Files
Lumotia/docs/architecture-map/02-tauri-runtime/commands/feedback.md
jars a1f3f3f134 docs: architecture map (initial 5-slice generation, 105 pages)
Five-slice navigable map of the entire codebase under
docs/architecture-map/. Each slice is a self-contained
breadcrumbed sub-tree:

  01-frontend (16)              Svelte/SvelteKit UI
  02-tauri-runtime (26)         src-tauri commands + lifecycle
  03-audio-transcription (16)   audio + transcription crates
  04-llm-formatting-mcp (19)    llm, ai-formatting, mcp, cloud
  05-core-storage-hotkey-build  core, storage, hotkey, workspace,
                          (26) CI, dev glue

Plus master README.md and data-flow-end-to-end.md tracing
audio bytes from microphone to FTS5 search to MCP read.

Generated by 5 parallel subagents on 2026/05/09 against
HEAD 3c47000. Each page has YAML frontmatter, file:line code
refs, sibling cross-links, plain-English summaries.

Aggregated debt surfaced (full lists in master README):
RB-08 macOS power assertion, schema head drift v14 vs v15,
VAD blocked on ort version conflict, streaming primitives
not wired into live.rs, no prompt versioning, MCP has no
auth, cloud-providers in-memory keystore, SettingsPage
2 484 LOC, commands/live.rs 1 737 LOC, dual theme system,
brand rename to Lumenote pending across the codebase.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-09 14:04:13 +01:00

4.3 KiB

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name type slice last_verified
HITL feedback architecture-map-page 02-tauri-runtime 2026/05/09

commands::feedback

Where you are: Architecture mapTauri runtimeCommands → Feedback

Plain English summary. Phase 2: thumbs + correction capture on AI-generated output (microsteps from a task decomposition, task lines extracted from a transcript, or LLM cleanup). The captured rows feed a few-shot loop: subsequent prompts are conditioned on the user's preferred style by injecting the (input, preferred-output) pairs as exemplars.

At a glance

  • Path: src-tauri/src/commands/feedback.rs.
  • LOC: 110.
  • Tauri commands exposed:
    • record_feedback(state, input: RecordFeedbackInput) -> Result<i64, String> — returns the new row id.
    • list_feedback_examples_cmd(state, target_type, limit, min_rating, profile_id) -> Result<Vec<FeedbackDto>, String>.
  • Events emitted: none.
  • Depends on: magnotia_storage::{record_feedback, list_feedback_examples, FeedbackRow, FeedbackTargetType, RecordFeedbackParams}.
  • Called from frontend at: dictation result panel (thumb up/down + correction-text on cleanup); Tasks page (thumb on extracted tasks and decomposed microsteps).

What's in here

RecordFeedbackInput (src-tauri/src/commands/feedback.rs:15)

Frontend-supplied shape:

  • targetType: "microstep" | "task_extraction" | "cleanup". Parsed via FeedbackTargetType::parse.
  • targetId: optional surface-specific id (subtask id, task id, transcript id).
  • rating: -1 (thumbs down), 0 (correction, neutral), +1 (thumbs up).
  • originalText: the AI-generated text the user is rating.
  • correctedText: the user's preferred text (when they corrected it).
  • contextJson: freeform JSON used by the prompt builder later to reconstruct the (input, preferred-output) pair.
  • profileId: scopes the row.

FeedbackDto (:38)

camelCase mirror of FeedbackRow. Note rating widens to i64 in the DTO (storage uses i64).

parse_target_type (:68)

Wraps FeedbackTargetType::parse(raw), returning "unknown feedback target_type: <raw>" on miss.

record_feedback (:73)

parse_target_type then db_record_feedback. Returns the row id.

list_feedback_examples_cmd (:95)

Clamps limit to [1, 64], defaults 8. Clamps min_rating to [-1, 1], default 0. Calls db_list_feedback_examples. Returns FeedbackDtos. Used by the commands::tasks few-shot exemplar pull and would be used by the equivalent in commands::llm if/when cleanup gets its own exemplar path.

Data flow

dictation result thumbs-up -> invoke('record_feedback', { targetType: 'cleanup', rating: +1, originalText, correctedText, contextJson, profileId })
                            -> magnotia_storage::record_feedback -> row id

decomposition thumbs-down + correction -> record_feedback({ targetType: 'microstep', rating: 0, originalText, correctedText: "user's preferred wording", contextJson: {parent_text}, profileId })

next decompose call -> list_feedback_examples_cmd('microstep', 5, 0, profile_id)
                    -> [FeedbackDto, ...] -> few-shot exemplars

Watch-outs

  • No ensure_main_window guard. Tasks float and History viewer secondary windows can also fire feedback. Intentional. If you ever want to lock down feedback writes, this is where to add the guard.
  • contextJson is freeform. Storage stores the raw string. commands::tasks::to_llm_examples parses it and skips rows that are malformed. Bad data therefore degrades gracefully but doesn't surface to the user. The eprintln! in to_llm_examples is the only visibility.
  • min_rating clamp is [-1, 1]. Pass 1 to get only thumbs-up examples, 0 for thumbs-up + corrections, -1 for everything. The default of 0 is what commands::tasks picks.
  • No deduplication. A user thumbs-upping the same output twice creates two rows. The exemplar trim in commands::tasks does not dedupe by originalText. If two identical exemplars steal slots, that's just lost prompt budget.

See also

  • Tasks — the consumer of list_feedback_examples_cmd.
  • LLM — the cleanup path that produces the text that thumbs-up/down feedback rates.
  • ProfilesprofileId is the scoping key.