Enhance Planning Exercise Retrieval and Suggestion with Semantic Features

- Introduced new functions to load exercise goals and variant names in chunks, improving data retrieval efficiency.
- Integrated semantic scoring into the ranking logic, allowing for more nuanced exercise suggestions based on semantic relevance.
- Updated the planning exercise suggestion process to include semantic brief handling, enriching the context for exercise recommendations.
- Adjusted the retrieval phase to incorporate dynamic retrieval weights based on semantic strength, enhancing the overall suggestion accuracy.
- Incremented version to 0.8.186 and updated changelog to reflect these significant enhancements in planning AI functionality.

.claude/docs/working/PLANNING_EXERCISE_SUGGEST_CONTEXT.md

@@ -2,7 +2,7 @@
 
 **Version:** 0.2  
 **Datum:** 2026-05-23  
-**Status:** P0–P2 ✅ · Phase A/B/B2 ✅ · **Phase C1–C3 ✅** (Progressionsgraph + Varianten + Pfad-Builder)  
+**Status:** P0–P2 ✅ · Phase A/B/B2 ✅ · **Phase C1–C3 ✅** · **Phase E ✅** (Semantik + Pfad-QA)  
 **Bezüge:** `AI_PLANNING_KI_MULTISTAGE_FORECAST.md` · `AI_PROMPT_TARGET_ARCHITECTURE.md` · `SKILL_SCORING_SPEC.md` · `TRAINING_FRAMEWORK_SPEC.md` §3 (Progressionsgraph)
 
 ---
@@ -191,6 +191,7 @@ Wenn `hits` leer oder Trainer wählt „Mit KI anlegen“:
 | **C1** | Graph auto-match + variantenbewusste Nachfolger | ✅ **0.8.183** |
 | **C2** | Varianten in Trefferliste / Picker | ✅ **0.8.184** |
 | **C3** | Graph-Builder (Ziel → Pfad → speichern) | ✅ **0.8.185** |
+| **E** | Semantik-Schicht + Pfad-QA (Lücken/Brücken/LLM-QS) | ✅ **0.8.186** |
 | **D** | Neu-Anlage: Pack an `suggestExerciseAi` | 🔲 |
 
 ---
@@ -446,4 +447,36 @@ Treffer: optional `hits[].suggested_variant_id`.
 
 ---
 
-## 22. Backlog (offen)
+## 22. Phase E — Semantik-Schicht + Pfad-QA (0.8.186) ✅
+
+### Semantic Brief (`planning_exercise_semantics.py`)
+
+Parallel zum Katalog-Overlay — **nicht ersetzend**:
+
+| Feld | Bedeutung |
+|------|-----------|
+| `primary_topic` | z. B. `mae geri` |
+| `must_phrases` / `exclude_phrases` | Phrasen-Match in Titel/Ziel/Varianten |
+| `development_arc` | einstieg → … → perfektion |
+| `semantic_strength` | 0–1 — steuert dynamisches Blend im Hybrid-Score |
+| `retrieval_query` | fokussierte Volltext-Query (nicht ganzer Satz) |
+
+Optional LLM: Prompt `planning_exercise_query_semantics` (Migration **075**).
+
+**Hybrid-Score:** neuer Term `w_semantic * semantic_score` — Profil/Volltext werden bei hoher `semantic_strength` relativ abgeschwächt.
+
+### Pfad-QA (`planning_exercise_path_qa.py`)
+
+Nach Pfad-Bildung:
+
+1. **Lücken-Messung** zwischen benachbarten Schritten (Skill-Jaccard + Semantik zum erwarteten Phasen-Segment)
+2. **Brücken-Übungen** bei großen Lücken (zusätzliche Schritte, markiert `is_bridge`)
+3. **LLM-QS** (Prompt `planning_exercise_path_qa`): Reihenfolge, Themen-Abdeckung, Empfehlungen
+
+**API-Erweiterung** `progression-path-suggest`: `include_path_qa`, `include_llm_path_qa` · Response: `semantic_brief_summary`, `path_qa`.
+
+**Pfad-Schritte:** Semantic Brief + Entwicklungsphase in **allen** Schritten (nicht nur Schritt 1).
+
+---
+
+## 23. Backlog (offen)

backend/migrations/075_ai_prompt_planning_semantics_path_qa.sql

@@ -0,0 +1,89 @@
+-- Migration 075: Planungs-KI Phase E — Semantik-Enrichment + Pfad-QA Prompts
+
+INSERT INTO ai_prompts (
+    slug, display_name, description, template,
+    category, output_format, output_schema, is_system_default, default_template, active, sort_order
+)
+SELECT
+    'planning_exercise_query_semantics',
+    'Planungs-Übungssuche Semantik',
+    'Erweitert deterministisches Semantic Brief um must/exclude phrases und Entwicklungsbogen.',
+    $t$Du bist Assistent für Kampfsport-Trainer bei der semantischen Analyse von Planungs-Anfragen.
+
+Ziel: JSON für ein Semantic Brief — präzise Kernbegriffe, Ausschlüsse, Entwicklungsbogen.
+Nutze das bestehende Brief als Basis; ergänze/verfeinere, ersetze aber keine eindeutige Technik-Identität.
+
+Anfrage: {{search_query}}
+Bestehendes Brief (deterministisch): {{semantic_brief_json}}
+
+Regeln:
+- must_phrases: konkrete Technik-/Themen-Phrasen aus der Anfrage (z. B. "mae geri", nicht nur "geri")
+- exclude_phrases: konkurrierende Techniken/Themen, die NICHT gemeint sind
+- development_arc: geordnete Phasen aus: einstieg, grundlage, vertiefung, anwendung, perfektion
+- semantic_strength: 0.0–1.0 (höher bei spezifischer Technik/Thema)
+- primary_topic: Hauptthema in wenigen Worten
+- topic_type: technique | focus | method | skill | general
+
+Antworte NUR mit JSON:
+{
+  "primary_topic": "Mae Geri",
+  "topic_type": "technique",
+  "must_phrases": ["mae geri"],
+  "exclude_phrases": ["mawashi geri", "sakuto geri"],
+  "development_arc": ["einstieg", "grundlage", "vertiefung", "perfektion"],
+  "semantic_strength": 0.9,
+  "rationale": "Kurz auf Deutsch"
+}$t$,
+    'training',
+    'json',
+    '{"type":"object","properties":{"must_phrases":{"type":"array"},"exclude_phrases":{"type":"array"},"development_arc":{"type":"array"},"semantic_strength":{"type":"number"}}}'::jsonb,
+    true,
+    NULL,
+    true,
+    12
+WHERE NOT EXISTS (SELECT 1 FROM ai_prompts WHERE slug = 'planning_exercise_query_semantics');
+
+INSERT INTO ai_prompts (
+    slug, display_name, description, template,
+    category, output_format, output_schema, is_system_default, default_template, active, sort_order
+)
+SELECT
+    'planning_exercise_path_qa',
+    'Planungs-Pfad QA',
+    'Semantische Qualitätsprüfung eines vorgeschlagenen Übungspfads inkl. Lücken und Brücken.',
+    $t$Du bist Assistent für Kampfsport-Trainer und prüfst einen vorgeschlagenen Übungspfad.
+
+Ziel-Anfrage: {{goal_query}}
+Semantic Brief: {{semantic_brief_json}}
+Schritte (JSON): {{steps_json}}
+Erkannte Lücken: {{gaps_json}}
+Eingefügte Brücken: {{bridge_inserts_json}}
+
+Prüfe:
+1. Deckt der Pfad das Hauptthema der Anfrage ab (nicht nur Oberbegriffe)?
+2. Ist die Reihenfolge didaktisch sinnvoll (Einstieg → Vertiefung → Ziel)?
+3. Sind Sprünge zwischen benachbarten Schritten zu groß?
+4. Sind Brücken-Übungen sinnvoll oder überflüssig?
+5. Fehlen wichtige Zwischenschritte?
+
+Antworte NUR mit JSON:
+{
+  "overall_ok": true,
+  "quality_score": 0.85,
+  "topic_coverage": "Kurz: wie gut das Hauptthema abgedeckt ist",
+  "issues": ["…"],
+  "sequence_notes": ["…"],
+  "recommendations": ["…"]
+}$t$,
+    'training',
+    'json',
+    '{"type":"object","required":["overall_ok"],"properties":{"overall_ok":{"type":"boolean"},"quality_score":{"type":"number"},"issues":{"type":"array"},"sequence_notes":{"type":"array"},"recommendations":{"type":"array"}}}'::jsonb,
+    true,
+    NULL,
+    true,
+    13
+WHERE NOT EXISTS (SELECT 1 FROM ai_prompts WHERE slug = 'planning_exercise_path_qa');
+
+UPDATE ai_prompts SET default_template = template
+WHERE slug IN ('planning_exercise_query_semantics', 'planning_exercise_path_qa')
+  AND (default_template IS NULL OR TRIM(default_template) = '');

backend/planning_exercise_path_builder.py

@@ -1,18 +1,33 @@
 """
-Planungs-KI Phase C3: Pfad-Vorschläge für Progressionsgraphen.
+Planungs-KI Phase C3/E: Pfad-Vorschläge für Progressionsgraphen.
 
