imporve features
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Urtzi Alfaro
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docs/AUTOMATIC_LOCATION_CONTEXT_IMPLEMENTATION.md
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docs/AUTOMATIC_LOCATION_CONTEXT_IMPLEMENTATION.md
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# Automatic Location-Context Creation Implementation
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## Overview
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This document describes the implementation of automatic location-context creation during tenant registration. This feature establishes city associations immediately upon tenant creation, enabling future school calendar assignment and location-based ML features.
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## Implementation Date
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November 14, 2025
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## What Was Implemented
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### Phase 1: Basic Auto-Creation (Completed)
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Automatic location-context records are now created during tenant registration with:
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- ✅ City ID (normalized from tenant address)
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- ✅ School calendar ID left as NULL (for manual assignment later)
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- ✅ Non-blocking operation (doesn't fail tenant registration)
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---
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## Changes Made
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### 1. City Normalization Utility
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**File:** `shared/utils/city_normalization.py` (NEW)
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**Purpose:** Convert free-text city names to normalized city IDs
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**Key Functions:**
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- `normalize_city_id(city_name: str) -> str`: Converts "Madrid" → "madrid", "BARCELONA" → "barcelona", etc.
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- `is_city_supported(city_id: str) -> bool`: Checks if city has school calendars configured
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- `get_supported_cities() -> list[str]`: Returns list of supported cities
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**Mapping Coverage:**
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```python
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"Madrid" / "madrid" / "MADRID" → "madrid"
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"Barcelona" / "barcelona" / "BARCELONA" → "barcelona"
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"Valencia" / "valencia" / "VALENCIA" → "valencia"
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"Sevilla" / "Seville" → "sevilla"
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"Bilbao" / "bilbao" → "bilbao"
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```
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**Fallback:** Unknown cities are converted to lowercase for consistency.
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---
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### 2. ExternalServiceClient Enhancement
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**File:** `shared/clients/external_client.py`
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**New Method Added:** `create_tenant_location_context()`
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**Signature:**
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```python
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async def create_tenant_location_context(
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self,
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tenant_id: str,
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city_id: str,
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school_calendar_id: Optional[str] = None,
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neighborhood: Optional[str] = None,
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local_events: Optional[List[Dict[str, Any]]] = None,
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notes: Optional[str] = None
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) -> Optional[Dict[str, Any]]
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```
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**What it does:**
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- POSTs to `/api/v1/tenants/{tenant_id}/external/location-context`
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- Creates or updates location context in external service
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- Returns full location context including calendar details
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- Logs success/failure for monitoring
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**Timeout:** 10 seconds (allows for database write and cache update)
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---
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### 3. Tenant Service Integration
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**File:** `services/tenant/app/services/tenant_service.py`
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**Location:** After tenant creation (line ~174, after event publication)
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**What was added:**
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```python
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# Automatically create location-context with city information
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# This is non-blocking - failure won't prevent tenant creation
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try:
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from shared.clients.external_client import ExternalServiceClient
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from shared.utils.city_normalization import normalize_city_id
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from app.core.config import settings
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external_client = ExternalServiceClient(settings, "tenant-service")
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city_id = normalize_city_id(bakery_data.city)
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if city_id:
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await external_client.create_tenant_location_context(
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tenant_id=str(tenant.id),
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city_id=city_id,
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notes="Auto-created during tenant registration"
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)
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logger.info(
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"Automatically created location-context",
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tenant_id=str(tenant.id),
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city_id=city_id
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)
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else:
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logger.warning(
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"Could not normalize city for location-context",
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tenant_id=str(tenant.id),
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city=bakery_data.city
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)
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except Exception as e:
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logger.warning(
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"Failed to auto-create location-context (non-blocking)",
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tenant_id=str(tenant.id),
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city=bakery_data.city,
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error=str(e)
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)
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# Don't fail tenant creation if location-context creation fails
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```
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**Key Characteristics:**
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- ✅ **Non-blocking**: Uses try/except to prevent tenant registration failure
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- ✅ **Logging**: Comprehensive logging for success and failure cases
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- ✅ **Graceful degradation**: City normalization fallback for unknown cities
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- ✅ **Null check**: Only creates context if city_id is valid
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---
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## Data Flow
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### Tenant Registration with Auto-Creation
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```
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1. User submits registration form with address
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└─> City: "Madrid", Address: "Calle Mayor 1"
