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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.
68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
"""Tests Planungs-KI Phase E — Semantik-Schicht."""
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from planning_exercise_semantics import (
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apply_dynamic_retrieval_weights,
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build_semantic_brief,
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score_exercise_semantic_relevance,
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step_retrieval_query,
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)
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def test_build_semantic_brief_mae_geri():
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brief = build_semantic_brief(
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"Von Erlernen bis zur Perfektion, des Fußtritts Mae Geri"
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)
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assert brief.primary_topic == "mae geri"
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assert "mae geri" in brief.must_phrases
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assert "mawashi geri" in brief.exclude_phrases
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assert brief.semantic_strength >= 0.8
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assert "einstieg" in brief.development_arc or "perfektion" in brief.development_arc
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def test_semantic_score_prefers_mae_over_mawashi():
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brief = build_semantic_brief("Mae Geri Perfektion")
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mae_score, _ = score_exercise_semantic_relevance(
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title="Mae Geri — Frontkick Grundstellung",
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summary="Frontkick von vorn",
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goal="Sauberer Mae Geri",
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variant_names=[],
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brief=brief,
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)
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mawashi_score, _ = score_exercise_semantic_relevance(
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title="Mawashi Geri — Rundkick",
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summary="Rundkick Technik",
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goal="Mawashi Geri Höhe",
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variant_names=[],
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brief=brief,
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)
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assert mae_score > mawashi_score
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def test_dynamic_weights_boost_semantic_for_query_only():
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brief = build_semantic_brief("Mae Geri bis Perfektion")
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base = {
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"fulltext": 0.45,
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"semantic": 0.0,
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"progression": 0.08,
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"skill": 0.08,
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"plan": 0.08,
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"profile": 0.15,
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"repeat_unit": -0.3,
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"repeat_group": -0.15,
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}
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out = apply_dynamic_retrieval_weights(
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base,
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brief,
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scenario="free_search",
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has_planning_reference=False,
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)
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assert out["semantic"] > 0.25
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assert out["fulltext"] < base["fulltext"]
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def test_step_retrieval_query_carries_topic_and_phase():
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brief = build_semantic_brief("Mae Geri von Einstieg bis Perfektion")
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q0 = step_retrieval_query(brief, brief.retrieval_query, 0, 5)
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q4 = step_retrieval_query(brief, brief.retrieval_query, 4, 5)
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assert "mae geri" in q0.lower()
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assert q0 != q4
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