- Enable backend tests in CI (remove if: false)
- Fix test_products_helpers.py to pass current_user parameter
- Fix test_routines_helpers.py to include short_id in products
- Fix llm_context.py to use product_effect_profile correctly
- All 221 tests passing
Constrain shopping target concerns to SkinConcern enums and add a regression test for invalid values. Simplify the shopping prompt so repurchase suggestions stay practical, use shorter product types, and avoid leaking raw scoring/debug language into user-facing copy.
Replace product weight and repurchase intent fields with per-package remaining levels and inventory-first restock signals. Enrich shopping suggestions with usage-aware replenishment scoring so the frontend and LLM can prioritize real gaps and near-empty staples more reliably.
Two bugs in /routines/suggest where the LLM could override hard constraints:
1. Products with min_interval_hours (e.g. retinol at 72h) were passed to
the LLM even if used too recently. The LLM reasoned away the constraint
in at least one observed case. Fix: added _filter_products_by_interval()
which removes ineligible products before the prompt is built, so they
don't appear in AVAILABLE PRODUCTS at all.
2. Minoxidil was included in the available products list regardless of the
include_minoxidil_beard flag. Only the objectives context was gated,
leaving the product visible to the LLM which would include it based on
recent usage history. Fix: added include_minoxidil param to
_get_available_products() and threaded it through suggest_routine and
suggest_batch.
Also refactored _build_products_context() to accept a pre-supplied
products list instead of calling _get_available_products() internally,
ensuring the tool handler and context text always use the same filtered set.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Keep /products/suggest lean by exposing product UUIDs and fetching INCI, safety rules, actives, and usage notes on demand through Gemini function tools. Add conservative fallback behavior for tool roundtrip limits and expand helper tests to cover tool wiring and payload handlers.
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.
Gemini API rejects int-valued enums (StrengthLevel) in response_schema,
raising a validation error before any request is sent. Fix by introducing
AIActiveIngredient (inherits ActiveIngredient, overrides strength_level and
irritation_potential as Optional[int]) and ProductParseLLMResponse used only
as the Gemini schema. The two-step validation converts ints back to StrengthLevel
via Pydantic coercion. Adds a test covering the numeric strength level path.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add backend/innercontext/mcp_server.py with tools covering products,
inventory, routines, skin snapshots, medications, lab results, and
grooming schedule
- Mount MCP app at /mcp in main.py using combine_lifespans
- Fix test isolation: patch app.router.lifespan_context in conftest to
avoid StreamableHTTPSessionManager single-run limitation
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Remove the derived `trend` field (better computed from history by the MCP
agent) and add `texture: smooth|rough|flaky|bumpy` which LLM can reliably
assess from photos. Updates model, API, system prompt, tests, and frontend.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
- Drop Product.personal_rating from model, API schemas, and all frontend
views (list table, detail view, quick-edit form, new-product form)
- Extract get_or_404 into backend/innercontext/api/utils.py; remove five
duplicate copies from individual API modules
- Fix all ty type errors: generic get_or_404 with TypeVar, cast() in
coerce_effect_profile validator, col() for ilike on SQLModel column,
dict[str, Any] annotation in test helper, ty: ignore for CORSMiddleware
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Drop fields identified as redundant or low-value from the Product model,
API schemas, frontend types, and forms. Raise effect_profile threshold in
to_llm_context() from >0 to >=2 to suppress noise values. Remove sku/barcode
from LLM context output (kept on model for catalog use).
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>
