Phase 2 of the rebrand cascade. Renames all 9 workspace crates from magnotia-* to lumotia-* plus the src-tauri binary crate name: - magnotia-ai-formatting -> lumotia-ai-formatting - magnotia-audio -> lumotia-audio - magnotia-cloud-providers -> lumotia-cloud-providers - magnotia-core -> lumotia-core - magnotia-hotkey -> lumotia-hotkey - magnotia-llm -> lumotia-llm - magnotia-mcp -> lumotia-mcp - magnotia-storage -> lumotia-storage - magnotia-transcription -> lumotia-transcription - magnotia -> lumotia (src-tauri binary) - magnotia_lib -> lumotia_lib (src-tauri lib target) Crate directories (crates/audio/ etc.) stay as-is; only the Cargo.toml [package] name field changes plus all consumer module imports (magnotia_core -> lumotia_core, etc.). Remaining magnotia_* references at this point are intentional and scoped to later phases: tracing targets (Phase 4), DB setting keys magnotia_preferences/magnotia_history (Phase 5). cargo build --workspace passes. cargo test --workspace: 330 pass, 0 fail. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
174 lines
5.0 KiB
Rust
174 lines
5.0 KiB
Rust
// Tauri commands wrapping lumotia_storage profile + profile_term CRUD.
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// Pattern mirrors tasks.rs — flat imports from `lumotia_storage` with a `db_`
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// alias prefix to avoid name collisions with the command functions, plain
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// snake_case parameters (Tauri 2.x auto-converts camelCase JS keys),
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// `.map_err(|e| e.to_string())` for error conversion, and camelCase DTOs
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// for the frontend-facing shape (the storage row types don't derive
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// Serialize — same reason TaskRow → TaskDto exists).
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//
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// The Default profile is guarded at the storage layer (SQLite triggers +
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// Rust pre-checks), so no extra guarding is needed here.
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use serde::Serialize;
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use lumotia_ai_formatting::extract_corrections;
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use lumotia_storage::{
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add_profile_term as db_add_profile_term, create_profile as db_create_profile,
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delete_profile as db_delete_profile, delete_profile_term as db_delete_profile_term,
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list_profile_terms as db_list_profile_terms, list_profiles as db_list_profiles,
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update_profile as db_update_profile, ProfileRow, ProfileTermRow,
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};
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use crate::AppState;
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const AUTO_LEARNED_NOTE: &str = "Auto-learned from transcript edit";
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/// Frontend-facing profile shape. Matches the object the Svelte profile
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/// picker + editor will consume.
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#[derive(Debug, Clone, Serialize)]
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#[serde(rename_all = "camelCase")]
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pub struct ProfileDto {
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pub id: String,
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pub name: String,
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pub initial_prompt: String,
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pub created_at: String,
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}
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impl From<ProfileRow> for ProfileDto {
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fn from(r: ProfileRow) -> Self {
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Self {
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id: r.id,
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name: r.name,
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initial_prompt: r.initial_prompt,
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created_at: r.created_at,
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}
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}
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}
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/// Frontend-facing profile term (dictionary-style vocabulary hint).
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#[derive(Debug, Clone, Serialize)]
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#[serde(rename_all = "camelCase")]
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pub struct ProfileTermDto {
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pub id: String,
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pub profile_id: String,
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pub term: String,
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pub note: String,
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pub created_at: String,
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}
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impl From<ProfileTermRow> for ProfileTermDto {
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fn from(r: ProfileTermRow) -> Self {
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Self {
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id: r.id,
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profile_id: r.profile_id,
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term: r.term,
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note: r.note,
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created_at: r.created_at,
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}
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}
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}
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#[tauri::command]
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pub async fn list_profiles_cmd(
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state: tauri::State<'_, AppState>,
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) -> Result<Vec<ProfileDto>, String> {
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db_list_profiles(&state.db)
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.await
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.map(|rows| rows.into_iter().map(ProfileDto::from).collect())
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.map_err(|e| e.to_string())
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}
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#[tauri::command]
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pub async fn create_profile_cmd(
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state: tauri::State<'_, AppState>,
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name: String,
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initial_prompt: String,
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) -> Result<ProfileDto, String> {
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db_create_profile(&state.db, &name, &initial_prompt)
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.await
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.map(ProfileDto::from)
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.map_err(|e| e.to_string())
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}
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#[tauri::command]
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pub async fn update_profile_cmd(
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state: tauri::State<'_, AppState>,
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id: String,
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name: String,
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initial_prompt: String,
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) -> Result<(), String> {
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db_update_profile(&state.db, &id, &name, &initial_prompt)
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.await
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.map_err(|e| e.to_string())
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}
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#[tauri::command]
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pub async fn delete_profile_cmd(
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state: tauri::State<'_, AppState>,
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id: String,
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) -> Result<(), String> {
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db_delete_profile(&state.db, &id)
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.await
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.map_err(|e| e.to_string())
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}
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#[tauri::command]
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pub async fn list_profile_terms_cmd(
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state: tauri::State<'_, AppState>,
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profile_id: String,
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) -> Result<Vec<ProfileTermDto>, String> {
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db_list_profile_terms(&state.db, &profile_id)
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.await
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.map(|rows| rows.into_iter().map(ProfileTermDto::from).collect())
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.map_err(|e| e.to_string())
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}
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#[tauri::command]
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pub async fn add_profile_term_cmd(
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state: tauri::State<'_, AppState>,
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profile_id: String,
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term: String,
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note: String,
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) -> Result<ProfileTermDto, String> {
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db_add_profile_term(&state.db, &profile_id, &term, ¬e)
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.await
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.map(ProfileTermDto::from)
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.map_err(|e| e.to_string())
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}
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#[tauri::command]
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pub async fn learn_profile_terms_from_edit_cmd(
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state: tauri::State<'_, AppState>,
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profile_id: String,
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original_text: String,
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edited_text: String,
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) -> Result<Vec<ProfileTermDto>, String> {
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let existing_terms: Vec<String> = db_list_profile_terms(&state.db, &profile_id)
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.await
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.map_err(|e| e.to_string())?
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.into_iter()
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.map(|row| row.term)
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.collect();
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let corrections = extract_corrections(&original_text, &edited_text, &existing_terms);
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let mut learned = Vec::new();
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for term in corrections {
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let row = db_add_profile_term(&state.db, &profile_id, &term, AUTO_LEARNED_NOTE)
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.await
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.map_err(|e| e.to_string())?;
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learned.push(ProfileTermDto::from(row));
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}
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Ok(learned)
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}
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#[tauri::command]
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pub async fn delete_profile_term_cmd(
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state: tauri::State<'_, AppState>,
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id: String,
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) -> Result<(), String> {
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db_delete_profile_term(&state.db, &id)
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.await
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.map_err(|e| e.to_string())
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}
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