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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.
26 lines
935 B
Python
26 lines
935 B
Python
"""Tests Planungs-KI Phase C3/E — Pfad-Vorschläge."""
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from planning_exercise_path_builder import _pick_best_path_hit, _hit_to_path_step
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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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