shinkan-jinkendo/backend/tests/test_planning_exercise_path_qa.py

"""Tests Planungs-KI Phase E/F — Pfad-QA."""
from planning_exercise_path_builder import _pick_best_path_hit
from planning_exercise_semantics import build_semantic_brief
from planning_exercise_path_qa import (
    apply_llm_path_reorder,
    detect_path_gaps,
    is_roadmap_planned_neighbor_pair,
)


def test_pick_best_path_hit_prefers_semantic_score():
    brief = build_semantic_brief("Mae Geri Perfektion")
    hits = [
        {"id": 1, "title": "Mawashi", "score": 0.9, "semantic_score": 0.1},
        {"id": 2, "title": "Mae Geri", "score": 0.75, "semantic_score": 0.85},
    ]
    chosen = _pick_best_path_hit(hits, set(), semantic_brief=brief)
    assert chosen["id"] == 2


def test_phrase_compact_match_maegeri():
    from planning_exercise_semantics import _phrase_in_blob

    assert _phrase_in_blob("mae geri", "Erlernen des Mae-Geri aus Einzelbewegungen")
    assert _phrase_in_blob("mae geri", "Maegeri Kihon")


def test_pick_best_path_hit_fallback_title_only_in_summary():
    from planning_exercise_semantics import pick_best_path_hit

    brief = build_semantic_brief("Mae Geri Perfektion")
    hits = [
        {
            "id": 1,
            "title": "Kumite Stellungen",
            "summary": "",
            "score": 0.9,
            "semantic_score": 0.02,
        },
        {
            "id": 2,
            "title": "Einzelbewegungen",
            "summary": "Schrittweise Erlernen des Mae Geri",
            "score": 0.5,
            "semantic_score": 0.08,
        },
    ]
    chosen = pick_best_path_hit(hits, set(), semantic_brief=brief)
    assert chosen is not None
    assert int(chosen["id"]) == 2


def test_pick_best_path_hit_skips_used():
    hits = [{"id": 1, "title": "A", "score": 0.5, "semantic_score": 0.5}]
    assert _pick_best_path_hit(hits, {1}) is None


def test_apply_llm_path_reorder_permutation():
    steps = [{"exercise_id": 1}, {"exercise_id": 2}, {"exercise_id": 3}]
    reordered, applied, notes = apply_llm_path_reorder(
        steps,
        {"ordered_step_indices": [0, 2, 1], "sequence_notes": ["Vertiefung vor Anwendung"]},
    )
    assert applied is True
    assert [s["exercise_id"] for s in reordered] == [1, 3, 2]
    assert notes


def test_is_roadmap_planned_neighbor_pair():
    a = {"roadmap_match_source": "stage_spec", "roadmap_major_step_index": 1}
    b = {"roadmap_match_source": "stage_spec", "roadmap_major_step_index": 2}
    c = {"roadmap_match_source": "stage_spec", "roadmap_major_step_index": 4}
    assert is_roadmap_planned_neighbor_pair(a, b) is True
    assert is_roadmap_planned_neighbor_pair(a, c) is False
    assert is_roadmap_planned_neighbor_pair({"exercise_id": 1}, b) is False


def test_detect_path_gaps_skips_roadmap_neighbors():
    brief = build_semantic_brief("Mae Geri")
    steps = [
        {
            "exercise_id": 1,
            "title": "A",
            "roadmap_match_source": "stage_spec",
            "roadmap_major_step_index": 0,
        },
        {
            "exercise_id": 2,
            "title": "B",
            "roadmap_match_source": "stage_spec",
            "roadmap_major_step_index": 1,
        },
    ]

    class _FakeCur:
        def execute(self, *args, **kwargs):
            return None

        def fetchall(self):
            return []

        def fetchone(self):
            return {"title": "X", "summary": "", "goal": ""}

    gaps = detect_path_gaps(_FakeCur(), steps, brief=brief, roadmap_first=True)
    assert gaps == []


def test_detect_path_gaps_skips_empty_slots():
    """Graph-Bewertung: leere Slots dürfen keinen 500er durch Übergangs-Lücken auslösen."""
    brief = build_semantic_brief("Mawashi Geri Kumite")
    steps = [
        {
            "exercise_id": 10,
            "title": "Stand",
            "roadmap_major_step_index": 0,
        },
        {
            "exercise_id": None,
            "title": "(leer: Slot 2)",
            "is_ai_proposal": True,
            "roadmap_major_step_index": 1,
        },
        {
            "exercise_id": 11,
            "title": "Anwendung",
            "roadmap_major_step_index": 2,
        },
    ]

    class _FakeCur:
        def execute(self, *args, **kwargs):
            return None

        def fetchall(self):
            return []

        def fetchone(self):
            return {"title": "X", "summary": "", "goal": ""}

    gaps = detect_path_gaps(_FakeCur(), steps, brief=brief, roadmap_first=True)
    assert isinstance(gaps, list)


def test_apply_llm_path_reorder_invalid_ignored():
    steps = [{"exercise_id": 1}, {"exercise_id": 2}]
    reordered, applied, _ = apply_llm_path_reorder(steps, {"ordered_step_indices": [0, 0]})
    assert applied is False
    assert reordered == steps