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- Introduced new functions for handling exercise visibility and retrieval based on progression graph context, including `fetch_exercise_rows_by_ids_for_graph`. - Updated `_load_supplemental_exercise_rows` to incorporate graph visibility rules, improving the accuracy of exercise retrieval. - Enhanced `_run_path_step_retrieval` to utilize preloaded supplemental exercise rows, optimizing performance and clarity in path step processing. - Added `exercise_title_equivalent_to_stage_goal` function to improve title matching against learning goals, enhancing exercise relevance. - Updated tests to validate new retrieval logic and title equivalence functionality, ensuring robustness in exercise selection processes.
88 lines
3.0 KiB
Python
88 lines
3.0 KiB
Python
"""Tests Planungs-KI Phase C3/E/F — Pfad-Vorschläge."""
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from planning_exercise_path_builder import (
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EvaluateStepPayload,
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ProgressionPathSuggestRequest,
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_annotate_roadmap_step,
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_hit_to_path_step,
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_pick_best_path_hit,
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_supplemental_exercise_ids_from_body,
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)
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from planning_progression_roadmap import MajorStep, StageSpecArtifact
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class _FakeCur:
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def execute(self, *_args, **_kwargs):
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return None
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def fetchall(self):
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return []
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def test_supplemental_boost_includes_slot_assignments_and_retrieval_boost():
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body = ProgressionPathSuggestRequest(
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query="Mawashi Geri Progression",
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slot_assignments=[
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EvaluateStepPayload(exercise_id=99, roadmap_major_step_index=0),
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],
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retrieval_boost_exercise_ids=[42, 7],
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)
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ids = _supplemental_exercise_ids_from_body(_FakeCur(), body)
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assert 99 in ids
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assert 42 in ids
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assert 7 in ids
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def test_pick_next_path_hit_skips_used():
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hits = [{"id": 1, "title": "A", "semantic_score": 0.2}, {"id": 2, "title": "B", "semantic_score": 0.2}, {"id": 3, "title": "C", "semantic_score": 0.2}]
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assert _pick_best_path_hit(hits, {1})["id"] == 2
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assert _pick_best_path_hit(hits, {1, 2, 3}) is None
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def test_hit_to_path_step_maps_variant():
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step = _hit_to_path_step(
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{
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"id": 10,
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"title": "Test",
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"score": 0.8,
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"reasons": ["Graph"],
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"suggested_variant_id": 7,
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"suggested_variant_name": "Leicht",
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"variants": [{"id": 7, "variant_name": "Leicht"}],
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}
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)
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assert step["exercise_id"] == 10
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assert step["variant_id"] == 7
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assert step["suggested_variant_name"] == "Leicht"
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def test_annotate_roadmap_step_adds_metadata():
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spec = StageSpecArtifact(major_step_index=1, learning_goal="Grundstellung Mae Geri")
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major = MajorStep(index=1, phase="grundlage", learning_goal=spec.learning_goal, consolidates=["m1"])
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step = _annotate_roadmap_step(
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{"exercise_id": 5, "title": "Test", "reasons": ["Bibliothek"]},
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stage_spec=spec,
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major_step=major,
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)
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assert step["roadmap_major_step_index"] == 1
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assert step["roadmap_phase"] == "grundlage"
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assert step["roadmap_match_source"] == "stage_spec"
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assert any("Roadmap:" in r for r in step["reasons"])
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def test_annotate_roadmap_step_adds_skill_expectations():
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spec = StageSpecArtifact(major_step_index=0, learning_goal="Timing und Distanz")
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step = _annotate_roadmap_step(
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{"exercise_id": 5, "title": "Test", "reasons": []},
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stage_spec=spec,
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major_step=None,
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skill_expectations={
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"scope": "progression_stage",
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"expected_skills": [
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{"skill_id": 2, "skill_name": "Timing", "weight": 0.9},
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{"skill_id": 3, "skill_name": "Distanz", "weight": 0.8},
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],
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},
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)
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assert step["skill_expectations"]["expected_skills"][0]["skill_name"] == "Timing"
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assert any("Fähigkeiten:" in r for r in step["reasons"])
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