-Ziel-Freitext → iterative Hybrid-Suche (Schritt 1 mit optional LLM-Profil, Folgeschritte deterministisch).
+Ziel-Freitext → semantisch gewichtete Schritte → Lücken/Brücken → optional LLM-QA.
 """
 from __future__ import annotations
 
-from typing import Any, Dict, List, Optional, Set, Tuple
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple
 
 from fastapi import HTTPException
 from pydantic import BaseModel, Field
 
 from tenant_context import TenantContext, library_content_visibility_sql
 from planning_exercise_profiles import PlanningTargetProfile
+from planning_exercise_path_qa import (
+    build_path_qa_summary,
+    detect_path_gaps,
+    insert_bridge_exercises,
+    try_llm_qa_progression_path,
+)
 from planning_exercise_retrieval import run_multistage_planning_retrieval
+from planning_exercise_semantics import (
+    PlanningSemanticBrief,
+    apply_dynamic_retrieval_weights,
+    brief_to_summary_dict,
+    build_semantic_brief,
+    step_phase_for_index,
+    step_retrieval_query,
+    try_enrich_semantic_brief_with_llm,
+)
 from planning_exercise_target_pipeline import build_planning_target_with_query_pipeline
 from planning_exercise_progression import apply_progression_context_to_pack
 from planning_exercise_suggest import (
@@ -30,23 +45,32 @@ class ProgressionPathSuggestRequest(BaseModel):
     query: str = Field(..., min_length=3, max_length=2000)
     max_steps: int = Field(default=5, ge=2, le=10)
     include_llm_intent: bool = True
+    include_path_qa: bool = True
+    include_llm_path_qa: bool = True
     progression_graph_id: Optional[int] = Field(default=None, ge=1)
     exercise_kind_any: Optional[List[str]] = None
 
 
-def _pick_next_path_hit(
+def _pick_best_path_hit(
     hits: List[Dict[str, Any]],
     used_exercise_ids: Set[int],
 ) -> Optional[Dict[str, Any]]:
+    best: Optional[Dict[str, Any]] = None
+    best_key: Tuple[float, float] = (-1.0, -1.0)
     for hit in hits:
         eid = int(hit["id"])
         if eid in used_exercise_ids:
             continue
-        return hit
-    return None
+        sem = float(hit.get("semantic_score") or 0.0)
+        score = float(hit.get("score") or 0.0)
+        key = (sem, score)
+        if key > best_key:
+            best_key = key
+            best = hit
+    return best
 
 
-def _hit_to_path_step(hit: Dict[str, Any]) -> Dict[str, Any]:
+def _hit_to_path_step(hit: Dict[str, Any], *, is_bridge: bool = False) -> Dict[str, Any]:
     raw_vid = hit.get("suggested_variant_id")
     variant_id: Optional[int] = None
     if raw_vid is not None:
@@ -56,17 +80,21 @@ def _hit_to_path_step(hit: Dict[str, Any]) -> Dict[str, Any]:
                 variant_id = vid
         except (TypeError, ValueError):
             variant_id = None
-    return {
+    step = {
         "exercise_id": int(hit["id"]),
         "variant_id": variant_id,
         "title": hit.get("title"),
         "summary": hit.get("summary"),
         "score": hit.get("score"),
+        "semantic_score": hit.get("semantic_score"),
         "reasons": list(hit.get("reasons") or []),
         "variants": hit.get("variants") or [],
         "suggested_variant_id": hit.get("suggested_variant_id"),
         "suggested_variant_name": hit.get("suggested_variant_name"),
     }
+    if is_bridge:
+        step["is_bridge"] = True
+    return step
 
 
 def _run_path_step_retrieval(
@@ -75,13 +103,22 @@ def _run_path_step_retrieval(
     tenant: TenantContext,
     goal_query: str,
     step_index: int,
+    max_steps: int,
     planned_ids: List[int],
     anchor_id: Optional[int],
     anchor_variant_id: Optional[int],
     progression_graph_id: Optional[int],
     include_llm_intent: bool,
     exercise_kind_any: Optional[List[str]],
+    semantic_brief: PlanningSemanticBrief,
+    bridge_mode: bool = False,
+    step_a: Optional[Dict[str, Any]] = None,
+    step_b: Optional[Dict[str, Any]] = None,
 ) -> Tuple[List[Dict[str, Any]], PlanningTargetProfile, Dict[str, Any], str]:
+    step_query = step_retrieval_query(semantic_brief, goal_query, step_index, max_steps)
+    if bridge_mode and step_a and step_b:
+        step_query = f"{semantic_brief.retrieval_query or goal_query} brücke zwischen schritten"
+
     pack: Dict[str, Any] = {
         "unit_id": None,
         "unit": {
@@ -94,14 +131,17 @@ def _run_path_step_retrieval(
         "group_name": None,
         "section_order_index": None,
         "section_title": None,
-        "section_guidance_notes": goal_query if step_index == 0 else None,
+        "section_guidance_notes": goal_query if step_index == 0 and not bridge_mode else step_query,
         "planned_exercise_ids": list(planned_ids),
         "anchor_exercise_id": anchor_id,
         "anchor_title": None,
         "anchor_skill_ids": sorted(_load_skill_ids_for_exercise(cur, anchor_id)),
         "group_recent_exercise_ids": [],
         "context_mode": "progression_path",
-        "has_planning_reference": bool(planned_ids or anchor_id),
+        "has_planning_reference": bool(planned_ids or anchor_id or bridge_mode),
+        "semantic_brief": semantic_brief,
+        "retrieval_query": step_query,
+        "path_step_phase": step_phase_for_index(semantic_brief, step_index, max_steps),
     }
     pack = apply_progression_context_to_pack(
         cur,
@@ -111,14 +151,12 @@ def _run_path_step_retrieval(
         anchor_variant_id=anchor_variant_id,
     )
 
-    if step_index == 0:
+    if step_index == 0 and not bridge_mode:
         heuristic_intent = resolve_planning_exercise_intent(goal_query, "free_search")
-        step_query = goal_query
     else:
         heuristic_intent = INTENT_SUGGEST_NEXT
-        step_query = "nächste sinnvolle übung im pfad"
 
-    has_plan_ref = bool(pack.get("has_planning_reference")) or step_index > 0
+    has_plan_ref = bool(pack.get("has_planning_reference"))
     pipeline_context = {
         "unit_title": None,
         "group_name": None,
@@ -142,14 +180,20 @@ def _run_path_step_retrieval(
         planned_exercise_ids=pack["planned_exercise_ids"],
         section_planned_exercise_ids=[],
         anchor_exercise_id=pack.get("anchor_exercise_id"),
-        query=goal_query if step_index == 0 else step_query,
+        query=goal_query if step_index == 0 and not bridge_mode else step_query,
         heuristic_intent=heuristic_intent,
-        include_llm_intent=include_llm_intent and step_index == 0,
+        include_llm_intent=include_llm_intent and step_index == 0 and not bridge_mode,
         context_summary=pipeline_context,
         has_planning_reference=has_plan_ref,
     )
 
-    weights = _intent_weights(intent)
+    weights = apply_dynamic_retrieval_weights(
+        _intent_weights(intent),
+        semantic_brief,
+        scenario="free_search" if step_index == 0 and not bridge_mode else "progression",
+        has_planning_reference=has_plan_ref,
+    )
+
     profile_id = tenant.profile_id
     role = tenant.global_role
     vis_sql, vis_params = library_content_visibility_sql(
@@ -163,7 +207,7 @@ def _run_path_step_retrieval(
         cur,
         vis_sql=vis_sql,
         vis_params=vis_params,
-        query=step_query if step_index > 0 else goal_query,
+        query=step_query,
         exercise_kind_any=exercise_kind_any,
         target=target_profile,
         intent=intent,
@@ -174,6 +218,45 @@ def _run_path_step_retrieval(
     return hits, target_profile, query_intent_summary, intent
 
 
+def _make_bridge_search_fn(
+    cur,
+    *,
+    tenant: TenantContext,
+    goal_query: str,
+    max_steps: int,
+    progression_graph_id: Optional[int],
+    include_llm_intent: bool,
+    exercise_kind_any: Optional[List[str]],
+    semantic_brief: PlanningSemanticBrief,
+    planned_ids: List[int],
+) -> Callable[..., List[Dict[str, Any]]]:
+    def _bridge_search(
+        step_a: Dict[str, Any],
+        step_b: Dict[str, Any],
+        _gap: Dict[str, Any],
+    ) -> List[Dict[str, Any]]:
+        hits, _, _, _ = _run_path_step_retrieval(
+            cur,
+            tenant=tenant,
+            goal_query=goal_query,
+            step_index=1,
+            max_steps=max_steps,
+            planned_ids=list(planned_ids) + [int(step_a["exercise_id"])],
+            anchor_id=int(step_a["exercise_id"]),
+            anchor_variant_id=step_a.get("variant_id"),
+            progression_graph_id=progression_graph_id,
+            include_llm_intent=include_llm_intent,
+            exercise_kind_any=exercise_kind_any,
+            semantic_brief=semantic_brief,
+            bridge_mode=True,
+            step_a=step_a,
+            step_b=step_b,
+        )
+        return hits
+
+    return _bridge_search
+
+
 def suggest_progression_path(
     cur,
     *,
@@ -189,6 +272,13 @@ def suggest_progression_path(
         raise HTTPException(status_code=400, detail="Ziel-Anfrage: mindestens 3 Zeichen")
 
     max_steps = int(body.max_steps)
+    semantic_brief = build_semantic_brief(goal_query)
+    semantic_llm_applied = False
+    if body.include_llm_intent and semantic_brief.semantic_strength >= 0.35:
+        semantic_brief, semantic_llm_applied = try_enrich_semantic_brief_with_llm(
+            cur, goal_query, semantic_brief
+        )
+
     used: Set[int] = set()
     steps: List[Dict[str, Any]] = []
     planned_ids: List[int] = []
@@ -203,17 +293,19 @@ def suggest_progression_path(
             tenant=tenant,
             goal_query=goal_query,
             step_index=step_index,
+            max_steps=max_steps,
             planned_ids=planned_ids,
             anchor_id=anchor_id,
             anchor_variant_id=anchor_variant_id,
             progression_graph_id=body.progression_graph_id,
             include_llm_intent=body.include_llm_intent,
             exercise_kind_any=body.exercise_kind_any,
+            semantic_brief=semantic_brief,
         )
         if step_index == 0:
             first_intent_summary = query_intent_summary
 
-        hit = _pick_next_path_hit(hits, used)
+        hit = _pick_best_path_hit(hits, used)
         if not hit:
             break
 
@@ -231,7 +323,56 @@ def suggest_progression_path(
             detail="Zu wenig passende Übungen für einen Pfad (mindestens 2 Schritte). Ziel präzisieren oder max_steps senken.",
         )
 