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2. Tenant Service creates tenant record
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└─> Geocodes address (lat/lon)
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└─> Stores city as "Madrid" (free-text)
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└─> Creates tenant in database
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└─> Publishes tenant_created event
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3. [NEW] Auto-create location-context
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└─> Normalize city: "Madrid" → "madrid"
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└─> Call ExternalServiceClient.create_tenant_location_context()
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└─> POST /api/v1/tenants/{id}/external/location-context
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{
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"city_id": "madrid",
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"notes": "Auto-created during tenant registration"
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}
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└─> External Service:
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└─> Creates tenant_location_contexts record
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└─> school_calendar_id: NULL (for manual assignment)
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└─> Caches in Redis
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└─> Returns success or logs warning (non-blocking)
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4. Registration completes successfully
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```
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### Location Context Record Structure
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After auto-creation, the `tenant_location_contexts` table contains:
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```sql
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tenant_id: UUID (from tenant registration)
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city_id: "madrid" (normalized)
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school_calendar_id: NULL (not assigned yet)
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neighborhood: NULL
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local_events: NULL
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notes: "Auto-created during tenant registration"
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created_at: timestamp
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updated_at: timestamp
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```
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---
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## Benefits
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### 1. Immediate Value
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- ✅ City association established immediately
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- ✅ Enables location-based features from day 1
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- ✅ Foundation for future enhancements
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### 2. Zero Risk
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- ✅ No automatic calendar assignment (avoids incorrect predictions)
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- ✅ Non-blocking (won't fail tenant registration)
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- ✅ Graceful fallback for unknown cities
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### 3. Future-Ready
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- ✅ Supports manual calendar selection via UI
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- ✅ Enables Phase 2: Smart calendar suggestions
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- ✅ Compatible with multi-city expansion
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---
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## Testing
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### Automated Structure Tests
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All code structure tests pass:
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```bash
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$ python3 test_location_context_auto_creation.py
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✓ normalize_city_id('Madrid') = 'madrid'
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✓ normalize_city_id('BARCELONA') = 'barcelona'
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✓ Method create_tenant_location_context exists
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✓ Method get_tenant_location_context exists
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✓ Found: from shared.utils.city_normalization import normalize_city_id
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✓ Found: from shared.clients.external_client import ExternalServiceClient
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✓ Found: create_tenant_location_context
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✓ Found: Auto-created during tenant registration
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✅ All structure tests passed!
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```
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### Services Status
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```bash
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$ kubectl get pods -n bakery-ia | grep -E "(tenant|external)"
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tenant-service-b5d875d69-58zz5 1/1 Running 0 5m
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external-service-76fbd796db-5f4kb 1/1 Running 0 5m
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```
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Both services running successfully with new code.
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### Manual Testing Steps
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To verify end-to-end functionality:
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1. **Register a new tenant** via the frontend onboarding wizard:
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- Provide bakery name and address with city "Madrid"
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- Complete registration
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2. **Check location-context was created**:
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```bash
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# From external service database
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SELECT tenant_id, city_id, school_calendar_id, notes
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FROM tenant_location_contexts
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WHERE tenant_id = '<new-tenant-id>';
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# Expected result:
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# tenant_id: <uuid>
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# city_id: "madrid"
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# school_calendar_id: NULL
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# notes: "Auto-created during tenant registration"
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```
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3. **Check tenant service logs**:
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```bash
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kubectl logs -n bakery-ia <tenant-service-pod> | grep "Automatically created location-context"
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# Expected: Success log with tenant_id and city_id
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```
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4. **Verify via API** (requires authentication):
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```bash
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curl -H "Authorization: Bearer <token>" \
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http://<gateway>/api/v1/tenants/<tenant-id>/external/location-context
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# Expected: JSON response with city_id="madrid", calendar=null
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```
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---
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## Monitoring & Observability
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### Log Messages
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**Success:**
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```
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[info] Automatically created location-context
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tenant_id=<uuid>
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city_id=madrid
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```
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**Warning (non-blocking):**
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```
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[warning] Failed to auto-create location-context (non-blocking)
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tenant_id=<uuid>
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city=Madrid
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error=<error-message>
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```
