feat(backend): implement database layer with models, schemas and repositories
Complete backend core implementation (BE-001 to BE-005):
BE-001: Database Connection & Session Management
- Create src/core/database.py with async SQLAlchemy 2.0
- Configure engine with pool_size=20
- Implement get_db() FastAPI dependency
BE-002: SQLAlchemy Models (5 models)
- Base model with TimestampMixin
- Scenario: status enum, relationships, cost tracking
- ScenarioLog: message hash, PII detection, metrics
- ScenarioMetric: time-series with extra_data (JSONB)
- AwsPricing: service pricing with region support
- Report: format enum, file tracking, extra_data
BE-003: Pydantic Schemas
- Scenario: Create, Update, Response, List schemas
- Log: Ingest, Response schemas
- Metric: Summary, CostBreakdown, MetricsResponse
- Common: PaginatedResponse generic type
BE-004: Base Repository Pattern
- Generic BaseRepository[T] with CRUD operations
- Methods: get, get_multi, count, create, update, delete
- Dynamic filter support
BE-005: Scenario Repository
- Extends BaseRepository[Scenario]
- Specific methods: get_by_name, list_by_status, list_by_region
- Business methods: update_status, increment_total_requests, update_total_cost
- ScenarioStatus enum
- Singleton instance: scenario_repository
All models, schemas and repositories tested and working.
Tasks: BE-001, BE-002, BE-003, BE-004, BE-005 complete
Luca Sacchi Ricciardi
ebefc323c3