physicalCombinatorics/CLAUDE.md

CLAUDE.md

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

What this project is

PhysCom (Physical Combinatorics) — innovation discovery engine that generates entity combinations across dimensions (e.g. platform × power_source), filters by physical constraints, scores against domain-specific metrics, and ranks results. Includes a CLI, a Flask/HTMX web UI, and a 5-pass pipeline (constraints → estimation → scoring → LLM review → human review).

Commands

• Install: pip install -e ".[dev,web]" (editable install with test and web deps)
• Tests (all): python -m pytest tests/ -q (48 tests, ~5s). Run after every change.
• Single test file: python -m pytest tests/test_scorer.py -q
• Single test: python -m pytest tests/test_scorer.py::test_score_combination -q
• Web dev server: python -m physcom_web
• CLI: python -m physcom (or physcom if installed)
• Docker: docker compose up web / docker compose run cli physcom seed
• Seed data: loaded automatically on first DB init (SQLite, physcom.db or $PHYSCOM_DB)

Architecture

src/physcom/               # Core library (no web dependency)
  models/                  # Dataclasses: Entity, Dependency, Combination, Domain, MetricBound
  db/schema.py             # DDL (all CREATE TABLE statements)
  db/repository.py         # All DB access — single Repository class, sqlite3 row_factory=Row
  engine/combinator.py     # Cartesian product of entities across dimensions
  engine/constraint_resolver.py  # Pass 1: requires/excludes/mutex/range/force checks
  engine/scorer.py         # Pass 3: log-normalize raw→0-1, weighted geometric mean composite
  engine/pipeline.py       # Orchestrator: combo-first loop, incremental saves, resume, cancel
  llm/base.py              # LLMProvider ABC (estimate_physics, review_plausibility)
  llm/providers/mock.py    # MockLLMProvider for tests
  seed/transport_example.py  # 9 platforms + 9 power sources, 2 domains

src/physcom_web/           # Flask web UI
  app.py                   # App factory, get_repo(), DB path resolution
  routes/pipeline.py       # Background thread pipeline execution, HTMX status/cancel endpoints
  routes/results.py        # Results browse, detail view, human review submission
  routes/entities.py       # Entity CRUD
  routes/domains.py        # Domain listing
  templates/               # Jinja2, extends base.html, uses HTMX for polling
  static/style.css         # Single stylesheet

tests/                     # pytest, uses seeded_repo fixture from conftest.py

Key patterns

• Repository is the only DB interface. No raw SQL outside repository.py.
• Pipeline is combo-first: each combo goes through all requested passes before the next combo starts. Progress is persisted per-combo (crash-safe, resumable).
• pipeline_runs table tracks run lifecycle: pending → running → completed/failed/cancelled. The web route creates the record, then starts a background thread with its own sqlite3.Connection.
• combination_results has rows for ALL combos including blocked ones (pass_reached=1, composite_score=0.0). Scored combos get pass_reached=3+.
• Status guard: update_combination_status refuses to downgrade reviewed → scored.
• save_combination loads existing status/block_reason on dedup (important for resume).
• ensure_metric backfills unit if the row already exists with an empty unit.
• MetricBound carries unit — flows through seed → ensure_metric → metrics table → get_combination_scores → template display.
• HTMX polling: _run_status.html partial polls every 2s while run is pending/running; stops polling when terminal.

Data flow (pipeline passes)

1. Pass 1 — Constraints: ConstraintResolver.resolve() → blocked/conditional/valid. Blocked combos get a result row and continue.
2. Pass 2 — Estimation: LLM or _stub_estimate() → raw metric values. Saved immediately via save_raw_estimates() (normalized_score=NULL).
3. Pass 3 — Scoring: Scorer.score_combination() → log-normalized scores + weighted geometric mean composite. Saves via save_scores() + save_result().
4. Pass 4 — LLM Review: Only for above-threshold combos with an LLM provider. No real provider yet (only MockLLMProvider).
5. Pass 5 — Human Review: Manual via web UI results page.

Testing

• Tests use seeded_repo fixture (in-memory SQLite with transport seed data: 9 platforms, 9 power sources, 2 domains). There's also a bare repo fixture for tests that seed their own data.
• Individual entity fixtures (walking, bicycle, spaceship, solar_sail, etc.) are defined in conftest.py.

Conventions

• Python 3.11+, from __future__ import annotations everywhere.
• Dataclasses for models, no ORM.
• Don't use cd in Bash commands — run from the working directory so pre-approved permission patterns match.
• Don't add docstrings/comments/type annotations to code you didn't change.
• INSERT OR IGNORE won't update existing rows — if adding a new column/field to seed data, also add an UPDATE for backfill.
• Jinja2 0.0 is falsy — use is not none not if value when displaying scores that can legitimately be zero.