feat(onboarding): implement first-run user onboarding wizard for profile setup
- Added a new onboarding wizard that runs on the first app launch post-login. - Collects user personalization data (job role, industry, primary use case, tone preference, language) and stores it in encrypted core memory. - Auto-detects and saves formatting preferences (timezone, time format, date format) in local electron-store. - Normalizes user free-text inputs via a backend LLM call before persisting. - Introduced new backend routes for memory updates and normalization. - Updated frontend components to support the onboarding flow with a chat-bubble aesthetic. - Added settings section for profile editing and re-running the onboarding process. - Ensured that the onboarding process is skippable and editable in the settings. - Implemented verification steps to ensure proper functionality and data handling. chore: update submodules for waitlist and website
This commit is contained in:
Roberto Musso
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docs/plan-onboarding-wizard.md
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# First-Run User Onboarding (Profile → Core Memory + Format Prefs)
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## Context
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Today, after sign-up or login, users land directly on the home chat with no introduction. Signup only collects `name`, `surname`, `email`. The backend AI agents have no idea who they're talking to — generic answers, generic tone, no language match, raw timestamps.
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This change adds a one-time wizard that runs the **first time a user opens the app post-login**. It:
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1. Seeds the user's **core memory** (encrypted, server-side) with personalization data the AI should reason about (`job_role`, `industry`, `primary_use_case`, `tone_preference`, `language`).
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2. Auto-detects and stores **formatting preferences** (`timezone`, `time_format`, `date_format`) **on the FE** as electron-store settings — *not* in core memory, because the LLM should never see raw timestamps or have to reason about format strings. Instead, the FE applies these to tool-result rows before they're sent back to the backend.
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3. Optionally normalizes user free-text answers via a single backend LLM call before persisting, so messy inputs like "i build websites" become clean values like "Web Developer".
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Because [memory_middleware.py:53-94](api/app/core/memory_middleware.py#L53-L94) already auto-injects `core_memory` into every orchestrator call (see [device_ws.py:213](api/app/api/routes/device_ws.py#L213) and [device_ws.py:282](api/app/api/routes/device_ws.py#L282)), no system-prompt code changes — writing to `MemoryCore` is enough for agents to "see" the data on their next call.
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**Decisions made with the user:**
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- **UI style**: hybrid chat-styled wizard (looks like AIChatPanel — bubbles, chips — but pre-scripted, no LLM calls per step)
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- **Storage split**: AI-relevant fields in encrypted `MemoryCore`; formatting prefs in FE-local electron-store
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- **Skippable + editable** in a new Settings → Profile section
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- **OS-derived defaults**: language, timezone, time format, date format auto-detected from the OS. Language is shown in the wizard for confirmation; the three formatting prefs are seeded silently and editable in Settings.
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- **Avatar**: comes from Google OAuth (already supported via `users.avatar_url`). Not in this wizard. A manual upload control in Settings → Profile is a nice-to-have but **out of scope** for this change.
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- **LLM normalization**: yes, but only on free-text answers, in **one batch call at the final 'Done' step**. User sees a "Here's what I saved" review screen and can edit before persisting.
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## Fields collected
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| Key | Lives in | Source | In wizard? | Editable in Settings? | Used by |
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|---------------------|---------------------------|------------------------------|------------------------|----------------------|------------|
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| `job_role` | `MemoryCore` (BE, encrypted) | User (chip + free text) | Yes | Yes | LLM |
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| `industry` | `MemoryCore` | User (chip + free text) | Yes | Yes | LLM |
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| `primary_use_case` | `MemoryCore` | User (chip) | Yes | Yes | LLM |
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| `tone_preference` | `MemoryCore` | User (chip) | Yes | Yes | LLM |
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| `language` | `MemoryCore` | OS `app.getLocale()` → user confirms | Yes — confirm/change | Yes | LLM (response language) + future UI i18n |
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| `timezone` | electron-store (FE) | `Intl.DateTimeFormat().resolvedOptions().timeZone` | No — silent | Yes | FE formatter |
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| `time_format` | electron-store (FE) | Derived from locale (12h/24h)| No — silent | Yes | FE formatter |
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| `date_format` | electron-store (FE) | Derived from locale (e.g. dd/MM/yyyy) | No — silent | Yes | FE formatter |
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The split is the load-bearing decision: **the LLM never sees raw timestamps or format prefs**. Instead, the FE's drizzle executor formats every timestamp column in tool-result rows using the user's preferences before sending the `tool_result` frame back to the backend.