+    gaps: List[Dict[str, Any]] = []
+    bridge_inserts: List[Dict[str, Any]] = []
+    llm_qa: Optional[Dict[str, Any]] = None
+    llm_qa_applied = False
+
+    if body.include_path_qa:
+        gaps = detect_path_gaps(cur, steps, brief=semantic_brief)
+        if gaps:
+            bridge_fn = _make_bridge_search_fn(
+                cur,
+                tenant=tenant,
+                goal_query=goal_query,
+                max_steps=max_steps,
+                progression_graph_id=body.progression_graph_id,
+                include_llm_intent=body.include_llm_intent,
+                exercise_kind_any=body.exercise_kind_any,
+                semantic_brief=semantic_brief,
+                planned_ids=planned_ids,
+            )
+            steps, bridge_inserts = insert_bridge_exercises(
+                cur,
+                steps,
+                gaps,
+                brief=semantic_brief,
+                bridge_search_fn=bridge_fn,
+            )
+
+        if body.include_llm_path_qa:
+            llm_qa, llm_qa_applied = try_llm_qa_progression_path(
+                cur,
+                goal_query=goal_query,
+                brief=semantic_brief,
+                steps=steps,
+                gaps=gaps,
+                bridge_inserts=bridge_inserts,
+            )
+
+    path_qa = build_path_qa_summary(
+        gaps=gaps,
+        bridge_inserts=bridge_inserts,
+        llm_qa=llm_qa,
+        llm_applied=llm_qa_applied,
+    )
+
     target_profile_summary = target_profile.to_summary_dict(cur) if target_profile else None
+    retrieval_parts = ["profile_v1", "full_library", "path_builder", "semantics"]
+    if body.include_path_qa:
+        retrieval_parts.append("path_qa")
+    if llm_qa_applied:
+        retrieval_parts.append("llm_path_qa")
 
     return {
         "goal_query": goal_query,
@@ -239,14 +380,21 @@ def suggest_progression_path(
         "steps": steps,
         "step_count": len(steps),
         "target_profile_summary": target_profile_summary,
+        "semantic_brief_summary": brief_to_summary_dict(semantic_brief),
+        "semantic_llm_applied": semantic_llm_applied,
         "query_intent_summary": first_intent_summary,
         "progression_graph_id": body.progression_graph_id,
-        "retrieval_phase": "profile_v1+full_library+path_builder",
+        "path_qa": path_qa,
+        "retrieval_phase": "+".join(retrieval_parts),
     }
 
 
 __all__ = [
     "ProgressionPathSuggestRequest",
     "suggest_progression_path",
+    "_pick_best_path_hit",
     "_pick_next_path_hit",
 ]
+
+# Legacy-Alias für Tests
+_pick_next_path_hit = _pick_best_path_hit

backend/planning_exercise_path_qa.py

@@ -0,0 +1,343 @@
+"""
+Planungs-KI Phase E: Pfad-QA — Lücken erkennen, Brücken vorschlagen, LLM-Prüfung.
+"""
+from __future__ import annotations
+
+import json
+import logging
+import re
+from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Set, Tuple
+
+from ai_prompt_runtime import AiPromptUnavailableError, load_and_render_ai_prompt
+from exercise_ai import strip_html_to_plain
+from openrouter_chat import (
+    effective_openrouter_model_for_prompt_row,
+    normalize_openrouter_env,
+    openrouter_chat_completion,
+)
+
+from planning_exercise_semantics import (
+    PlanningSemanticBrief,
+    brief_to_summary_dict,
+    score_exercise_semantic_relevance,
+    step_phase_for_index,
+)
+
+_logger = logging.getLogger("shinkan.planning_exercise_path_qa")
+
+_GAP_SKILL_THRESHOLD = 0.10
+_GAP_SEMANTIC_THRESHOLD = 0.28
+_LARGE_GAP_SCORE = 0.52
+_MAX_BRIDGE_INSERTS = 4
+
+
+def _extract_json_object(text: str) -> Dict[str, Any]:
+    s = (text or "").strip()
+    if s.startswith("```"):
+        s = re.sub(r"^```[a-zA-Z0-9]*\s*", "", s)
+        if s.endswith("```"):
+            s = s[:-3].strip()
+    start = s.find("{")
+    end = s.rfind("}")
+    if start < 0 or end <= start:
+        raise ValueError("Kein JSON-Objekt in LLM-Antwort")
+    obj = json.loads(s[start : end + 1])
+    if not isinstance(obj, dict):
+        raise ValueError("LLM-Antwort ist kein JSON-Objekt")
+    return obj
+
+
+def _skill_jaccard(a: Set[int], b: Set[int]) -> float:
+    if not a or not b:
+        return 0.0
+    inter = len(a & b)
+    union = len(a | b)
+    return inter / union if union else 0.0
+
+
+def _load_exercise_skill_ids(cur, exercise_id: int) -> Set[int]:
+    cur.execute(
+        "SELECT skill_id FROM exercise_skills WHERE exercise_id = %s",
+        (int(exercise_id),),
+    )
+    return {int(r["skill_id"]) for r in cur.fetchall() if r.get("skill_id") is not None}
+
+
+def _load_exercise_text_bundle(cur, exercise_id: int) -> Dict[str, Any]:
+    cur.execute(
+        "SELECT id, title, summary, goal FROM exercises WHERE id = %s",
+        (int(exercise_id),),
+    )
+    row = cur.fetchone()
+    if not row:
+        return {"title": "", "summary": "", "goal": "", "variant_names": []}
+    cur.execute(
+        """
+        SELECT variant_name FROM exercise_variants
+        WHERE exercise_id = %s
+        ORDER BY sequence_order ASC NULLS LAST, id ASC
+        LIMIT 8
+        """,
+        (int(exercise_id),),
+    )
+    variants = [str(r.get("variant_name") or "") for r in cur.fetchall()]
+    return {
+        "title": str(row.get("title") or ""),
+        "summary": str(row.get("summary") or ""),
+        "goal": str(row.get("goal") or ""),
+        "variant_names": variants,
+    }
+
+
+def measure_step_transition_gap(
+    cur,
+    step_a: Mapping[str, Any],
+    step_b: Mapping[str, Any],
+    *,
+    brief: PlanningSemanticBrief,
+    segment_index: int,
+    total_segments: int,
+) -> Dict[str, Any]:
+    eid_a = int(step_a["exercise_id"])
+    eid_b = int(step_b["exercise_id"])
+    skills_a = _load_exercise_skill_ids(cur, eid_a)
+    skills_b = _load_exercise_skill_ids(cur, eid_b)
+    skill_sim = _skill_jaccard(skills_a, skills_b)
+
+    bundle_b = _load_exercise_text_bundle(cur, eid_b)
+    mid_phase = step_phase_for_index(brief, segment_index + 1, total_segments + 1)
+    sem_b, sem_reasons = score_exercise_semantic_relevance(
+        title=bundle_b["title"],
+        summary=bundle_b["summary"],
+        goal=bundle_b["goal"],
+        variant_names=bundle_b["variant_names"],
+        brief=brief,
+        step_phase=mid_phase,
+    )
+
+    gap_score = 0.0
+    if skill_sim < _GAP_SKILL_THRESHOLD:
+        gap_score += 0.45 * (1.0 - skill_sim / max(_GAP_SKILL_THRESHOLD, 0.01))
+    if sem_b < _GAP_SEMANTIC_THRESHOLD:
+        gap_score += 0.35 * (1.0 - sem_b / max(_GAP_SEMANTIC_THRESHOLD, 0.01))
+    if brief.semantic_strength >= 0.5 and sem_b < 0.15:
+        gap_score += 0.2
+
+    gap_score = min(1.0, round(gap_score, 4))
+    is_large = gap_score >= _LARGE_GAP_SCORE
+
+    return {
+        "from_exercise_id": eid_a,
+        "to_exercise_id": eid_b,
+        "from_title": step_a.get("title"),
+        "to_title": step_b.get("title"),
+        "skill_similarity": round(skill_sim, 4),
+        "semantic_score_to": sem_b,
+        "gap_score": gap_score,
+        "is_large_gap": is_large,
+        "expected_phase": mid_phase,
+        "reasons": sem_reasons,
+    }
+
+
+def detect_path_gaps(
+    cur,
+    steps: Sequence[Mapping[str, Any]],
+    *,
+    brief: PlanningSemanticBrief,
+) -> List[Dict[str, Any]]:
+    if len(steps) < 2:
+        return []
+    gaps: List[Dict[str, Any]] = []
+    total_segments = len(steps) - 1
+    for i in range(total_segments):
+        gap = measure_step_transition_gap(
+            cur,
+            steps[i],
+            steps[i + 1],
+            brief=brief,
+            segment_index=i,
+            total_segments=total_segments,
+        )
+        if gap.get("is_large_gap"):
+            gaps.append(gap)
+    return gaps
+
+
+def _pick_bridge_hit(
+    hits: Sequence[Mapping[str, Any]],
+    *,
+    used_ids: Set[int],
+    step_a_id: int,
+    step_b_id: int,
+) -> Optional[Dict[str, Any]]:
+    for hit in hits:
+        eid = int(hit["id"])
+        if eid in used_ids or eid in {step_a_id, step_b_id}:
+            continue
+        return dict(hit)
+    return None
+
+
+def insert_bridge_exercises(
+    cur,
+    steps: List[Dict[str, Any]],
+    gaps: Sequence[Mapping[str, Any]],
+    *,
+    brief: PlanningSemanticBrief,
+    bridge_search_fn: Callable[..., List[Dict[str, Any]]],
+    max_inserts: int = _MAX_BRIDGE_INSERTS,
+) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
+    """
+    Fügt zwischen großen Lücken Brücken-Übungen ein.
+    bridge_search_fn(from_step, to_step, gap) -> hits
+    """
+    if not gaps:
+        return steps, []
+
+    used_ids = {int(s["exercise_id"]) for s in steps}
+    inserts: List[Dict[str, Any]] = []
+    out = list(steps)
+
+    gap_by_pair = {
+        (int(g["from_exercise_id"]), int(g["to_exercise_id"])): g for g in gaps
+    }
+
+    i = 0
+    while i < len(out) - 1 and len(inserts) < max_inserts:
+        a = out[i]
+        b = out[i + 1]
+        key = (int(a["exercise_id"]), int(b["exercise_id"]))
+        gap = gap_by_pair.get(key)
+        if not gap:
+            i += 1
+            continue
+
+        hits = bridge_search_fn(a, b, gap)
+        bridge_hit = _pick_bridge_hit(
+            hits,
+            used_ids=used_ids,
+            step_a_id=int(a["exercise_id"]),
+            step_b_id=int(b["exercise_id"]),
+        )
+        if not bridge_hit:
+            i += 1
+            continue
+
+        bridge_step = {
+            "exercise_id": int(bridge_hit["id"]),
+            "variant_id": bridge_hit.get("suggested_variant_id"),
+            "title": bridge_hit.get("title"),
+            "summary": bridge_hit.get("summary"),
+            "score": bridge_hit.get("score"),
+            "reasons": list(bridge_hit.get("reasons") or []) + ["Brücken-Übung (Lückenfüller)"],
+            "variants": bridge_hit.get("variants") or [],
+            "suggested_variant_id": bridge_hit.get("suggested_variant_id"),
+            "suggested_variant_name": bridge_hit.get("suggested_variant_name"),
+            "is_bridge": True,
+            "bridge_for_gap": {
+                "from_exercise_id": int(a["exercise_id"]),
+                "to_exercise_id": int(b["exercise_id"]),
+                "gap_score": gap.get("gap_score"),
+            },
+        }
+        out.insert(i + 1, bridge_step)
+        used_ids.add(int(bridge_step["exercise_id"]))
+        inserts.append(
+            {
+                "inserted_after_index": i,
+                "bridge_exercise_id": int(bridge_step["exercise_id"]),
+                "bridge_title": bridge_step.get("title"),
+                "gap": gap,
+            }
+        )
+        i += 2
+
+    return out, inserts
+
+
+def try_llm_qa_progression_path(
+    cur,
+    *,
+    goal_query: str,
+    brief: PlanningSemanticBrief,
+    steps: Sequence[Mapping[str, Any]],
+    gaps: Sequence[Mapping[str, Any]],
+    bridge_inserts: Sequence[Mapping[str, Any]],
+) -> Tuple[Optional[Dict[str, Any]], bool]:
+    api_key, _ = normalize_openrouter_env()
+    if not api_key or len(steps) < 2:
+        return None, False
+
+    step_payload = []
+    for idx, step in enumerate(steps):
+        bundle = _load_exercise_text_bundle(cur, int(step["exercise_id"]))
+        step_payload.append(
+            {
+                "index": idx + 1,
+                "exercise_id": int(step["exercise_id"]),
+                "title": step.get("title") or bundle["title"],
+                "goal": strip_html_to_plain(bundle["goal"], max_len=400),
+                "is_bridge": bool(step.get("is_bridge")),
+                "reasons": list(step.get("reasons") or [])[:3],
+            }
+        )
+
+    variables = {
+        "goal_query": goal_query or "",
+        "semantic_brief_json": json.dumps(brief_to_summary_dict(brief), ensure_ascii=False),
+        "steps_json": json.dumps(step_payload, ensure_ascii=False),
+        "gaps_json": json.dumps(list(gaps), ensure_ascii=False),
+        "bridge_inserts_json": json.dumps(list(bridge_inserts), ensure_ascii=False),
+    }
+
+    try:
+        prow, rendered = load_and_render_ai_prompt(cur, "planning_exercise_path_qa", variables)
+        model = effective_openrouter_model_for_prompt_row(prow)
+        raw = openrouter_chat_completion(api_key=api_key, model=model, user_content=rendered.text)
+        obj = _extract_json_object(raw)
+        return obj, True
+    except AiPromptUnavailableError:
+        return None, False
+    except Exception as exc:
+        _logger.warning("Pfad-QA-LLM fehlgeschlagen: %s", exc)
+        return None, False
+
+
+def build_path_qa_summary(
+    *,
+    gaps: Sequence[Mapping[str, Any]],
+    bridge_inserts: Sequence[Mapping[str, Any]],
+    llm_qa: Optional[Mapping[str, Any]],
+    llm_applied: bool,
+) -> Dict[str, Any]:
+    summary: Dict[str, Any] = {
+        "gap_count": len(gaps),
+        "large_gaps": list(gaps),
+        "bridge_insert_count": len(bridge_inserts),
+        "bridge_inserts": list(bridge_inserts),
+        "llm_qa_applied": llm_applied,
+    }
+    if llm_qa:
+        summary["overall_ok"] = bool(llm_qa.get("overall_ok", True))
+        summary["quality_score"] = llm_qa.get("quality_score")
+        summary["issues"] = list(llm_qa.get("issues") or [])
+        summary["sequence_notes"] = list(llm_qa.get("sequence_notes") or [])
+        summary["topic_coverage"] = llm_qa.get("topic_coverage")
+        summary["recommendations"] = list(llm_qa.get("recommendations") or [])
+    else:
+        summary["overall_ok"] = len(gaps) == 0
+        summary["issues"] = [
+            f"Lücke zwischen „{g.get('from_title')}“ und „{g.get('to_title')}“ (Score {g.get('gap_score')})"
+            for g in gaps
+        ] if gaps else []
+    return summary
+
+
+__all__ = [
+    "build_path_qa_summary",
+    "detect_path_gaps",
+    "insert_bridge_exercises",
+    "measure_step_transition_gap",
+    "try_llm_qa_progression_path",
+]

