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- Replaced the manual path selection logic with a new `pick_best_path_hit` function to streamline the process of selecting the best exercise based on semantic scores and gating criteria. - Updated the semantic gating logic to apply a soft penalty for off-topic exercises, improving the flexibility of exercise selection. - Enhanced the handling of title, summary, and goal parameters in semantic checks to ensure more accurate relevance assessments. - Incremented version to 0.8.189 and updated changelog to reflect these improvements in planning AI functionality.
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
"""Tests Planungs-KI Phase E — Pfad-QA."""
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from planning_exercise_path_builder import _pick_best_path_hit
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from planning_exercise_semantics import build_semantic_brief
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from planning_exercise_path_qa import apply_llm_path_reorder
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def test_pick_best_path_hit_prefers_semantic_score():
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brief = build_semantic_brief("Mae Geri Perfektion")
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hits = [
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{"id": 1, "title": "Mawashi", "score": 0.9, "semantic_score": 0.1},
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{"id": 2, "title": "Mae Geri", "score": 0.75, "semantic_score": 0.85},
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]
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chosen = _pick_best_path_hit(hits, set(), semantic_brief=brief)
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assert chosen["id"] == 2
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def test_phrase_compact_match_maegeri():
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from planning_exercise_semantics import _phrase_in_blob
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assert _phrase_in_blob("mae geri", "Erlernen des Mae-Geri aus Einzelbewegungen")
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assert _phrase_in_blob("mae geri", "Maegeri Kihon")
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def test_pick_best_path_hit_fallback_title_only_in_summary():
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from planning_exercise_semantics import pick_best_path_hit
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brief = build_semantic_brief("Mae Geri Perfektion")
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hits = [
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{
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"id": 1,
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"title": "Kumite Stellungen",
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"summary": "",
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"score": 0.9,
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"semantic_score": 0.02,
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},
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{
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"id": 2,
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"title": "Einzelbewegungen",
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"summary": "Schrittweise Erlernen des Mae Geri",
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"score": 0.5,
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"semantic_score": 0.08,
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},
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]
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chosen = pick_best_path_hit(hits, set(), semantic_brief=brief)
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assert chosen is not None
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assert int(chosen["id"]) == 2
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def test_pick_best_path_hit_skips_used():
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hits = [{"id": 1, "title": "A", "score": 0.5, "semantic_score": 0.5}]
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assert _pick_best_path_hit(hits, {1}) is None
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def test_apply_llm_path_reorder_permutation():
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steps = [{"exercise_id": 1}, {"exercise_id": 2}, {"exercise_id": 3}]
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reordered, applied, notes = apply_llm_path_reorder(
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steps,
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{"ordered_step_indices": [0, 2, 1], "sequence_notes": ["Vertiefung vor Anwendung"]},
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)
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assert applied is True
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assert [s["exercise_id"] for s in reordered] == [1, 3, 2]
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assert notes
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def test_apply_llm_path_reorder_invalid_ignored():
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steps = [{"exercise_id": 1}, {"exercise_id": 2}]
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reordered, applied, _ = apply_llm_path_reorder(steps, {"ordered_step_indices": [0, 0]})
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assert applied is False
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assert reordered == steps
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