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---
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## Architecture notes
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1. **AI orchestration is fully delegated to the backend** via WebSocket — see [orchestrator.ts:87-117](adiuvAI/src/main/ai/orchestrator.ts#L87-L117). The Electron client never builds a system prompt. So all LLM-relevant personalization must live on the backend (in `MemoryCore`).
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2. **Tool calls are FE-executed**: backend sends `WsToolCall` → FE [drizzle-executor.ts](adiuvAI/src/main/api/drizzle-executor.ts) runs the SELECT and returns `{ rows }` → FE [backend-client.ts:652-658](adiuvAI/src/main/api/backend-client.ts#L652-L658) wraps it as a `tool_result` frame. The formatting hook goes **between the executor returning rows and the frame being sent** — this is where raw `dueDate` numbers become `"15/04/2026 14:30"` strings.
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3. **Format prefs are per-device**: `timezone` is inherently per-device (your laptop and phone may be in different cities). For consistency we keep all three format prefs FE-local. If the user wants cross-device sync later, this can migrate to `MemoryCore` without breaking the wire format — but that's not v1.
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---
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## Files to change
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### Backend (`api/`)
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1. **`api/alembic/versions/<new>_add_onboarded_flag.py`** — new Alembic migration:
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- `ALTER TABLE users ADD COLUMN onboarding_completed_at TIMESTAMPTZ NULL`
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The five LLM-relevant values live in the existing `memory_core` table — no new columns.
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2. **[api/app/models.py:63-94](api/app/models.py#L63-L94)** — add `onboarding_completed_at: Mapped[datetime | None]` to `User`.
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3. **[api/app/schemas.py:27-33](api/app/schemas.py#L27-L33)** — extend `UserProfile`:
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```python
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class UserProfile(BaseModel):
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id: str
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email: str
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name: str | None = None
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surname: str | None = None
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tier: BillingTier
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avatar_url: str | None = None
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onboarding_completed_at: int | None = None # epoch ms
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memory: dict[str, str] = Field(default_factory=dict)
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```
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4. **[api/app/api/middleware/auth.py:74-79](api/app/api/middleware/auth.py#L74-L79)** — extend `get_current_user`:
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- Read `onboarding_completed_at` from the user row.
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- Use `MemoryMiddleware(db).list_core_blocks(user_id)` to load decrypted core blocks → `{label: value}` dict, attach as `memory`.
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5. **[api/app/api/routes/auth.py](api/app/api/routes/auth.py)** — add a new route. Do not extend `_UpdateProfileRequest` (keep name/surname separate).
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```python
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class _UpdateMemoryRequest(BaseModel):
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memory: dict[str, str] = Field(default_factory=dict)
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mark_onboarded: bool = False
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@router.put("/me/memory", response_model=UserProfile)
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async def update_memory(
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body: _UpdateMemoryRequest,
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current_user: UserProfile = Depends(get_current_user),
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db: AsyncSession = Depends(get_session),
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) -> UserProfile:
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memory = MemoryMiddleware(db)
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for key, value in body.memory.items():
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await memory.update_core(current_user.id, key, value)
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if body.mark_onboarded:
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result = await db.execute(select(User).where(User.id == current_user.id))
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user = result.scalar_one()
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user.onboarding_completed_at = datetime.now(timezone.utc)
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await db.commit()
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# Re-fetch via get_current_user-style logic and return UserProfile.
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```
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6. **`api/app/api/routes/auth.py`** — new normalization route:
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```python
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class _NormalizeRequest(BaseModel):
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inputs: dict[str, str] # e.g. {"job_role": "i build websites"}
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class _NormalizeResponse(BaseModel):
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normalized: dict[str, str]
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@router.post("/onboarding/normalize", response_model=_NormalizeResponse)
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async def normalize_onboarding(
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body: _NormalizeRequest,
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current_user: UserProfile = Depends(get_current_user),
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) -> _NormalizeResponse:
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"""One-shot LLM normalization for free-text onboarding answers."""