backend/planning_exercise_retrieval.py

@@ -14,6 +14,7 @@ from planning_exercise_profiles import (
     load_exercise_match_profiles_bulk,
     score_exercise_against_target,
 )
+from planning_exercise_semantics import PlanningSemanticBrief, score_exercise_semantic_relevance
 
 _MAX_LIBRARY_ROWS = 8000
 _PROFILE_LOAD_BATCH = 400
@@ -133,6 +134,44 @@ def _load_skill_sets_chunked(cur, exercise_ids: Sequence[int], *, batch: int = _
     return out
 
 
+def _load_exercise_goals_chunked(cur, exercise_ids: Sequence[int], *, batch: int = _PROFILE_LOAD_BATCH) -> Dict[int, str]:
+    ids = sorted({int(x) for x in exercise_ids if int(x) > 0})
+    out: Dict[int, str] = {}
+    if not ids:
+        return out
+    for i in range(0, len(ids), batch):
+        chunk = ids[i : i + batch]
+        ph = ",".join(["%s"] * len(chunk))
+        cur.execute(f"SELECT id, goal FROM exercises WHERE id IN ({ph})", chunk)
+        for row in cur.fetchall():
+            out[int(row["id"])] = str(row.get("goal") or "")
+    return out
+
+
+def _load_variant_names_chunked(cur, exercise_ids: Sequence[int], *, batch: int = _PROFILE_LOAD_BATCH) -> Dict[int, List[str]]:
+    ids = sorted({int(x) for x in exercise_ids if int(x) > 0})
+    out: Dict[int, List[str]] = {eid: [] for eid in ids}
+    if not ids:
+        return out
+    for i in range(0, len(ids), batch):
+        chunk = ids[i : i + batch]
+        ph = ",".join(["%s"] * len(chunk))
+        cur.execute(
+            f"""
+            SELECT exercise_id, variant_name FROM exercise_variants
+            WHERE exercise_id IN ({ph})
+            ORDER BY sequence_order ASC NULLS LAST, id ASC
+            """,
+            chunk,
+        )
+        for row in cur.fetchall():
+            eid = int(row["exercise_id"])
+            name = str(row.get("variant_name") or "").strip()
+            if name:
+                out.setdefault(eid, []).append(name[:80])
+    return out
+
+
 def rank_visible_library_hits(
     cur,
     rows: Sequence[Dict[str, Any]],
@@ -151,6 +190,11 @@ def rank_visible_library_hits(
     anchor_id = pack.get("anchor_exercise_id")
     progression_notes = pack.get("progression_edge_notes") or {}
     requires_partner = pack.get("requires_partner")
+    semantic_brief_raw = pack.get("semantic_brief")
+    semantic_brief: Optional[PlanningSemanticBrief] = None
+    if isinstance(semantic_brief_raw, PlanningSemanticBrief):
+        semantic_brief = semantic_brief_raw
+    step_phase = pack.get("path_step_phase")
 
     last_planned_skills: Set[int] = set()
     planned_ids = pack.get("planned_exercise_ids") or []
@@ -175,6 +219,11 @@ def rank_visible_library_hits(
     cand_ids = [int(r["id"]) for r in cand_rows]
     match_profiles = _load_match_profiles_chunked(cur, cand_ids)
     skills_by_ex = _load_skill_sets_chunked(cur, cand_ids)
+    goals_by_ex: Dict[int, str] = {}
+    variants_by_ex: Dict[int, List[str]] = {}
+    if semantic_brief and semantic_brief.semantic_strength > 0.05:
+        goals_by_ex = _load_exercise_goals_chunked(cur, cand_ids)
+        variants_by_ex = _load_variant_names_chunked(cur, cand_ids)
 
     max_ft = 0.0
     scored_items: List[Dict[str, Any]] = []
@@ -213,8 +262,21 @@ def rank_visible_library_hits(
                 emp, target, intent=intent
             )
 