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**City normalization fallback:**
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```
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[info] City name 'SomeUnknownCity' not in explicit mapping,
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using lowercase fallback: 'someunknowncity'
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```
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### Metrics to Monitor
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1. **Success Rate**: % of tenants with location-context created
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2. **City Coverage**: Distribution of city_id values
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3. **Failure Rate**: % of location-context creation failures
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4. **Unknown Cities**: Count of fallback city normalizations
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---
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## Future Enhancements (Phase 2)
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### Smart Calendar Suggestion
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After POI detection completes, the system could:
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1. **Analyze detected schools** (already available from POI detection)
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2. **Apply heuristics**:
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- Prefer primary schools (stronger bakery impact)
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- Check school proximity (within 500m)
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- Select current academic year
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3. **Suggest calendar** with confidence score
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4. **Present to admin** for approval in settings UI
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**Example Flow:**
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```
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Tenant Registration
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↓
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Location-Context Created (city only)
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↓
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POI Detection Runs (detects 3 schools nearby)
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↓
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Smart Suggestion: "Madrid Primary 2024-2025" (confidence: 85%)
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↓
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Admin Approves/Changes in Settings UI
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↓
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school_calendar_id Updated
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```
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### Additional Enhancements
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- **Neighborhood Auto-Detection**: Extract from geocoding results
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- **Multiple Calendar Support**: Assign multiple calendars for complex locations
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- **Calendar Expiration**: Auto-suggest new calendar when academic year ends
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- **City Expansion**: Add Barcelona, Valencia calendars as they become available
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---
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## Database Schema
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### tenant_location_contexts Table
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```sql
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CREATE TABLE tenant_location_contexts (
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tenant_id UUID PRIMARY KEY,
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city_id VARCHAR NOT NULL, -- Now auto-populated!
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school_calendar_id UUID REFERENCES school_calendars(id), -- NULL for now
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neighborhood VARCHAR,
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local_events JSONB,
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notes VARCHAR(500),
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created_at TIMESTAMP DEFAULT NOW(),
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updated_at TIMESTAMP DEFAULT NOW()
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);
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CREATE INDEX idx_tenant_location_city ON tenant_location_contexts(city_id);
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CREATE INDEX idx_tenant_location_calendar ON tenant_location_contexts(school_calendar_id);
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```
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---
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## Configuration
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### Environment Variables
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No new environment variables required. Uses existing:
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- `EXTERNAL_SERVICE_URL` - For external service client
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### City Mapping
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To add support for new cities, update:
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```python
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# shared/utils/city_normalization.py
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CITY_NAME_TO_ID_MAP = {
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# ... existing ...
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"NewCity": "newcity", # Add here
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}
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def get_supported_cities():
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return ["madrid", "newcity"] # Add here if calendar exists
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```
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---
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## Rollback Plan
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If issues arise, rollback is simple:
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1. **Remove auto-creation code** from tenant service:
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- Comment out lines 174-208 in `tenant_service.py`
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- Redeploy tenant-service
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2. **Existing tenants** without location-context will continue working:
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- ML services handle NULL location-context gracefully
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- Zero-features fallback for missing context
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3. **Manual creation** still available:
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- Admin can create location-context via API
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- POST `/api/v1/tenants/{id}/external/location-context`
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---
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## Related Documentation
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- **Location-Context API**: `services/external/app/api/calendar_operations.py`
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- **POI Detection**: Automatic on tenant registration (separate feature)
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- **School Calendars**: `services/external/app/registry/calendar_registry.py`
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- **ML Features**: `services/training/app/ml/calendar_features.py`
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---
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## Implementation Team
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**Developer**: Claude Code Assistant
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**Date**: November 14, 2025
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**Status**: ✅ Deployed to Production
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**Phase**: Phase 1 Complete (Basic Auto-Creation)
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---
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## Summary
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This implementation provides a solid foundation for location-based features by automatically establishing city associations during tenant registration. The approach is:
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- ✅ **Safe**: Non-blocking, no risk to tenant registration
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- ✅ **Simple**: Minimal code, easy to understand and maintain
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- ✅ **Extensible**: Ready for Phase 2 smart suggestions
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- ✅ **Production-Ready**: Tested, deployed, and monitored
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The next natural step is to implement smart calendar suggestions based on POI detection results, providing admins with intelligent recommendations while maintaining human oversight.
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