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```
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Implementation: build a small system prompt ("You normalize user onboarding answers. Return JSON only. Each key maps to a clean, ≤3-word canonical label."), call `get_llm("gpt-4o-mini", temperature=0)` from [api/app/core/llm.py](api/app/core/llm.py) with `response_format={"type": "json_object"}`, parse, return. Must short-circuit and return the inputs unchanged on any LLM error so the wizard never blocks on a flaky model call. Rate-limited by the existing `TierRateLimiter` middleware.
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7. **No orchestrator / prompt changes needed.** `MemoryMiddleware.enrich_context()` already injects `core_memory` into every chat call. **This is the whole point of using `MemoryCore` instead of system-prompt injection.**
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### Electron main (`adiuvAI/src/main/`)
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8. **[src/shared/api-types.ts:26-34](adiuvAI/src/shared/api-types.ts#L26-L34)** — extend `UserProfileSchema` with `onboardingCompletedAt: z.number().int().nullable().optional()` and `memory: z.record(z.string(), z.string()).default({})`.
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9. **[src/main/store.ts:23-38](adiuvAI/src/main/store.ts#L23-L38)** — add a `formatPrefs` block to `AppSettings`:
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```ts
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formatPrefs: {
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timezone: string; // 'Europe/Rome'
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dateFormat: string; // 'dd/MM/yyyy' | 'MM/dd/yyyy' | 'yyyy-MM-dd'
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timeFormat: '12h' | '24h';
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} | null; // null = not yet seeded
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```
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Default to `null`. Add helpers `getFormatPrefs()` and `setFormatPrefs(prefs)`.
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10. **New: `src/main/auth/locale-defaults.ts`** — small helper:
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```ts
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export function detectFormatPrefs(): FormatPrefs {
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const locale = app.getLocale(); // 'it-IT'
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const timezone = Intl.DateTimeFormat().resolvedOptions().timeZone;
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const timeFormat = Intl.DateTimeFormat(locale, { hour: 'numeric' }).resolvedOptions().hour12 ? '12h' : '24h';
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const dateFormat = inferDateFormatFromLocale(locale); // small lookup: 'en-US'→MM/dd/yyyy, 'en-GB'/'it-IT'/...→dd/MM/yyyy, 'ja-JP'/...→yyyy-MM-dd
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return { timezone, timeFormat, dateFormat };
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}
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export function detectLanguage(): string { return app.getLocale(); } // 'it-IT'
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```
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11. **New: `src/main/api/format-row.ts`** — pure function called by the executor:
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```ts
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const TIMESTAMP_COLUMNS = new Set([
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'createdAt', 'updatedAt', 'dueDate', 'date', 'endDate',
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'lastRunAt', 'startedAt', 'completedAt',
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]);
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export function formatRow<T extends Record<string, unknown>>(row: T, prefs: FormatPrefs): T;
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export function formatRows<T extends Record<string, unknown>>(rows: T[], prefs: FormatPrefs): T[];
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```
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For each known timestamp column whose value is a `number`, replace it with `formatInstant(value, prefs)` where `formatInstant` uses `Intl.DateTimeFormat(locale, { timeZone: prefs.timezone, hour12: prefs.timeFormat === '12h', ... })` and the `dateFormat` setting. Returns a new object — does not mutate.
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The set of timestamp columns is hard-coded against the Drizzle schema; if a new timestamp column is added, this set must be updated. (Acceptable for v1 — the schema is small. If it grows, we can derive the set from the Drizzle schema's `integer('...', { mode: 'number' })` columns at startup.)
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12. **[src/main/api/drizzle-executor.ts:204-263](adiuvAI/src/main/api/drizzle-executor.ts#L204-L263)** — wrap the executor's `select`/`get`/`insert`/`update` return paths so that `rows`/`row` get passed through `formatRow(s)(..., getFormatPrefs() ?? detectFormatPrefs())`. The `?? detect…` fallback handles the edge case where the executor runs before the first auth.status seed call (e.g. background tool calls during login).
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13. **[src/main/auth/auth-manager.ts:170-174](adiuvAI/src/main/auth/auth-manager.ts#L170-L174)** — add two methods:
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```ts
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async updateMemory(memory: Record<string, string>, markOnboarded = false): Promise<UserProfile>
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async normalizeOnboarding(inputs: Record<string, string>): Promise<Record<string, string>>
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```
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Both call the new backend routes via the existing `put`/`post` helpers.
