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- Introduced path reordering functionality using LLM with `ordered_step_indices`, allowing for dynamic adjustment of exercise progression paths. - Added AI gap filling capabilities, enabling the system to propose new exercises when unbridgeable gaps are detected. - Updated the backend to support new request parameters for path reordering and AI gap filling. - Enhanced frontend components to reflect these new features, including alerts for AI proposals and adjustments in exercise display. - Incremented version to 0.8.187 and updated changelog to document these significant enhancements in planning AI functionality.
36 lines
1.3 KiB
Python
36 lines
1.3 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_path_qa import apply_llm_path_reorder
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def test_pick_best_path_hit_prefers_semantic_score():
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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())
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assert 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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