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- Introduced a roadmap-first approach for retrieval, allowing for structured exercise suggestions based on stage specifications and major steps. - Added functionality to generate gap-fill offers for unfilled roadmap stages, improving the relevance of exercise recommendations. - Updated the `ExerciseProgressionPathBuilder` to support the new roadmap-first feature, enhancing user experience with clearer exercise paths. - Incremented application version to 0.8.206 and updated the database schema version to reflect these changes.
100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
"""Tests Planungs-KI Phase F — Progressions-Roadmap Pipeline."""
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from planning_progression_roadmap import (
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PROMPT_SLUG_GOAL_ANALYSIS,
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PROMPT_SLUG_ROADMAP,
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PROMPT_SLUG_STAGE_SPEC,
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MajorStep,
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StageSpecArtifact,
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build_goal_analysis,
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build_roadmap_unfilled_gap_specs,
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consolidate_micro_to_major,
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develop_micro_objectives,
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progression_roadmap_to_api_dict,
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resolve_step_exercise_kind_filter,
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run_progression_roadmap_pipeline,
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stage_spec_exercise_kind_filter,
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stage_spec_retrieval_query,
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)
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from planning_exercise_semantics import build_semantic_brief
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def test_run_progression_roadmap_pipeline_major_step_count():
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ctx = run_progression_roadmap_pipeline(
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"Von Erlernen bis zur Perfektion des Fußtritts Mae Geri",
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max_steps=5,
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)
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assert ctx.roadmap is not None
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assert len(ctx.roadmap.major_steps) == 5
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assert len(ctx.roadmap.micro_objectives) >= 6
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assert len(ctx.stage_specs) == 5
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assert ctx.goal_analysis is not None
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assert "Mae" in ctx.goal_analysis.primary_topic or "mae" in ctx.goal_analysis.primary_topic.lower()
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def test_consolidate_micro_to_major_reduces_count():
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brief = build_semantic_brief("Mae Geri")
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ga = build_goal_analysis("Mae Geri Perfektion", brief)
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micro = develop_micro_objectives(brief, goal_analysis=ga, min_count=8)
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majors, notes = consolidate_micro_to_major(micro, max_steps=5)
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assert len(majors) == 5
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if len(micro) > 5:
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assert notes
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assert all(m.learning_goal for m in majors)
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def test_major_steps_have_learning_goals():
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ctx = run_progression_roadmap_pipeline("Mae Geri Grundlagen", max_steps=3)
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for step in ctx.roadmap.major_steps:
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assert step.learning_goal.strip()
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assert step.consolidates
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def test_stage_spec_retrieval_query_includes_learning_goal():
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brief = build_semantic_brief("Mae Geri Perfektion")
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spec = StageSpecArtifact(
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major_step_index=1,
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learning_goal="Koordination und Präzision vertiefen",
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load_profile=["präzision"],
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exercise_type="kihon_einzel",
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)
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major = MajorStep(index=1, phase="vertiefung", learning_goal=spec.learning_goal, consolidates=["m3"])
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q = stage_spec_retrieval_query(
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semantic_brief=brief,
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goal_query="Mae Geri Perfektion",
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stage_spec=spec,
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major_step=major,
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)
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assert "vertiefung" in q.lower()
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assert "Koordination" in q or "Präzision" in q
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def test_stage_spec_exercise_kind_filter_maps_combination():
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spec = StageSpecArtifact(major_step_index=0, exercise_type="kombination")
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assert stage_spec_exercise_kind_filter(spec) == ["combination"]
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assert resolve_step_exercise_kind_filter(spec, ["simple"]) == ["simple"]
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def test_build_roadmap_unfilled_gap_specs():
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brief = build_semantic_brief("Mae Geri")
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spec = StageSpecArtifact(major_step_index=2, learning_goal="Anwendung im Partnerdrill")
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major = MajorStep(index=2, phase="anwendung", learning_goal=spec.learning_goal, consolidates=["m5"])
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specs = build_roadmap_unfilled_gap_specs(
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unfilled_specs=[(2, spec)],
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major_steps_by_index={2: major},
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steps=[{"exercise_id": 1, "title": "A"}, {"exercise_id": 2, "title": "B"}],
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brief=brief,
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goal_query="Mae Geri",
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)
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assert len(specs) == 1
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assert specs[0]["source"] == "roadmap_unfilled"
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assert specs[0]["phase"] == "anwendung"
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def test_api_dict_exposes_prompt_slug_catalog():
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ctx = run_progression_roadmap_pipeline("Mae Geri", max_steps=3, include_llm_roadmap=False)
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api = progression_roadmap_to_api_dict(ctx)
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assert api["prompt_slug_catalog"]["goal_analysis"] == PROMPT_SLUG_GOAL_ANALYSIS
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assert api["prompt_slug_catalog"]["roadmap"] == PROMPT_SLUG_ROADMAP
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assert api["prompt_slug_catalog"]["stage_spec"] == PROMPT_SLUG_STAGE_SPEC
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assert api["prompt_slugs"] == []
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