Keep the /routines/suggest base context lean by sending only active names and fetching detailed safety, actives, usage notes, and INCI on demand. Add a conservative fallback when tool roundtrip limits are hit to preserve safe outputs instead of failing the request.
Enable on-demand INCI retrieval in /routines/suggest through Gemini function calling so detailed ingredient data is fetched only when needed. Persist and normalize tool_trace data in AI logs to make function-call behavior directly inspectable via /ai-logs endpoints.
Add ProductBase, ProductPublic, ProductWithInventory and
SkinConditionSnapshotBase, SkinConditionSnapshotPublic. Table models now inherit
from their Base counterpart and override JSON fields with sa_column. All
field_serializer hacks removed; FastAPI response models use the non-table Public
classes so Pydantic coerces raw DB dicts → typed models cleanly. ProductCreate
and SnapshotCreate now simply inherit their respective Base classes.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Rename skincare route prefix /skin-snapshots → /skincare to match API client
- Add redirect_slashes=False to FastAPI app; change collection routes from "/" to ""
to eliminate 307 redirects on POST/GET without trailing slash
- Fix redirect() inside try/catch in products/new and routines/new server actions
(SvelteKit redirect() throws and was being caught as a 500 error)
- Eagerly load inventory and steps relationships via explicit SELECT + model_dump(mode="json"),
working around SQLModel 0.0.37 not serializing Relationship fields in response_model
- Add field_validator for product_effect_profile to coerce DB-returned dict → ProductEffectProfile,
eliminating Pydantic serializer warning
- Update all tests to use routes without trailing slash
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
FastAPI backend for personal health and skincare data with MCP export.
Includes SQLModel models for products, inventory, medications, lab results,
routines, and skin condition snapshots. Pytest suite with 111 tests running
on SQLite in-memory (no PostgreSQL required).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
