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- Introduced a roadmap-first approach for the planning AI, allowing for a structured progression graph that aligns with the overall project roadmap. - Added new functionality to strip off-topic steps from exercise paths, improving the relevance of generated exercise suggestions. - Implemented a detailed goal text generation for AI proposals, enhancing the context provided for new exercises. - Updated the ExerciseProgressionPathBuilder component to support new features, including roadmap previews and improved focus area handling. - Incremented application version to 0.8.205 and updated database schema version to 20260606086 to reflect these changes.
94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
"""Tests Planungs-KI Phase E3 — Lücken-Angebote und Off-Topic."""
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from planning_exercise_path_ai_fill import build_gap_fill_goal_text, collect_gap_fill_specs
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from planning_exercise_path_qa import parse_llm_suggested_new_exercises, strip_off_topic_steps_from_path
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from planning_exercise_semantics import build_semantic_brief
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def test_parse_llm_suggested_new_exercises():
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brief = build_semantic_brief("Mae Geri Perfektion")
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llm_qa = {
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"suggested_new_exercises": [
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{
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"title_hint": "Mae Geri Kraft am Sandsack",
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"sketch": "Kraft und Schnelligkeit",
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"phase": "vertiefung",
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"insert_after_step_index": 1,
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"rationale": "Zwischenschritt",
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}
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]
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}
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specs = parse_llm_suggested_new_exercises(llm_qa, brief=brief, step_count=5)
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assert len(specs) == 1
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assert specs[0]["insert_after_index"] == 1
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assert "Mae Geri" in specs[0]["title_hint"]
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def test_collect_gap_fill_specs_off_topic_and_unfilled():
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brief = build_semantic_brief("Mae Geri Perfektion")
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steps = [
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{"exercise_id": 1, "title": "Mae Geri Kihon"},
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{"exercise_id": 2, "title": "Präzision"},
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{"exercise_id": 3, "title": "One Leg Squat"},
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{"exercise_id": 4, "title": "Gleichgewichtstritt"},
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]
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unfilled = [
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{
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"from_exercise_id": 2,
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"to_exercise_id": 3,
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"expected_phase": "vertiefung",
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"from_title": "Präzision",
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"to_title": "One Leg Squat",
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}
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]
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off_topic = [
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{
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"step_index": 2,
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"exercise_id": 3,
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"title": "One Leg Squat",
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"expected_phase": "vertiefung",
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}
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]
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specs = collect_gap_fill_specs(
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steps=steps,
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unfilled_gaps=unfilled,
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off_topic_steps=off_topic,
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llm_specs=[],
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brief=brief,
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goal_query="Mae Geri Perfektion",
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)
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sources = {s["source"] for s in specs}
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assert "unfilled_gap" in sources
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assert "off_topic" in sources
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off = next(s for s in specs if s["source"] == "off_topic")
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assert off["replace_step_index"] == 2
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assert off["insert_after_index"] == 1
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def test_strip_off_topic_steps_from_path():
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steps = [
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{"exercise_id": 1, "title": "A"},
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{"exercise_id": 2, "title": "B"},
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{"exercise_id": 3, "title": "One Leg Squat"},
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{"exercise_id": 4, "title": "D"},
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]
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off_topic = [{"step_index": 2, "title": "One Leg Squat", "exercise_id": 3}]
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out, removed = strip_off_topic_steps_from_path(steps, off_topic)
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assert len(out) == 3
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assert len(removed) == 1
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assert removed[0]["removed_title"] == "One Leg Squat"
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assert [s["exercise_id"] for s in out] == [1, 2, 4]
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def test_build_gap_fill_goal_text_includes_topic():
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brief = build_semantic_brief("Mae Geri Perfektion")
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text = build_gap_fill_goal_text(
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goal_query="Mae Geri Perfektion",
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brief=brief,
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spec={"phase": "anwendung", "rationale": "Fehlt Kombinationstraining"},
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step_a={"title": "Kihon"},
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step_b={"title": "Kumite"},
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)
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assert "Mae Geri" in text or "mae geri" in text.lower()
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assert "anwendung" in text
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assert "Kihon" in text
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