+        semantic_score = 0.0
+        semantic_reasons: List[str] = []
+        if semantic_brief and semantic_brief.semantic_strength > 0.05:
+            semantic_score, semantic_reasons = score_exercise_semantic_relevance(
+                title=str(row.get("title") or ""),
+                summary=str(row.get("summary") or ""),
+                goal=goals_by_ex.get(eid, ""),
+                variant_names=variants_by_ex.get(eid, []),
+                brief=semantic_brief,
+                step_phase=step_phase,
+            )
+
         score = (
-            weights["fulltext"] * ft_norm
+            weights.get("semantic", 0.0) * semantic_score
+            + weights["fulltext"] * ft_norm
             + weights["progression"] * prog_hit
             + weights["skill"] * skill_sim
             + weights["plan"] * plan_aff
@@ -224,6 +286,10 @@ def rank_visible_library_hits(
         )
 
         reasons: List[str] = []
+        if semantic_score >= 0.35 and semantic_reasons:
+            for sr in semantic_reasons:
+                if sr not in reasons:
+                    reasons.append(sr)
         if query and ft_norm >= 0.35:
             reasons.append("Volltext-Treffer")
         if prog_hit > 0:
@@ -255,6 +321,7 @@ def rank_visible_library_hits(
                 "focus_area": row.get("primary_focus_name"),
                 "score": round(max(0.0, min(1.0, score)), 4),
                 "reasons": reasons,
+                "semantic_score": round(semantic_score, 4),
             }
         )
         succ_variants = pack.get("progression_successor_variants") or {}
@@ -283,7 +350,7 @@ def run_multistage_planning_retrieval(
         cur,
         vis_sql=vis_sql,
         vis_params=vis_params,
-        query=query,
+        query=pack.get("retrieval_query") or query,
         exercise_kind_any=exercise_kind_any,
     )
     hits, skills_by_ex = rank_visible_library_hits(

