Implement Pruning of Filled Steps from Roadmap Unfilled

- Introduced `_prune_filled_from_roadmap_unfilled` function to remove steps with filled exercises from the unfilled roadmap, preventing outdated references.
- Updated `_run_roadmap_rematch_loop` to incorporate the new pruning logic, ensuring only relevant unfilled steps are retained during rematching.
- Added tests for the pruning function to validate its behavior with various step scenarios.
- Bumped version to 0.8.232 to reflect the new functionality and improvements.

backend/planning_exercise_path_builder.py

@@ -1518,6 +1518,31 @@ def _merge_rematch_unfilled(
     return kept
 
 
+def _prune_filled_from_roadmap_unfilled(
+    steps: Sequence[Mapping[str, Any]],
+    roadmap_unfilled: List[Tuple[int, StageSpecArtifact]],
+) -> List[Tuple[int, StageSpecArtifact]]:
+    """Entfernt Stufen mit Bibliotheks-Treffer — verhindert veraltete roadmap_unfilled-Hinweise."""
+    filled_majors: Set[int] = set()
+    for raw in steps:
+        if raw.get("exercise_id") is None:
+            continue
+        midx = raw.get("roadmap_major_step_index")
+        if midx is None:
+            continue
+        try:
+            filled_majors.add(int(midx))
+        except (TypeError, ValueError):
+            continue
+    if not filled_majors:
+        return roadmap_unfilled
+    return [
+        item
+        for item in roadmap_unfilled
+        if int(item[1].major_step_index) not in filled_majors
+    ]
+
+
 def _run_roadmap_rematch_loop(
     cur,
     *,
@@ -1683,6 +1708,7 @@ def _run_roadmap_rematch_loop(
 
         current_stripped = prune_stripped_after_rematch(current_stripped, round_log)
         roadmap_unfilled = _merge_rematch_unfilled(roadmap_unfilled, rematch_new_unfilled)
+        roadmap_unfilled = _prune_filled_from_roadmap_unfilled(steps, roadmap_unfilled)
         use_initial_off_topic = False
 
         off_topic_steps = detect_off_topic_steps(
@@ -1721,6 +1747,8 @@ def _run_roadmap_rematch_loop(
             goal_query=goal_query,
         )
 
+    roadmap_unfilled = _prune_filled_from_roadmap_unfilled(steps, roadmap_unfilled)
+
     return (
         steps,
         rematch_log,

backend/tests/test_planning_path_rematch.py

@@ -223,3 +223,15 @@ def test_rematch_unfilled_leaves_placeholder_step():
     assert slot1["roadmap_match_source"] == "unfilled"
     assert log[0]["action"] == "rematch_unfilled"
     assert len(unfilled) == 1
+
+
+def test_prune_filled_from_roadmap_unfilled():
+    from planning_exercise_path_builder import _prune_filled_from_roadmap_unfilled
+
+    spec = StageSpecArtifact(major_step_index=5, learning_goal="Zielgenauigkeit")
+    steps = [{"exercise_id": 99, "roadmap_major_step_index": 5}]
+    kept = _prune_filled_from_roadmap_unfilled(steps, [(4, spec)])
+    assert kept == []
+    unfilled_steps = [{"exercise_id": None, "roadmap_major_step_index": 5}]
+    kept2 = _prune_filled_from_roadmap_unfilled(unfilled_steps, [(4, spec)])
+    assert len(kept2) == 1

backend/version.py

@@ -1,6 +1,6 @@
 # Shinkan Jinkendo Version Information
 
-APP_VERSION = "0.8.231"
+APP_VERSION = "0.8.232"
 BUILD_DATE = "2026-05-22"
 DB_SCHEMA_VERSION = "20260607090"
 
@@ -38,7 +38,7 @@ MODULE_VERSIONS = {
     "skill_profiles": "1.0.0",  # Phase 3: gewichtetes Fähigkeiten-Profil + skill-discovery/suggestions
     "methods": "0.1.0",
     "exercises": "2.37.1",  # KI-Endpoints: feature_usage nach ai_calls consume
-    "planning_exercise_suggest": "0.23.6",  # Gap-Angebote nach Rematch; LLM-QA auf finalem Pfad; Post-Match-Gate
+    "planning_exercise_suggest": "0.23.7",  # roadmap_unfilled nach Rematch-Treffer bereinigen
     "training_units": "0.4.0",  # POST .../publish-to-framework: Ablauf aus geplanter Einheit → Rahmen-Slot-Blueprint
     "training_programs": "0.1.0",
     "planning": "0.15.0",  # Vorlagen: Strukturvorschau, Bearbeiten inkl. Split-Sessions + Beschreibung