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- Introduced `planning_catalog_context` to `ProgressionPathSuggestRequest` for improved handling of catalog-related data during path suggestions. - Implemented `_resolve_planning_catalog_context` to retrieve and validate the planning catalog context, enhancing the robustness of the suggestion process. - Updated `_build_path_target_profile` to incorporate catalog context, enriching target profiles with relevant planning data. - Enhanced frontend components in `ProgressionGraphEditor` to manage and display planning catalog context, including new selection options for focus areas, style directions, training types, and target groups. - Added utility functions for parsing and transforming planning catalog context data for API interactions. - Bumped version to 0.8.233 to reflect the new features and improvements.
48 lines
1.7 KiB
Python
48 lines
1.7 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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