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POLICY SERVICE SQLALCHEMY MODELS - IMPLEMENTATION COMPLETE
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File Location:
/mnt/data-disk1/archie-platform-v3-worktrees/policy-service-impl/services/policy-service/models.py

Database Specification Reference:
/mnt/data-disk1/archie-platform-v3-worktrees/policy-service-impl/docs/specifications/POLICY_SERVICE_DATABASE_SPEC.md

Test File:
/mnt/data-disk1/archie-platform-v3-worktrees/policy-service-impl/services/policy-service/tests/test_database.py

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IMPLEMENTATION SUMMARY
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Created 9 SQLAlchemy model classes matching the database specification:

1. Policy (policies) - Main insurance policy table
   - 22 columns including JSONB fields for coverage_details, underwriting_data
   - 13 indexes (B-tree, GIN for JSONB, composite indexes)
   - Optimistic locking with version column
   - Soft delete support with deleted_at
   - Multi-tenant via organization_id

2. PolicyVersion (policy_versions) - Audit trail of changes
   - 9 columns with full policy snapshots in JSONB
   - Tracks changed fields and change types
   - Unique constraint on (policy_id, version)

3. PolicyDocument (policy_documents) - Document attachments
   - 20 columns for file metadata and storage
   - Support for Supabase Storage integration
   - Document signing tracking (is_signed, signed_at, signed_by)

4. PolicyEndorsement (policy_endorsements) - Mid-term changes
   - 16 columns for endorsement tracking
   - Premium adjustment support (positive/negative)
   - Status workflow (pending → approved → applied)

5. PolicyClaim (policy_claims) - Insurance claims
   - 19 columns for claim management
   - Financial tracking (claimed, reserved, paid amounts)
   - Status workflow (reported → investigating → approved/denied → closed)

6. PolicyBeneficiary (policy_beneficiaries) - Life insurance beneficiaries
   - 16 columns for beneficiary information
   - Allocation percentage validation (0-100%)
   - Support for primary, contingent, irrevocable types

7. PolicyAuditLog (policy_audit_log) - Comprehensive audit logging
   - 16 columns for complete audit trail
   - Captures old_values and new_values as JSONB
   - Request context (IP address, user agent, request ID)

8. PolicyEvent (policy_events) - Event sourcing/outbox pattern
   - 13 columns for event-driven architecture
   - Retry mechanism with retry_count and last_error
   - Correlation and causation tracking

9. PolicyTag (policy_tags) - Normalized tags
   - 4 columns with composite primary key (policy_id, tag)
   - Efficient tag querying with GIN index

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KEY FEATURES IMPLEMENTED
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✓ Async SQLAlchemy with AsyncAttrs mixin
✓ All columns with proper types (String, Numeric, JSONB, ARRAY, TIMESTAMP, etc.)
✓ Primary keys with ULID generation (pol_, pver_, pdoc_, etc.)
✓ Foreign key relationships with CASCADE rules
✓ CHECK constraints for data validation
✓ UNIQUE constraints for business rules
✓ Composite indexes for query optimization
✓ GIN indexes for JSONB and ARRAY columns
✓ Soft delete support (deleted_at timestamp)
✓ Optimistic locking (version column)
✓ Audit fields (created_at, updated_at, created_by, updated_by)
✓ Proper schema namespacing (policy_service)
✓ Bidirectional relationships with back_populates

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IMPORTANT NOTES
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1. Reserved Word Conflicts Resolved:
   - "metadata" column → "metadata_" attribute (maps to "metadata" in DB)
   - "relationship" column → "relationship_" attribute (maps to "relationship" in DB)

2. Schema:
   - All tables use schema="policy_service"
   - Foreign keys reference public schema (public.organizations, public.users, public.customers)

3. Type Hints:
   - Full type annotations using Mapped[] generic
   - Optional[] for nullable columns
   - Dict[str, Any] for JSONB
   - List[str] for ARRAY

4. Relationships:
   - All models have bidirectional relationships to Policy
   - Cascade rules: delete-orphan for child tables
   - Lazy loading: selectin (eager loading)

5. Indexes:
   - 13 indexes on Policy table for query optimization
   - GIN indexes for JSONB and ARRAY columns
   - Composite indexes for common filter combinations
   - Partial indexes where appropriate

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NEXT STEPS
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1. Create database migration script (Alembic)
2. Set up database connection configuration
3. Run migrations against PostgreSQL/Supabase
4. Enable Row-Level Security (RLS) policies
5. Run test suite (49 tests in test_database.py)
6. Implement database service layer (CRUD operations)
7. Add database triggers (updated_at, versioning, audit logging)

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TEST COMPATIBILITY
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The models are designed to support all 49 test scenarios in test_database.py:
- Schema validation tests (4 scenarios)
- Data integrity constraints (4 scenarios)
- Row-Level Security tests (11 scenarios)
- JSONB operations (4 scenarios)
- Index performance (3 scenarios)
- Triggers & automation (4 scenarios)
- Optimistic locking (1 scenario)
- Multi-tenancy isolation (2 scenarios)
- Related tables (4 scenarios)
- Full-text search (2 scenarios)
- Performance scenarios (2 scenarios)
- Database migrations (2 scenarios)
- Edge cases (3 scenarios)
- Service contracts (3 scenarios)

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