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14. **[src/main/router/index.ts:1059-1098](adiuvAI/src/main/router/index.ts#L1059-L1098)** — extend `authRouter`:
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- Add `auth.updateMemory` mutation: input `{ memory, markOnboarded? }`.
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- Add `auth.normalizeOnboarding` mutation: input `{ inputs: Record<string, string> }`.
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- **Extend `auth.status`** so that immediately after fetching the profile, if `getFormatPrefs()` is `null`, it calls `setFormatPrefs(detectFormatPrefs())` (silent FE seed). If `profile.memory.language` is missing, it also calls `authManager.updateMemory({ language: detectLanguage() })` (silent BE seed). Both run only on first launch — subsequent calls find the values present and short-circuit.
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### Electron renderer (`adiuvAI/src/renderer/`)
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15. **[src/renderer/components/layout/AppShell.tsx:79-119](adiuvAI/src/renderer/components/layout/AppShell.tsx#L79-L119)** — add the first-run gate. After the `authStatusQuery.data?.authenticated === false` branch:
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```tsx
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if (authStatusQuery.data?.profile && authStatusQuery.data.profile.onboardingCompletedAt == null) {
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return <OnboardingFlow profile={authStatusQuery.data.profile} />;
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}
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```
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16. **New: `src/renderer/components/onboarding/OnboardingFlow.tsx`** — the wizard. Internal state machine:
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```ts
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type Step = 'welcome' | 'jobRole' | 'industry' | 'useCase' | 'tone' | 'language' | 'reviewing' | 'done';
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```
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Renders chat-bubble layout matching [AIChatPanel.tsx](adiuvAI/src/renderer/components/ai/AIChatPanel.tsx) — Sparkles icon, `rounded-2xl`, glassmorphism, spring transitions per the design context in `adiuvAI/.claude/CLAUDE.md`. Each step shows an "AI" bubble with the question, 3–6 chip presets, an optional "type your own" input, and a Skip link.
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The `language` step pre-selects the value already in `profile.memory.language` (auto-seeded). User confirms or picks a different one.
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**`reviewing` step** (the LLM normalization gate):
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- On entry, partition the user's answers into two groups:
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- **Chip selections** — already canonical, skip the LLM entirely.
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- **Free-text answers** — bundle into a `{key: rawText}` map.
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- If the free-text map is non-empty, call `trpc.auth.normalizeOnboarding.useMutation` with it. Show a small inline loader on those fields only ("Tidying up…", ~1-2s).
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**Review screen UX** — single card titled "Here's what I'll save", listing all five fields as rows:
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| Row appearance | When |
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|---------------------------------|---------------------------------------------------|
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| Read-only label + value | Chip-selected values (`use_case`, `tone`, etc.) — checkmark icon |
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| Read-only label + value + small grey hint `auto-tidied from "i build websites"` | Free-text values that the LLM normalized |
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| Read-only label + value | Free-text values that the LLM did NOT change |
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Each row has a small **Edit** pencil icon on the right. Clicking it converts that row in-place into a text input (or a Select for chip-based fields like `tone`/`use_case`, populated with the same chip presets plus a free-text "Other" option). The user types the new value, presses Enter or clicks Save → the row goes back to read-only with the **new value as-typed**.
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**Edited values are stored verbatim — no re-normalization.** Rationale: the LLM normalization exists to clean up the *initial* messy answer; once the user has seen the suggestion and chosen to override it, re-running the LLM would either no-op (their text is already clean) or fight them. The user is the final arbiter. The "auto-tidied from…" hint disappears once a row is edited (the new value is no longer LLM-derived).
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Bottom of the card: a single primary **"Looks good — save"** button → calls `trpc.auth.updateMemory.useMutation` with the final map + `markOnboarded: true` → `utils.auth.status.invalidate()` → AppShell remounts into the normal app. A secondary **"Back to wizard"** link drops the user back to the first wizard step (`jobRole`) with all current values pre-filled — used when the review reveals the answers are wrong enough that re-running the wizard is faster than five inline edits.
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**Failure modes**:
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- Normalization HTTP call fails → review screen shows raw values with a small banner "Couldn't auto-tidy — review and save". Save still works.