backend/planning_exercise_semantics.py

@@ -0,0 +1,492 @@
+"""
+Planungs-KI Phase E: Semantik-Schicht für Anfrage-Verständnis und Retrieval.
+
+Trennt anfrage-spezifische Semantik (Technik, Phrasen, Entwicklungsbogen) vom
+Katalog-Profil-Overlay (Fokus/Skills). Wird in Hybrid-Retrieval und Pfad-QA genutzt.
+"""
+from __future__ import annotations
+
+import json
+import logging
+import re
+from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple
+
+from pydantic import BaseModel, Field, field_validator
+
+from ai_prompt_runtime import AiPromptUnavailableError, load_and_render_ai_prompt
+from exercise_ai import strip_html_to_plain
+from openrouter_chat import (
+    effective_openrouter_model_for_prompt_row,
+    normalize_openrouter_env,
+    openrouter_chat_completion,
+)
+
+_logger = logging.getLogger("shinkan.planning_exercise_semantics")
+
+_GERI_TECHNIQUES: Tuple[Tuple[str, Tuple[str, ...]], ...] = (
+    ("mae geri", ("mawashi geri", "yoko geri", "ushiro geri", "sakuto geri", "mikazuki geri")),
+    ("mawashi geri", ("mae geri", "yoko geri", "ushiro geri", "sakuto geri")),
+    ("yoko geri", ("mae geri", "mawashi geri", "ushiro geri", "sakuto geri")),
+    ("ushiro geri", ("mae geri", "mawashi geri", "yoko geri", "sakuto geri")),
+    ("sakuto geri", ("mae geri", "mawashi geri", "yoko geri", "mikazuki geri")),
+    ("mikazuki geri", ("mae geri", "mawashi geri", "sakuto geri")),
+)
+
+_OTHER_TECHNIQUE_PATTERNS: Tuple[Tuple[str, Tuple[str, ...]], ...] = (
+    ("oi zuki", ("gyaku zuki", "age uke", "gedan barai")),
+    ("gyaku zuki", ("oi zuki", "mae geri")),
+    ("age uke", ("gedan barai", "soto uke")),
+    ("gedan barai", ("age uke", "soto uke")),
+)
+
+_ARC_PHASES: Tuple[Tuple[str, Tuple[str, ...]], ...] = (
+    ("einstieg", ("einstieg", "erlernen", "lernen", "anfänger", "anfaenger", "beginn", "grund")),
+    ("grundlage", ("grundlage", "fundament", "basis", "basic")),
+    ("vertiefung", ("vertief", "festigung", "übung", "uebung", "wiederhol")),
+    ("anwendung", ("anwend", "partner", "kampf", "kumite", "reaktion")),
+    ("perfektion", ("perfekt", "meisterschaft", "höchst", "hoechst", "kime", "sauber")),
+)
+
+_PHASE_QUERY_HINTS: Dict[str, str] = {
+    "einstieg": "einstieg grundübung einfach",
+    "grundlage": "grundtechnik festigung",
+    "vertiefung": "vertiefung technik übung",
+    "anwendung": "anwendung partner variante",
+    "perfektion": "perfektion kontrolle kime höchste stufe",
+}
+
+_QUERY_STOPWORDS = frozenset(
+    {
+        "von",
+        "bis",
+        "zur",
+        "zum",
+        "der",
+        "die",
+        "das",
+        "des",
+        "den",
+        "dem",
+        "ein",
+        "eine",
+        "einer",
+        "eines",
+        "und",
+        "oder",
+        "mit",
+        "für",
+        "fuer",
+        "im",
+        "in",
+        "am",
+        "an",
+        "auf",
+        "aus",
+        "beim",
+        "nach",
+        "vor",
+        "über",
+        "ueber",
+        "unter",
+        "wie",
+        "was",
+        "wo",
+        "wir",
+        "soll",
+        "sollen",
+        "bitte",
+        "schlage",
+        "vorschlag",
+        "übung",
+        "uebung",
+        "übungen",
+        "uebungen",
+    }
+)
+
+
+class PlanningSemanticBrief(BaseModel):
+    primary_topic: Optional[str] = Field(default=None, max_length=120)
+    topic_type: str = Field(default="general", max_length=40)
+    must_phrases: List[str] = Field(default_factory=list)
+    exclude_phrases: List[str] = Field(default_factory=list)
+    development_arc: List[str] = Field(default_factory=list)
+    retrieval_query: str = Field(default="", max_length=500)
+    semantic_strength: float = Field(default=0.0, ge=0.0, le=1.0)
+    rationale: Optional[str] = Field(default=None, max_length=400)
+
+    @field_validator("topic_type")
+    @classmethod
+    def _topic_type(cls, v: str) -> str:
+        s = (v or "general").strip().lower()
+        return s if s in {"general", "technique", "focus", "method", "skill"} else "general"
+
+    @field_validator("must_phrases", "exclude_phrases", "development_arc", mode="before")
+    @classmethod
+    def _norm_phrase_list(cls, v: Any) -> List[str]:
+        if not v:
+            return []
+        if isinstance(v, str):
+            s = _normalize_phrase(v)
+            return [s] if s else []
+        out: List[str] = []
+        for item in v:
+            s = _normalize_phrase(str(item or ""))
+            if s and s not in out:
+                out.append(s[:120])
+        return out[:12]
+
+
+def _normalize_phrase(text: str) -> str:
+    return re.sub(r"\s+", " ", (text or "").strip().lower())
+
+
+def _normalize_query(text: str) -> str:
+    return re.sub(r"\s+", " ", (text or "").strip())
+
+
+def _extract_json_object(text: str) -> Dict[str, Any]:
+    s = (text or "").strip()
+    if s.startswith("```"):
+        s = re.sub(r"^```[a-zA-Z0-9]*\s*", "", s)
+        if s.endswith("```"):
+            s = s[:-3].strip()
+    start = s.find("{")
+    end = s.rfind("}")
+    if start < 0 or end <= start:
+        raise ValueError("Kein JSON-Objekt in LLM-Antwort")
+    obj = json.loads(s[start : end + 1])
+    if not isinstance(obj, dict):
+        raise ValueError("LLM-Antwort ist kein JSON-Objekt")
+    return obj
+
+
+def _find_technique_in_text(q_lower: str) -> Optional[Tuple[str, Tuple[str, ...]]]:
+    for primary, excludes in _GERI_TECHNIQUES + _OTHER_TECHNIQUE_PATTERNS:
+        if primary in q_lower:
+            return primary, excludes
+    return None
+
+
+def _detect_development_arc(q_lower: str) -> List[str]:
+    found: List[str] = []
+    for phase, markers in _ARC_PHASES:
+        if any(m in q_lower for m in markers):
+            if phase not in found:
+                found.append(phase)
+    if not found and ("von" in q_lower and "bis" in q_lower):
+        found = ["einstieg", "perfektion"]
+    return found
+
+
+def _keyword_phrases_from_query(query: str) -> List[str]:
+    q = _normalize_query(query).lower()
+    tokens = re.findall(r"[a-zäöüß]{3,}", q, flags=re.IGNORECASE)
+    phrases: List[str] = []
+    for i, tok in enumerate(tokens):
+        low = tok.lower()
+        if low in _QUERY_STOPWORDS:
+            continue
+        if i + 1 < len(tokens):
+            nxt = tokens[i + 1].lower()
+            if nxt not in _QUERY_STOPWORDS:
+                pair = _normalize_phrase(f"{low} {nxt}")
+                if len(pair) >= 5 and pair not in phrases:
+                    phrases.append(pair)
+        if len(low) >= 4 and low not in phrases:
+            phrases.append(low)
+    return phrases[:6]
+
+
+def build_semantic_brief(query: Optional[str]) -> PlanningSemanticBrief:
+    """Deterministisches Anfrage-Verständnis — ohne LLM."""
+    q = _normalize_query(query)
+    if not q:
+        return PlanningSemanticBrief(retrieval_query="", semantic_strength=0.0)
+
+    q_lower = q.lower()
+    must: List[str] = []
+    exclude: List[str] = []
+    topic_type = "general"
+    primary: Optional[str] = None
+    strength = 0.25
+
+    technique = _find_technique_in_text(q_lower)
+    if technique:
+        primary, ex = technique
+        must.append(primary)
+        exclude.extend(list(ex))
+        topic_type = "technique"
+        strength = max(strength, 0.82)
+
+    arc = _detect_development_arc(q_lower)
+    if arc:
+        strength = max(strength, 0.55 if technique else 0.45)
+
+    extra_phrases = _keyword_phrases_from_query(q)
+    for ph in extra_phrases:
+        if ph not in must and not any(ph in m or m in ph for m in must):
+            if len(ph) >= 5:
+                must.append(ph)
+
+    if len(q) >= 24 and not technique:
+        strength = max(strength, 0.4)
+
+    retrieval = " ".join(must[:4]) if must else q
+    if arc and primary:
+        retrieval = f"{primary} {' '.join(arc[:2])}"
+
+    return PlanningSemanticBrief(
+        primary_topic=primary,
+        topic_type=topic_type,
+        must_phrases=must[:8],
+        exclude_phrases=exclude[:10],
+        development_arc=arc[:5],
+        retrieval_query=retrieval[:500],
+        semantic_strength=min(1.0, round(strength, 3)),
+        rationale=None,
+    )
+
+
+def merge_semantic_brief_llm(
+    base: PlanningSemanticBrief,
+    llm_obj: Mapping[str, Any],
+) -> PlanningSemanticBrief:
+    """LLM-Enrichment in deterministisches Brief mergen (LLM ergänzt, ersetzt nicht harte Technik-Regeln)."""
+    data = base.model_dump()
+    for key in ("primary_topic", "topic_type", "rationale"):
+        val = llm_obj.get(key)
+        if val:
+            data[key] = val
+
+    for key in ("must_phrases", "exclude_phrases", "development_arc"):
+        extra = llm_obj.get(key) or []
+        merged = list(data.get(key) or [])
+        for item in extra:
+            s = _normalize_phrase(str(item or ""))
+            if s and s not in merged:
+                merged.append(s)
+        data[key] = merged[:12]
+
+    llm_strength = llm_obj.get("semantic_strength")
+    if llm_strength is not None:
+        try:
+            data["semantic_strength"] = min(
+                1.0,
+                max(float(data["semantic_strength"]), float(llm_strength)),
+            )
+        except (TypeError, ValueError):
+            pass
+
+    if data.get("must_phrases"):
+        data["retrieval_query"] = " ".join(data["must_phrases"][:4])[:500]
+    out = PlanningSemanticBrief.model_validate(data)
+    if out.primary_topic and out.topic_type == "general":
+        out = out.model_copy(update={"topic_type": "technique"})
+    return out
+
+
+def try_enrich_semantic_brief_with_llm(
+    cur,
+    query: str,
+    base: PlanningSemanticBrief,
+) -> Tuple[PlanningSemanticBrief, bool]:
+    api_key, _ = normalize_openrouter_env()
+    if not api_key or base.semantic_strength < 0.35:
+        return base, False
+    if not (query or "").strip():
+        return base, False
+
+    variables = {
+        "search_query": (query or "").strip(),
+        "semantic_brief_json": json.dumps(brief_to_summary_dict(base), ensure_ascii=False),
+    }
+    try:
+        prow, rendered = load_and_render_ai_prompt(cur, "planning_exercise_query_semantics", variables)
+        model = effective_openrouter_model_for_prompt_row(prow)
+        raw = openrouter_chat_completion(api_key=api_key, model=model, user_content=rendered.text)
+        obj = _extract_json_object(raw)
+        return merge_semantic_brief_llm(base, obj), True
+    except AiPromptUnavailableError:
+        return base, False
+    except Exception as exc:
+        _logger.warning("Semantik-LLM fehlgeschlagen: %s", exc)
+        return base, False
+
+
+def brief_to_summary_dict(brief: PlanningSemanticBrief) -> Dict[str, Any]:
+    return {
+        "primary_topic": brief.primary_topic,
+        "topic_type": brief.topic_type,
+        "must_phrases": list(brief.must_phrases),
+        "exclude_phrases": list(brief.exclude_phrases),
+        "development_arc": list(brief.development_arc),
+        "retrieval_query": brief.retrieval_query,
+        "semantic_strength": brief.semantic_strength,
+        "rationale": brief.rationale,
+    }
+
+
+def step_phase_for_index(brief: PlanningSemanticBrief, step_index: int, max_steps: int) -> Optional[str]:
+    arc = list(brief.development_arc or [])
+    if not arc:
+        if max_steps <= 1:
+            return None
+        default_arc = ["einstieg", "grundlage", "vertiefung", "anwendung", "perfektion"]
+        arc = default_arc[:max_steps] if brief.semantic_strength >= 0.5 else []
+    if not arc:
+        return None
+    if len(arc) == 1:
+        return arc[0]
+    pos = step_index / max(max_steps - 1, 1)
+    idx = min(len(arc) - 1, int(round(pos * (len(arc) - 1))))
+    return arc[idx]
+
+
+def step_retrieval_query(
+    brief: PlanningSemanticBrief,
+    goal_query: str,
+    step_index: int,
+    max_steps: int,
+) -> str:
+    phase = step_phase_for_index(brief, step_index, max_steps)
+    parts: List[str] = []
+    if brief.retrieval_query:
+        parts.append(brief.retrieval_query)
+    elif goal_query:
+        parts.append(goal_query)
+    if brief.primary_topic and brief.primary_topic not in " ".join(parts).lower():
+        parts.append(brief.primary_topic)
+    if phase:
+        hint = _PHASE_QUERY_HINTS.get(phase, phase)
+        parts.append(hint)
+    return _normalize_query(" ".join(parts)) or _normalize_query(goal_query)
+
+
+def apply_dynamic_retrieval_weights(
+    base_weights: Mapping[str, float],
+    brief: PlanningSemanticBrief,
+    *,
+    scenario: str,
+    has_planning_reference: bool,
+) -> Dict[str, float]:
+    """Semantik-Kanal dynamisch gegen Profil/Plan abwägen."""
+    out = dict(base_weights)
+    sem = float(brief.semantic_strength or 0.0)
+    if sem <= 0.05:
+        out.setdefault("semantic", 0.0)
+        return out
+
+    query_driven = scenario == "free_search" or not has_planning_reference
+    sem_weight = 0.12 + sem * (0.38 if query_driven else 0.22)
+    out["semantic"] = round(sem_weight, 4)
+
+    if query_driven:
+        scale = 1.0 - sem * 0.35
+        out["fulltext"] = round(float(out.get("fulltext", 0.18)) * scale, 4)
+        out["profile"] = round(float(out.get("profile", 0.22)) * (1.0 - sem * 0.25), 4)
+    else:
+        out["fulltext"] = round(float(out.get("fulltext", 0.18)) * (1.0 - sem * 0.15), 4)
+
+    total = sum(v for k, v in out.items() if k not in {"repeat_unit", "repeat_group"} and v > 0)
+    if total > 0.92:
+        factor = 0.88 / total
+        for k in list(out.keys()):
+            if k in {"repeat_unit", "repeat_group"}:
+                continue
+            if out[k] > 0:
+                out[k] = round(out[k] * factor, 4)
+    return out
+
+
+def _blob_from_fields(
+    title: str,
+    summary: str,
+    goal: str,
+    variant_names: Sequence[str],
+) -> str:
+    parts = [title or "", strip_html_to_plain(summary, max_len=600), strip_html_to_plain(goal, max_len=800)]
+    parts.extend(variant_names or [])
+    return " ".join(p for p in parts if p).lower()
+
+
+def _phrase_in_blob(phrase: str, blob: str) -> bool:
+    ph = _normalize_phrase(phrase)
+    if not ph or not blob:
+        return False
+    if ph in blob:
+        return True
+    if " " not in ph:
+        return bool(re.search(rf"\b{re.escape(ph)}\b", blob))
+    return ph in blob
+
+
+def score_exercise_semantic_relevance(
+    *,
+    title: str,
+    summary: str,
+    goal: str,
+    variant_names: Sequence[str],
+    brief: PlanningSemanticBrief,
+    step_phase: Optional[str] = None,
+) -> Tuple[float, List[str]]:
+    if brief.semantic_strength <= 0.05:
+        return 0.0, []
+
+    blob = _blob_from_fields(title, summary, goal, variant_names)
+    if not blob.strip():
+        return 0.0, []
+
+    reasons: List[str] = []
+    must = list(brief.must_phrases or [])
+    exclude = list(brief.exclude_phrases or [])
+
+    must_hits = sum(1 for ph in must if _phrase_in_blob(ph, blob))
+    exclude_hits = sum(1 for ph in exclude if _phrase_in_blob(ph, blob))
+
+    score = 0.0
+    if must:
+        must_ratio = must_hits / len(must)
+        score += 0.55 * must_ratio
+        if must_hits == len(must):
+            reasons.append("Alle Kernbegriffe der Anfrage im Übungstext")
+        elif must_hits > 0:
+            reasons.append("Teilweise passende Kernbegriffe")
+        elif brief.primary_topic and _phrase_in_blob(brief.primary_topic, blob):
+            score += 0.45
+            reasons.append(f"Thema „{brief.primary_topic}“ im Übungstext")
+    elif brief.primary_topic and _phrase_in_blob(brief.primary_topic, blob):
+        score += 0.5
+        reasons.append(f"Thema „{brief.primary_topic}“ im Übungstext")
+
+    if exclude_hits > 0:
+        penalty = min(0.55, 0.18 * exclude_hits)
+        if must_hits == 0 or exclude_hits >= must_hits:
+            score -= penalty
+            reasons.append("Enthält ausgeschlossene Nebenthemen")
+
+    if step_phase and step_phase in _PHASE_QUERY_HINTS:
+        phase_markers = next((markers for phase, markers in _ARC_PHASES if phase == step_phase), ())
+        if any(m in blob for m in phase_markers) or step_phase in blob:
+            score += 0.12
+            reasons.append(f"Passt zur Pfad-Phase „{step_phase}“")
+
+    if brief.development_arc and not step_phase:
+        arc_hits = sum(1 for phase in brief.development_arc if phase in blob)
+        if arc_hits:
+            score += min(0.15, 0.05 * arc_hits)
+
+    return max(0.0, min(1.0, round(score, 4))), reasons[:4]
+
+
+__all__ = [
+    "PlanningSemanticBrief",
+    "apply_dynamic_retrieval_weights",
+    "brief_to_summary_dict",
+    "build_semantic_brief",
+    "merge_semantic_brief_llm",
+    "score_exercise_semantic_relevance",
+    "step_phase_for_index",
+    "step_retrieval_query",
+    "try_enrich_semantic_brief_with_llm",
+]

backend/planning_exercise_suggest.py

@@ -21,6 +21,14 @@ from planning_exercise_target_pipeline import (
     compose_retrieval_phase,
     should_run_llm_rank_pipeline,
 )
+from planning_exercise_semantics import (
+    PlanningSemanticBrief,
+    apply_dynamic_retrieval_weights,
+    brief_to_summary_dict,
+    build_semantic_brief,
+    step_retrieval_query,
+    try_enrich_semantic_brief_with_llm,
+)
 
