second llama.cpp inference run, blocking the LLM engine for the main
window and leaking model-inferred tags out of the History page's trust
boundary.
This change:
- adds a `window: tauri::WebviewWindow` parameter (Tauri injects it
automatically — `HistoryPage.svelte`'s `invoke("extract_content_tags_cmd",
…)` call site is unchanged and `npm run check` is clean);
- calls `ensure_main_window(&window)?` before the engine check so the
rejection is fast and the cap mirrors the rest of the surface.
Behaviour is otherwise identical: same engine path, same spawn_blocking,
same App-Nap power assertion.
The shared `ensure_main_window_label` test in `commands::security`
already covers the secondary-window rejection behaviour; no
command-level scaffolding for handler-style tests exists in this
codebase, so introducing one for a single new line was out of scope.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Add an Option<bool> use_gpu parameter to test_llm_model with the same
default-true semantics as load_llm_model. Hard-coding true triggered an
engine tear-down/rebuild when a parallel load_llm_model(use_gpu=false)
was in flight (LlmEngine::load_model triples on id+path+use_gpu) and
silently flipped the user's GPU mode underneath the test.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Picks up the registry rename in the front-end and Tauri command layer:
- src/lib/types/app.ts: LlmModelIdStr now lists the four new ids
(qwen3_5_2b / qwen3_5_4b / qwen3_5_9b / qwen3_6_27b).
- src/lib/pages/SettingsPage.svelte: LLM_MODELS table rebuilt with
four tiers (Minimal / Standard / High / Maximum), matching subtitles
and download-size copy. selectedLlmModelId fallback, hardware-warning
thresholds, tier-availability check, and ensureRecommendedLlmTier
fallback all retargeted at the new ids. The Maximum tier surfaces a
64 GB / 24 GB warning so users with mid-range hardware see honest
expectations.
- src-tauri/src/commands/llm.rs and commands/tasks.rs: doc-comment
examples refreshed (Qwen3 4B → Qwen3.5 4B, Qwen3's tokenizer →
Qwen's tokenizer — the BPE family is shared).
- src/lib/stores/llmStatus.svelte.ts: chip-detail example updated.
cargo build --workspace clean. cargo test --workspace clean.
npx svelte-check reports one pre-existing error in vite.config.js
(unused @ts-expect-error directive, dates back to the original
scaffold commit 9926a42); not introduced here, out of scope to fix.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Replace all instances of the legacy product names "Kon" and "Corbie" with
"Magnotia" across user-facing copy, code identifiers, package names, bundle
ids, file paths, and documentation. Preserves the unrelated "konsole" (KDE
terminal) reference and the parent CORBEL company name.
- Renames 10 Rust crates (kon-* → magnotia-*) and the tauri binary
- Updates package.json, tauri.conf.json (productName + identifier)
- Renames CSS classes (kon-rh-* → magnotia-rh-*) and animations
- Renames brand and roadmap docs
- Regenerates Cargo.lock and package-lock.json
Verified: svelte-check passes; pure-rust crates compile under new names.
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>
