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voyage/.memory/knowledge/domain/collections-and-sharing.md
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2026-03-13 20:15:22 +00:00

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---
title: collections-and-sharing
type: note
permalink: voyage/knowledge/domain/collections-and-sharing
---
# Collections & Sharing Domain
## Collection Sharing Architecture
### Data Model
- `Collection.shared_with` - `ManyToManyField(User, related_name='shared_with', blank=True)` - the primary access grant
- `CollectionInvite` - staging table for pending invites: `(collection FK, invited_user FK, unique_together)`; prevents self-invite and double-invite
- Both models in `backend/server/adventures/models.py`
### Access Flow (Invite -> Accept -> Membership)
1. Owner calls `POST /api/collections/{id}/share/{uuid}/` -> creates `CollectionInvite`
2. Invited user sees pending invites via `GET /api/collections/invites/`
3. Invited user calls `POST /api/collections/{id}/accept-invite/` -> adds to `shared_with`, deletes invite
4. Owner revokes: `POST /api/collections/{id}/revoke-invite/{uuid}/`
5. User declines: `POST /api/collections/{id}/decline-invite/`
6. Owner removes: `POST /api/collections/{id}/unshare/{uuid}/` - removes user's locations from collection
7. User self-removes: `POST /api/collections/{id}/leave/` - removes their locations
### Permission Classes
- `CollectionShared` - full access for owner and `shared_with` members; invite actions for invitees; anonymous read for `is_public`
- `IsOwnerOrSharedWithFullAccess` - child-object viewsets; traverses `obj.collections`/`obj.collection` to check `shared_with`
- `ContentImagePermission` - delegates to `IsOwnerOrSharedWithFullAccess` on `content_object`
### Key Design Constraints
- No role differentiation - all shared users have same write access
- On unshare/leave, departing user's locations are removed from collection (not deleted)
- `duplicate` action creates a private copy with no `shared_with` transfer
### Chat Agent Tool Access
- `get_trip_details` and `add_to_itinerary` tools authorize using `Q(user=user) | Q(shared_with=user)` — shared members can use AI chat tools on shared collections.
- `list_trips` remains owner-only (shared collections not listed).
- `add_to_itinerary` assigns `Location.user = shared_user` (shared users own their contributed locations), consistent with REST API behavior.
- See [patterns/chat-and-llm.md](../patterns/chat-and-llm.md#shared-trip-tool-access).
## Itinerary Architecture
### Primary Component
`CollectionItineraryPlanner.svelte` (~3818 lines) - monolith rendering the entire itinerary view and all modals.
### The "Add" Button
DaisyUI dropdown at bottom of each day card with "Link existing item" and "Create new" submenu (Location, Lodging, Transportation, Note, Checklist).
### Day Suggestions Modal (WS3)
- Component: `ItinerarySuggestionModal.svelte`
- Props: `collection`, `user`, `targetDate`, `displayDate`
- Events: `close`, `addItem { type: 'location', itemId, updateDate }`
- 3-step UX: category selection -> filters -> results from `POST /api/chat/suggestions/day/`
## User Recommendation Preference Profile
Backend-only feature: model, API, and system-prompt integration exist, but **no frontend UI** built yet.
### Auto-learned preference updates
- `backend/server/integrations/utils/auto_profile.py` derives profile from user history
- Fields: `interests` (from activities + locations), `trip_style` (from lodging), `notes` (frequently visited regions)
- `ChatViewSet.send_message()` invokes `update_auto_preference_profile(request.user)` at method start
### Model
`UserRecommendationPreferenceProfile` (OneToOne -> `CustomUser`): `cuisines`, `interests` (JSONField), `trip_style`, `notes`, timestamps.
### System Prompt Integration
- Single-user: labeled as auto-inferred with structured markdown section
- Shared collections: `get_aggregated_preferences(collection)` appends per-participant preferences
- Missing profiles skipped gracefully
### Frontend Gap
- No settings tab for manual preference editing
- TypeScript type available as `UserRecommendationPreferenceProfile` in `src/lib/types.ts`
- See [Plan: AI travel agent redesign](../../plans/ai-travel-agent-redesign.md#ws2-user-preference-learning)