 # Planungs-Berechtigung + Sektionen (bestehende Implementierung)
 from routers.training_planning import (
@@ -89,6 +97,7 @@ def resolve_planning_exercise_intent(query: Optional[str], intent_hint: Optional
 def _intent_weights(intent: str) -> Dict[str, float]:
     base = {
         "fulltext": 0.18,
+        "semantic": 0.0,
         "progression": 0.18,
         "skill": 0.12,
         "plan": 0.08,
@@ -714,12 +723,25 @@ def suggest_planning_exercises(
         context_summary=pipeline_context,
         has_planning_reference=has_plan_ref,
     )
-    weights = _intent_weights(intent)
     target_profile_summary = target_profile.to_summary_dict(cur)
     query_intent_applied = bool(query_intent_summary.get("llm_applied"))
     llm_expectation_applied = bool(query_intent_summary.get("llm_expectation_applied"))
     profile_llm_applied = bool(query_intent_summary.get("profile_llm_applied"))
 
+    semantic_brief = build_semantic_brief(query)
+    semantic_llm_applied = False
+    if body.include_llm_intent and semantic_brief.semantic_strength >= 0.35:
+        semantic_brief, semantic_llm_applied = try_enrich_semantic_brief_with_llm(
+            cur, query, semantic_brief
+        )
+
+    weights = apply_dynamic_retrieval_weights(
+        _intent_weights(intent),
+        semantic_brief,
+        scenario=scenario_kind,
+        has_planning_reference=has_plan_ref,
+    )
+
     profile_id = tenant.profile_id
     role = tenant.global_role
     vis_sql, vis_params = library_content_visibility_sql(
@@ -741,6 +763,8 @@ def suggest_planning_exercises(
         pack={
             **pack,
             "requires_partner": query_intent_summary.get("requires_partner"),
+            "semantic_brief": semantic_brief,
+            "retrieval_query": semantic_brief.retrieval_query or query,
         },
     )
 
@@ -755,6 +779,7 @@ def suggest_planning_exercises(
         query_intent=query_intent_applied,
         llm_expectation=llm_expectation_applied,
         llm_rank=False,
+        semantics=semantic_brief.semantic_strength >= 0.35,
     )
     run_llm_rank = should_run_llm_rank_pipeline(
         query,
@@ -792,6 +817,7 @@ def suggest_planning_exercises(
                 query_intent=query_intent_applied,
                 llm_expectation=llm_expectation_applied,
                 llm_rank=True,
+                semantics=semantic_brief.semantic_strength >= 0.35,
             )
             tail = hits[pre_limit:]
             hits = pool_hits + tail
@@ -837,6 +863,8 @@ def suggest_planning_exercises(
         "llm_intent_applied": query_intent_applied,
         "llm_expectation_applied": llm_expectation_applied,
         "profile_llm_applied": profile_llm_applied,
+        "semantic_brief_summary": brief_to_summary_dict(semantic_brief),
+        "semantic_llm_applied": semantic_llm_applied,
         "intent_resolved": intent,
         "intent_heuristic": heuristic_intent,
         "query_normalized": query or None,

backend/planning_exercise_target_pipeline.py

@@ -430,12 +430,15 @@ def compose_retrieval_phase(
     query_intent: bool = False,
     llm_expectation: bool = False,
     llm_rank: bool = False,
+    semantics: bool = False,
 ) -> str:
     parts = ["profile_v1"]
     if full_library or profile_preselect:
         parts.append("full_library")
     if text_signals:
         parts.append("text_signals")
+    if semantics:
+        parts.append("semantics")
     if llm_expectation:
         parts.append("llm_expectation")
     elif query_intent:

backend/tests/test_planning_exercise_path_builder.py

@@ -1,11 +1,11 @@
-"""Tests Planungs-KI Phase C3 — Pfad-Vorschläge."""
-from planning_exercise_path_builder import _pick_next_path_hit, _hit_to_path_step
+"""Tests Planungs-KI Phase C3/E — Pfad-Vorschläge."""
+from planning_exercise_path_builder import _pick_best_path_hit, _hit_to_path_step
 
 
 def test_pick_next_path_hit_skips_used():
-    hits = [{"id": 1, "title": "A"}, {"id": 2, "title": "B"}, {"id": 3, "title": "C"}]
-    assert _pick_next_path_hit(hits, {1})["id"] == 2
-    assert _pick_next_path_hit(hits, {1, 2, 3}) is None
+    hits = [{"id": 1, "title": "A", "semantic_score": 0.2}, {"id": 2, "title": "B", "semantic_score": 0.2}, {"id": 3, "title": "C", "semantic_score": 0.2}]
+    assert _pick_best_path_hit(hits, {1})["id"] == 2
+    assert _pick_best_path_hit(hits, {1, 2, 3}) is None
 
 
 def test_hit_to_path_step_maps_variant():

backend/tests/test_planning_exercise_path_qa.py

@@ -0,0 +1,16 @@
+"""Tests Planungs-KI Phase E — Pfad-QA."""
+from planning_exercise_path_builder import _pick_best_path_hit
+
+
+def test_pick_best_path_hit_prefers_semantic_score():
+    hits = [
+        {"id": 1, "title": "Mawashi", "score": 0.9, "semantic_score": 0.1},
+        {"id": 2, "title": "Mae Geri", "score": 0.75, "semantic_score": 0.85},
+    ]
+    chosen = _pick_best_path_hit(hits, set())
+    assert chosen["id"] == 2
+
+
+def test_pick_best_path_hit_skips_used():
+    hits = [{"id": 1, "title": "A", "score": 0.5, "semantic_score": 0.5}]
+    assert _pick_best_path_hit(hits, {1}) is None

backend/tests/test_planning_exercise_semantics.py

@@ -0,0 +1,67 @@
+"""Tests Planungs-KI Phase E — Semantik-Schicht."""
+from planning_exercise_semantics import (
+    apply_dynamic_retrieval_weights,
+    build_semantic_brief,
+    score_exercise_semantic_relevance,
+    step_retrieval_query,
+)
+
+
+def test_build_semantic_brief_mae_geri():
+    brief = build_semantic_brief(
+        "Von Erlernen bis zur Perfektion, des Fußtritts Mae Geri"
+    )
+    assert brief.primary_topic == "mae geri"
+    assert "mae geri" in brief.must_phrases
+    assert "mawashi geri" in brief.exclude_phrases
+    assert brief.semantic_strength >= 0.8
+    assert "einstieg" in brief.development_arc or "perfektion" in brief.development_arc
+
+
+def test_semantic_score_prefers_mae_over_mawashi():
+    brief = build_semantic_brief("Mae Geri Perfektion")
+    mae_score, _ = score_exercise_semantic_relevance(
+        title="Mae Geri — Frontkick Grundstellung",
+        summary="Frontkick von vorn",
+        goal="Sauberer Mae Geri",
+        variant_names=[],
+        brief=brief,
+    )
+    mawashi_score, _ = score_exercise_semantic_relevance(
+        title="Mawashi Geri — Rundkick",
+        summary="Rundkick Technik",
+        goal="Mawashi Geri Höhe",
+        variant_names=[],
+        brief=brief,
+    )
+    assert mae_score > mawashi_score
+
+
+def test_dynamic_weights_boost_semantic_for_query_only():
+    brief = build_semantic_brief("Mae Geri bis Perfektion")
+    base = {
+        "fulltext": 0.45,
+        "semantic": 0.0,
+        "progression": 0.08,
+        "skill": 0.08,
+        "plan": 0.08,
+        "profile": 0.15,
+        "repeat_unit": -0.3,
+        "repeat_group": -0.15,
+    }
+    out = apply_dynamic_retrieval_weights(
+        base,
+        brief,
+        scenario="free_search",
+        has_planning_reference=False,
+    )
+    assert out["semantic"] > 0.25
+    assert out["fulltext"] < base["fulltext"]
+
+
+def test_step_retrieval_query_carries_topic_and_phase():
+    brief = build_semantic_brief("Mae Geri von Einstieg bis Perfektion")
+    q0 = step_retrieval_query(brief, brief.retrieval_query, 0, 5)
+    q4 = step_retrieval_query(brief, brief.retrieval_query, 4, 5)
+    assert "mae geri" in q0.lower()
+    assert q0 != q4

backend/version.py

@@ -1,6 +1,6 @@
 # Shinkan Jinkendo Version Information
 
-APP_VERSION = "0.8.185"
+APP_VERSION = "0.8.186"
 BUILD_DATE = "2026-05-23"
 DB_SCHEMA_VERSION = "20260531074"
 
@@ -29,7 +29,7 @@ MODULE_VERSIONS = {
     "skill_profiles": "1.0.0",  # Phase 3: gewichtetes Fähigkeiten-Profil + skill-discovery/suggestions
     "methods": "0.1.0",
     "exercises": "2.37.0",  # Planungs-KI P1: Szenario-Pipeline + Query-Intent-Overlay
-    "planning_exercise_suggest": "0.13.0",  # Phase C3: progression-path-suggest für Graph-Builder
+    "planning_exercise_suggest": "0.14.0",  # Phase E: Semantik-Schicht + Pfad-QA
     "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
@@ -44,6 +44,14 @@ MODULE_VERSIONS = {
 }
 
 CHANGELOG = [
+    {
+        "version": "0.8.186",
+        "date": "2026-05-23",
+        "changes": [
+            "Planungs-KI Phase E: Semantic Brief (Technik-Phrasen, Ausschlüsse, Entwicklungsbogen) im Hybrid-Retrieval.",
+            "Pfad-Builder: semantische Schritte, Lücken-Erkennung, Brücken-Übungen, optional LLM-Pfad-QS.",
+        ],
+    },
     {
         "version": "0.8.185",
         "date": "2026-05-23",

docs/HANDOVER.md

@@ -1,7 +1,7 @@
 # Shinkan Jinkendo – Entwicklungsstand & Handover
 
 **Stand:** 2026-05-23  
-**App-Version / DB-Schema:** App **`0.8.185`** (Planungs-KI Phase C3); DB **`20260531074`** — maßgeblich **`backend/version.py`**.
+**App-Version / DB-Schema:** App **`0.8.186`** (Planungs-KI Phase E Semantik + Pfad-QA); DB **`20260531074`** — maßgeblich **`backend/version.py`**.
 
