Piotr Oleszczyk 8f7d893a63 Initial commit: backend API, data models, and test suite

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>

2026-02-26 15:10:24 +01:00

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CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Repository structure

This is a monorepo. The backend lives in backend/; a frontend will be added in the future.

Commands

Run all backend commands from the backend/ directory:

# Run scripts
cd backend && uv run python main.py

# Linting / formatting
cd backend && uv run ruff check .
cd backend && uv run black .
cd backend && uv run isort .

No test suite exists yet.

Architecture

innercontext collects personal health and skincare data and exposes it via MCP to an LLM agent. Stack: Python 3.12, SQLModel (0.0.37) + SQLAlchemy, Pydantic v2, FastAPI, PostgreSQL (psycopg3).

Models (`backend/innercontext/models/`)

File	Tables
`product.py`	`products`, `product_inventory`
`health.py`	`medication_entries`, `medication_usages`, `lab_results`
`routine.py`	`routines`, `routine_steps`
`skincare.py`	`skin_condition_snapshots`

Product is the core model. JSON columns store inci (list), actives (list of ActiveIngredient), recommended_for, targets, incompatible_with, synergizes_with, context_rules, and product_effect_profile. The to_llm_context() method returns a token-optimised dict for MCP.

ProductInventory tracks physical packages (opened status, expiry, remaining weight). One product → many inventory entries.

Routine / RoutineStep record daily AM/PM skincare sessions. A step references either a Product or a free-text action (e.g. shaving).

SkinConditionSnapshot is a weekly LLM-filled record (skin state, metrics 1–5, active concerns).

Key conventions

All table=True models use Column(DateTime(timezone=True), onupdate=utc_now) for updated_at via raw SQLAlchemy column — do not use plain Field(default_factory=...) for auto-update.
List/complex fields stored as JSON use sa_column=Column(JSON, nullable=...) pattern (DB-agnostic; not JSONB).
model_validator(mode="after") does not fire on table=True SQLModel instances (SQLModel 0.0.37 + Pydantic v2 bug). Validators in Product are present for documentation but are unreliable at construction time.
backend/skincare.yaml is a legacy notes file — ignore it, it is not part of the data model and will not be imported.
_ev() helper in product.py normalises enum values when fields may be raw dicts (as returned from DB) or Python enum instances.

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