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- Introduced new functions to load exercise goals and variant names in chunks, improving data retrieval efficiency. - Integrated semantic scoring into the ranking logic, allowing for more nuanced exercise suggestions based on semantic relevance. - Updated the planning exercise suggestion process to include semantic brief handling, enriching the context for exercise recommendations. - Adjusted the retrieval phase to incorporate dynamic retrieval weights based on semantic strength, enhancing the overall suggestion accuracy. - Incremented version to 0.8.186 and updated changelog to reflect these significant enhancements in planning AI functionality.
17 lines
581 B
Python
17 lines
581 B
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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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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