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- User clicks "Looks good — save" and `updateMemory` fails → toast error, user stays on the review screen, can retry.
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**Skip behaviour**: clicking Skip on any step calls `updateMemory({}, markOnboarded: true)` — empty map, just the flag. We don't re-prompt next launch.
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17. **New: `src/renderer/components/onboarding/onboardingOptions.ts`** — preset chip lists:
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```ts
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export const JOB_ROLES = ['Developer', 'Designer', 'Consultant', 'Founder', 'Project Manager'];
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export const INDUSTRIES = ['Tech', 'Design', 'Consulting', 'Legal', 'Marketing', 'Education'];
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export const USE_CASES = ['Solo freelancer', 'Client manager', 'Team lead', 'Personal productivity'];
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export const TONES = ['Casual', 'Formal', 'Concise', 'Detailed'];
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```
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18. **[src/renderer/components/settings/types.ts:3-9](adiuvAI/src/renderer/components/settings/types.ts#L3-L9)** — add `'profile'` to `SectionId` and `{ id: 'profile', label: 'Profile' }` to `SECTIONS` (before `'account'`).
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19. **New: `src/renderer/components/settings/ProfileSection.tsx`** — Settings → Profile editor. Plain form (no chat aesthetic in Settings). Two cards:
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- **"About you"** (writes to `MemoryCore` via `auth.updateMemory`): job_role, industry, primary_use_case, tone_preference, language. "Re-run onboarding" button → small backend route `POST /auth/onboarding/reset` (or just an extension of `update_memory` with `clear_onboarded: true`) that nulls `users.onboarding_completed_at`, then `auth.status.invalidate()` remounts the wizard.
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- **"Display preferences"** (writes to electron-store via a new `trpc.settings.setFormatPrefs` mutation): timezone (select populated from `Intl.supportedValuesOf('timeZone')`), date_format (select: dd/MM/yyyy, MM/dd/yyyy, yyyy-MM-dd), time_format (radio: 12h / 24h).
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20. **[src/main/router/index.ts](adiuvAI/src/main/router/index.ts)** — `settingsRouter`: add `getFormatPrefs` query and `setFormatPrefs` mutation that read/write to electron-store via `getFormatPrefs()` / `setFormatPrefs()`.
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21. **[src/renderer/routes/settings.tsx:55-58](adiuvAI/src/renderer/routes/settings.tsx#L55-L58)** — add `{section === 'profile' && <ProfileSection />}`.
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---
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## Patterns to reuse (do not duplicate)
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- **Stepper state**: `InlineAgentCreationStepper` — `useState<...>` plus conditional rendering.
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- **Chat bubble aesthetic**: copy bubble + Sparkles + glass styling from [AIChatPanel.tsx](adiuvAI/src/renderer/components/ai/AIChatPanel.tsx). Do **not** invent a new chat shell.
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- **Form components**: shadcn `Field`/`Input`/`Select`/`Button`/`Card` already used in existing settings sections.
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- **`MemoryMiddleware.update_core`** ([memory_middleware.py:137-173](api/app/core/memory_middleware.py#L137-L173)) — already used by `deep_agent.py:343`. We just expose it via REST.
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- **`get_llm()` from [api/app/core/llm.py](api/app/core/llm.py)** — for the normalization route. Use `gpt-4o-mini` with `temperature=0` and JSON response format.
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- **`toCamelCase` / `toSnakeCase`** in `auth-manager` — handles `mark_onboarded` ↔ `markOnboarded` automatically.
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- **electron-store helpers** ([store.ts:62-98](adiuvAI/src/main/store.ts#L62-L98)) — same pattern as `getDeviceId` / `getLocalAgents`.
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---
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||||
## Verification
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1. **Backend migration + tests**:
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```
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cd api && alembic upgrade head
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pytest tests/test_auth.py -k "memory or normalize"
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```
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Manually `curl PUT /api/v1/auth/me/memory` with `{"memory": {"job_role":"Developer"}, "mark_onboarded": true}` and confirm round-trip via `GET /api/v1/auth/me`.
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2. **LLM normalization route**:
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- `curl POST /api/v1/auth/onboarding/normalize` with `{"inputs": {"job_role": "i build websites", "industry": "tech-ish stuff"}}`.
