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- Updated `PROJECT_STATUS.md` to reflect the addition of the Planning AI Progression Graph and its context in the roadmap. - Enhanced `DOMAIN_MODEL.md` with details on the new `planning_catalog_context` features, allowing trainers to manage curriculum stages and context. - Added tests in `test_planning_catalog_context.py` to validate the separation of LLM highlights from fix hints during QA processes. - Updated `HANDOVER.md` and `PLANNING_KI_ROADMAP.md` to reflect the latest app version and improvements in the planning context. - Enhanced frontend components to support the new planning catalog context, including updates to `ExerciseProgressionPathBuilder` and `ProgressionGraphEditor`. - Bumped version to 0.8.233 to reflect the new features and improvements.
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
"""Tests Katalog-Kontext für Progressionsgraph-Matching."""
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from planning_catalog_context import (
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ProgressionPlanningCatalogContext,
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PlanningCatalogContextItem,
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catalog_context_has_items,
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merge_catalog_context_into_target,
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)
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from planning_exercise_profiles import PlanningTargetProfile
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def test_catalog_context_has_items():
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assert not catalog_context_has_items(None)
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assert not catalog_context_has_items(ProgressionPlanningCatalogContext())
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assert catalog_context_has_items(
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ProgressionPlanningCatalogContext(
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focus_areas=[PlanningCatalogContextItem(id=3, is_primary=True)],
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)
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)
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def test_merge_catalog_context_into_target_sets_focus():
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base = PlanningTargetProfile(sources=["query_only"])
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merged = merge_catalog_context_into_target(
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base,
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ProgressionPlanningCatalogContext(
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focus_areas=[PlanningCatalogContextItem(id=7, is_primary=True)],
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training_types=[PlanningCatalogContextItem(id=2, is_primary=True)],
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),
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)
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assert merged.focus_area_ids.get(7, 0) > 0.5
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assert merged.training_type_ids.get(2, 0) > 0.5
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assert "catalog_context" in merged.sources
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def test_normalize_planning_roadmap_with_catalog_context():
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from progression_graph_planning_artifact import normalize_planning_roadmap_payload
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out = normalize_planning_roadmap_payload(
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{
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"goal_query": "Deeskalation Kinder",
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"planning_catalog_context": {
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"focus_areas": [{"id": 4, "is_primary": True}],
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"target_groups": [{"id": 9, "is_primary": True}],
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},
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}
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)
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assert out["planning_catalog_context"]["focus_areas"][0]["id"] == 4
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def test_multistage_qa_splits_llm_highlights_from_fix_hints():
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from planning_path_qa_pipeline import run_multistage_path_qa
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result = run_multistage_path_qa(
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off_topic_steps=[],
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stripped_off_topic=[
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{
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"issue": "roadmap_unfilled",
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"step_index": 1,
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"reasons": ["Keine passende Übung"],
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}
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],
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gaps=[],
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llm_qa={
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"overall_ok": True,
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"quality_score": 0.88,
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"recommendations": [
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"Gute didaktische Progression",
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"Optional: Vertiefung Koordination",
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],
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},
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llm_applied=True,
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)
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hints = result["optimization_hints"]
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llm_hints = [h for h in hints if h.get("issue") == "llm_recommendation"]
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fix_hints = [h for h in hints if h.get("issue") != "llm_recommendation"]
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assert len(llm_hints) >= 2
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assert any(h.get("issue") == "roadmap_unfilled" for h in fix_hints)
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assert result["qa_tiers"][2]["recommendations"][0].startswith("Gute didaktische")
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