 Diese Datei ist die **Einstiegs-Doku für neue Chat-Sessions**: Anforderungen im Detail stehen in `.claude/docs/` (siehe unten); hier der **implementierte Stand**, **Medien-Meilenstein** und **sinnvolle nächste Schritte**.
 
@@ -104,6 +104,7 @@ Das Schema ist gegenüber dem Code zurück: Migration **`022_skills_schema_compl
 | **C1** | Progressionsgraph auto-match + variantenbewusste Nachfolger | ✅ **0.8.183** |
 | **C2** | Varianten in Trefferliste / Picker-Auswahl | ✅ **0.8.184** |
 | **C3** | Graph-Builder: Ziel → Pfad vorschlagen → in Graph speichern | ✅ **0.8.185** |
+| **E** | Semantik-Schicht (Brief, Phrasen-Score) + Pfad-QA (Lücken, Brücken, LLM-QS) | ✅ **0.8.186** |
 | **D** | `planning_context` an `suggestExerciseAi` (Neu-Anlage) | 🔲 |
 
 **Backend:** `planning_exercise_suggest.py`, `planning_exercise_retrieval.py`, `planning_exercise_profiles.py`, `planning_exercise_target_pipeline.py`, `planning_exercise_progression.py` · Router `POST /api/planning/exercise-suggest`
@@ -248,9 +249,9 @@ Das Schema ist gegenüber dem Code zurück: Migration **`022_skills_schema_compl
 ### Planungs-KI (priorisiert)
 
 1. **Graph-Auswahl UI:** Dropdown neben Auto-Match; Rahmen-Slot mit Default-Graph verknüpfen.
-2. **Enrichment:** Skills für Kern-Übungen nachziehen (sonst schwaches Profil-Ranking).
+2. **Enrichment:** Skills/Tags pro Technik (Feinauflösung statt nur Geri Waza).
 3. **D — Neu-Anlage:** `planning_context_json` an `POST /api/exercises/ai/suggest`.
-4. **C3 Feinschliff:** Einzelschritte im Pfad manuell ersetzen (Picker); Pfad an bestehende Kette anhängen.
+4. **E Feinschliff:** Pfad-QA → automatische Neuordnung; fehlende Schritte als KI-Neuanlage vorschlagen.
 
 ### Allgemein
 

frontend/src/components/ExerciseProgressionPathBuilder.jsx

@@ -19,6 +19,8 @@ function mapApiStepToRow(step) {
     variantId,
     variants,
     reasons: Array.isArray(step?.reasons) ? step.reasons : [],
+    isBridge: Boolean(step?.is_bridge),
+    semanticScore: step?.semantic_score,
   }
 }
 
@@ -34,6 +36,8 @@ export default function ExerciseProgressionPathBuilder({
   const [saving, setSaving] = useState(false)
   const [error, setError] = useState('')
   const [targetSummary, setTargetSummary] = useState(null)
+  const [semanticBrief, setSemanticBrief] = useState(null)
+  const [pathQa, setPathQa] = useState(null)
   const [pathSteps, setPathSteps] = useState([])
 
   const patchStep = useCallback((idx, patch) => {
@@ -73,6 +77,8 @@ export default function ExerciseProgressionPathBuilder({
         query: q,
         max_steps: Number(maxSteps),
         include_llm_intent: true,
+        include_path_qa: true,
+        include_llm_path_qa: true,
         progression_graph_id: Number(graphId),
       })
       const rows = (Array.isArray(res?.steps) ? res.steps : []).map(mapApiStepToRow)
@@ -81,12 +87,16 @@ export default function ExerciseProgressionPathBuilder({
       }
       setPathSteps(rows)
       setTargetSummary(res?.target_profile_summary || null)
+      setSemanticBrief(res?.semantic_brief_summary || null)
+      setPathQa(res?.path_qa || null)
       if (!segmentNotes.trim() && q) setSegmentNotes(q.slice(0, 400))
     } catch (e) {
       console.error(e)
       setError(e.message || 'Pfad-Vorschlag fehlgeschlagen')
       setPathSteps([])
       setTargetSummary(null)
+      setSemanticBrief(null)
+      setPathQa(null)
     } finally {
       setLoading(false)
     }
@@ -122,6 +132,8 @@ export default function ExerciseProgressionPathBuilder({
       })
       setPathSteps([])
       setTargetSummary(null)
+      setSemanticBrief(null)
+      setPathQa(null)
       if (typeof onSaved === 'function') await onSaved()
       alert(`${n} Nachfolger-Kante(n) aus KI-Pfad gespeichert.`)
     } catch (e) {
@@ -142,8 +154,8 @@ export default function ExerciseProgressionPathBuilder({
     >
       <h3 style={{ marginTop: 0, fontSize: '1rem' }}>KI: Pfad zum Ziel</h3>
       <p style={{ fontSize: '12px', color: 'var(--text3)', marginTop: 0, lineHeight: 1.45 }}>
-        Ziel in Freitext formulieren — die Planungs-KI schlägt eine aufbauende Übungsreihe vor. Nach Review als
-        Nachfolger-Ketten in den aktiven Graph speichern (über mehrere Trainingspläne hinweg nutzbar).
+        Ziel in Freitext formulieren — die Planungs-KI schlägt eine semantisch passende, aufbauende Reihenfolge vor,
+        prüft Lücken (ggf. Brücken-Übungen) und optional per LLM-QS. Nach Review in den Graph speichern.
       </p>
       <div style={{ display: 'flex', flexWrap: 'wrap', gap: '10px', alignItems: 'flex-end' }}>
         <div className="form-row" style={{ flex: '2 1 240px', marginBottom: 0 }}>
@@ -184,15 +196,59 @@ export default function ExerciseProgressionPathBuilder({
         </p>
       ) : null}
 
-      {targetSummary && pathSteps.length > 0 ? (
+      {(semanticBrief || targetSummary) && pathSteps.length > 0 ? (
         <div style={{ marginTop: '10px', display: 'flex', flexWrap: 'wrap', gap: '6px' }}>
-          {Array.isArray(targetSummary.focus_areas) &&
-            targetSummary.focus_areas.slice(0, 2).map((fa) => (
+          {semanticBrief?.primary_topic ? (
+            <span className="exercise-tag" style={{ borderColor: 'var(--accent)' }}>
+              Thema: {semanticBrief.primary_topic}
+            </span>
+          ) : null}
+          {Array.isArray(semanticBrief?.development_arc) &&
+            semanticBrief.development_arc.slice(0, 3).map((phase) => (
+              <span key={phase} className="exercise-tag">
+                {phase}
+              </span>
+            ))}
+          {Array.isArray(targetSummary?.focus_areas) &&
+            targetSummary.focus_areas.slice(0, 1).map((fa) => (
               <span key={fa} className="exercise-tag">
                 Fokus: {fa}
               </span>
             ))}
-          {Array.isArray(targetSummary.top_skills) &&
+        </div>
+      ) : null}
+
+      {pathQa && pathSteps.length > 0 ? (
+        <div
+          style={{
+            marginTop: '10px',
+            padding: '10px 12px',
+            borderRadius: '8px',
+            background: pathQa.overall_ok ? 'color-mix(in srgb, var(--accent) 8%, var(--surface2))' : 'color-mix(in srgb, var(--danger) 8%, var(--surface2))',
+            fontSize: '12px',
+            lineHeight: 1.45,
+          }}
+        >
+          <strong>
+            Pfad-QS: {pathQa.overall_ok ? 'OK' : 'Hinweise'}
+            {pathQa.quality_score != null ? ` (${Math.round(Number(pathQa.quality_score) * 100)} %)` : ''}
+          </strong>
+          {pathQa.topic_coverage ? (
+            <p style={{ margin: '6px 0 0', color: 'var(--text2)' }}>{pathQa.topic_coverage}</p>
+          ) : null}
+          {Array.isArray(pathQa.issues) && pathQa.issues.length > 0 ? (
+            <ul style={{ margin: '6px 0 0', paddingLeft: '16px', color: 'var(--text2)' }}>
+              {pathQa.issues.slice(0, 4).map((issue) => (
+                <li key={issue}>{issue}</li>
+              ))}
+            </ul>
+          ) : null}
+          {Number(pathQa.bridge_insert_count) > 0 ? (
+            <p style={{ margin: '6px 0 0', color: 'var(--accent-dark)' }}>
+              {pathQa.bridge_insert_count} Brücken-Übung(en) eingefügt (Lückenfüller).
+            </p>
+          ) : null}
+          {Array.isArray(targetSummary?.top_skills) &&
             targetSummary.top_skills.slice(0, 2).map((sk) => (
               <span key={sk.skill_id} className="exercise-tag">
                 {sk.name}
@@ -220,6 +276,7 @@ export default function ExerciseProgressionPathBuilder({
                 <div className="form-row" style={{ marginBottom: 0 }}>
                   <label className="form-label">
                     Schritt {idx + 1}
+                    {step.isBridge ? ' (Brücke)' : ''}
                     {idx === 0 ? ' (Einstieg)' : idx === pathSteps.length - 1 ? ' (Zielnähe)' : ''}
                   </label>
                   <div style={{ fontSize: '13px' }}>