ALwrity/backend/services/onboarding

Onboarding Services Package

This package contains all onboarding-related services and utilities for ALwrity. All onboarding data is stored in the database with proper user isolation, replacing the previous file-based JSON storage system.

Architecture

Database-First Design

• Primary Storage: PostgreSQL database with proper foreign keys and relationships
• User Isolation: Each user's onboarding data is completely separate
• No File Storage: Removed all JSON file operations for production scalability
• Local Development: API keys still written to .env for developer convenience

Service Structure

backend/services/onboarding/
├── __init__.py                      # Package exports
├── database_service.py              # Core database operations
├── progress_service.py              # Progress tracking and step management
├── data_service.py                  # Data validation and processing
├── api_key_manager.py               # API key management + progress tracking
└── README.md                        # This documentation

Services

1. OnboardingDatabaseService (database_service.py)

Purpose: Core database operations for onboarding data with user isolation.

Key Features:

• User-specific session management
• API key storage and retrieval
• Website analysis persistence
• Research preferences management
• Persona data storage
• Brand analysis support (feature-flagged)

Main Methods:

• get_or_create_session(user_id) - Get or create user session
• save_api_key(user_id, provider, key) - Store API keys
• save_website_analysis(user_id, data) - Store website analysis
• save_research_preferences(user_id, prefs) - Store research settings
• save_persona_data(user_id, data) - Store persona information

2. OnboardingProgressService (progress_service.py)

Purpose: High-level progress tracking and step management.

Key Features:

• Database-only progress tracking
• Step completion validation
• Progress percentage calculation
• Onboarding completion management

Main Methods:

• get_onboarding_status(user_id) - Get current status
• update_step(user_id, step_number) - Update current step
• update_progress(user_id, percentage) - Update progress
• complete_onboarding(user_id) - Mark as complete

3. OnboardingDataService (data_service.py)

Purpose: Extract and use onboarding data for AI personalization.

Key Features:

• Personalized AI input generation
• Website analysis data extraction
• Research preferences integration
• Default fallback data

Main Methods:

• get_personalized_ai_inputs(user_id) - Generate personalized inputs
• get_user_website_analysis(user_id) - Get website data
• get_user_research_preferences(user_id) - Get research settings

4. OnboardingProgress + APIKeyManager (api_key_manager.py)

Purpose: Combined API key management and progress tracking with database persistence.

Key Features:

• Database-only progress persistence (no JSON files)
• API key management with environment integration
• Step-by-step progress tracking
• User-specific progress instances

Main Classes:

• OnboardingProgress - Progress tracking with database persistence
• APIKeyManager - API key management
• StepData - Individual step data structure
• StepStatus - Step status enumeration

Database Schema

Core Tables

• onboarding_sessions - User session tracking
• api_keys - User-specific API key storage
• website_analyses - Website analysis data
• research_preferences - User research settings
• persona_data - Generated persona information

Relationships

• All data tables reference onboarding_sessions.id
• User isolation via user_id foreign key
• Proper cascade deletion and updates

Usage Examples

Basic Progress Tracking

from services.onboarding import OnboardingProgress

# Get user-specific progress
progress = OnboardingProgress(user_id="user123")

# Mark step as completed
progress.mark_step_completed(1, {"api_keys": {"openai": "sk-..."}})

# Get progress summary
summary = progress.get_progress_summary()

Database Operations

from services.onboarding import OnboardingDatabaseService
from services.database import SessionLocal

db = SessionLocal()
service = OnboardingDatabaseService(db)

# Save API key
service.save_api_key("user123", "openai", "sk-...")

# Get website analysis
analysis = service.get_website_analysis("user123", db)

Progress Service

from services.onboarding import OnboardingProgressService

service = OnboardingProgressService()

# Get status
status = service.get_onboarding_status("user123")

# Update progress
service.update_step("user123", 2)
service.update_progress("user123", 50.0)

Migration from File-Based Storage

What Was Removed

• JSON file operations (.onboarding_progress*.json)
• File-based progress persistence
• Dual persistence system (file + database)

What Was Kept

• Database persistence (enhanced)
• Local development .env API key writing
• All existing functionality and APIs

Benefits

• Production Ready: No ephemeral file storage
• Scalable: Database-backed with proper indexing
• User Isolated: Complete data separation
• Maintainable: Single source of truth

Environment Variables

Required

• Database connection (via services.database)
• User authentication system

Optional

• ENABLE_WEBSITE_BRAND_COLUMNS=true - Enable brand analysis features
• DEPLOY_ENV=local - Enable local .env API key writing

Error Handling

All services include comprehensive error handling:

• Database connection failures
• User not found scenarios
• Invalid data validation
• Graceful fallbacks to defaults

Performance Considerations

• Database queries are optimized with proper indexing
• User-specific caching where appropriate
• Minimal database calls through efficient service design
• Connection pooling via SQLAlchemy

Testing

Each service can be tested independently:

• Unit tests for individual methods
• Integration tests with database
• Mock database sessions for isolated testing

Future Enhancements

• Real-time progress updates via WebSocket
• Progress analytics and reporting
• Bulk user operations
• Advanced validation rules
• Progress recovery mechanisms