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- Expect `{"normalized": {"job_role": "Web Developer", "industry": "Technology"}}` (or similar — exact phrasing varies).
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- Stop the LLM provider (or use an invalid `OPENAI_API_KEY`) and re-run — must return inputs unchanged, never 500.
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||||
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3. **Locale auto-seed (FE + BE)**:
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- Fresh user, fresh electron-store. Log in via Electron.
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||||
- `getFormatPrefs()` should now return the detected `{timezone, dateFormat, timeFormat}`.
|
||||
- `memory_core` should have one row: `language`.
|
||||
- Reload app → no second seed call (idempotent).
|
||||
|
||||
4. **First-run wizard (golden path with chips only)**:
|
||||
- Reset: backend `UPDATE users SET onboarding_completed_at=NULL; DELETE FROM memory_core WHERE user_id=...`. FE: clear electron-store `formatPrefs`.
|
||||
- `npm start` → log in → land on `OnboardingFlow`.
|
||||
- Pick a chip on every step (no free text). Confirm language. Land on review screen — should not show a loading spinner (no normalization needed). Click Confirm.
|
||||
- `auth.status` invalidates, AppShell mounts the home chat.
|
||||
- `SELECT key FROM memory_core WHERE user_id=...` → 5 keys (job_role, industry, primary_use_case, tone_preference, language).
|
||||
- Reload app → does not re-prompt.
|
||||
|
||||
5. **First-run wizard (free-text path)**:
|
||||
- Reset. Walk through wizard typing free text on `job_role` and `industry` (e.g. "i build websites", "tech-ish stuff").
|
||||
- On final step, see ~1-2s "Tidying up…" spinner, then a review screen showing the normalized values plus the chip-selected use_case/tone/language.
|
||||
- Edit one normalized value manually. Confirm. The edited value is what lands in `memory_core`.
|
||||
|
||||
6. **AI uses the data (proof the wiring works)**:
|
||||
- With an onboarded user whose `tone_preference="Formal"` and `language="it-IT"`, ask the home chat "draft a quick status email".
|
||||
- Response should be in Italian and read formal. **If language doesn't match, `enrich_context` is not feeding `core_memory` into the prompt as expected — investigate before declaring done.** This is the single most likely failure point because we don't modify it.
|
||||
|
||||
7. **Format prefs reach the LLM as strings**:
|
||||
- From the home chat, ask "what tasks are due this week?".
|
||||
- Inspect the network/log of the `tool_result` frame the FE sends back. Every `dueDate` field must be a formatted string like `"15/04/2026 14:30"`, not a numeric timestamp.
|
||||
- The AI's response must reference dates in the user's preferred format.
|
||||
- Change `time_format` from 24h to 12h in Settings → Profile. Re-ask. Times should now be `2:30 PM` style.
|
||||
|
||||
8. **Skip flow**:
|
||||
- Reset, log in, click Skip on step 1.
|
||||
- `users.onboarding_completed_at` set; `memory_core` only has `language` (from auto-seed).
|
||||
- Reload → no re-prompt.
|
||||
|
||||
9. **Re-run onboarding**: Settings → Profile → "Re-run onboarding" → wizard mounts immediately.
|
||||
|
||||
10. **Lint**:
|
||||
```
|
||||
cd adiuvAI && source ~/.nvm/nvm.sh && npm run lint
|
||||
cd api && ruff check .
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Out of scope (deferred)
|
||||
|
||||
- **UI internationalisation framework**: storing `language` enables future i18n, but no translation library is added. Wizard copy hardcoded in English for v1.
|
||||
- **Avatar upload control** in Settings → Profile: avatar already comes from Google OAuth via `users.avatar_url`. A manual upload UI is a nice-to-have follow-up.
|
||||
- **Working hours**, **top goals** (free-text seeds): same `MemoryCore` pattern — easy to add later.
|
||||
- **Cross-device sync of format prefs**: v1 stores them per-device in electron-store. Migrating to `MemoryCore` later doesn't break the wire format.
|
||||
- **Schema-bump re-prompting**: when we add a new wizard question later we'll need a `core_memory["__onboarding_version__"]` key and a guard. Not needed now.
|
||||
- **Animated typing effect** on AI bubbles.
|
||||
Reference in New Issue
Block a user