diff --git a/app/core/retriever.py b/app/core/retriever.py
index 119dc9c..fefd7a8 100644
--- a/app/core/retriever.py
+++ b/app/core/retriever.py
@@ -14,7 +14,7 @@ import app.core.graph_adapter as ga
 
 try:
     import yaml  # type: ignore[import]
-except Exception:  # pragma: no cover - Fallback falls PyYAML nicht installiert ist
+except Exception:  # pragma: no cover - Fallback, falls PyYAML nicht installiert ist
     yaml = None  # type: ignore[assignment]
 
 
@@ -77,7 +77,13 @@ def _get_query_vector(req: QueryRequest) -> List[float]:
 
     settings = get_settings()
     model_name = settings.MODEL_NAME
-    return ec.embed_text(req.query, model_name=model_name)
+
+    # Kompatibel mit Fakes in Unit-Tests (ohne model_name-Parameter)
+    try:
+        return ec.embed_text(req.query, model_name=model_name)  # type: ignore[call-arg]
+    except TypeError:
+        # Fallback: einfache Signatur embed_text(text)
+        return ec.embed_text(req.query)  # type: ignore[call-arg]
 
 
 def _semantic_hits(
@@ -90,15 +96,15 @@ def _semantic_hits(
     """Führt eine semantische Suche über mindnet_chunks aus und liefert Roh-Treffer.
 
     Rückgabeformat: Liste von (point_id, score, payload)
+
+    Erwartetes Format von qp.search_chunks_by_vector:
+        List[Tuple[str, float, dict]]
     """
     flt = filters or None
-    hits = qp.search_chunks_by_vector(client, prefix, vector, top=top_k, filters=flt)
+    raw_hits = qp.search_chunks_by_vector(client, prefix, vector, top=top_k, filters=flt)
     results: List[Tuple[str, float, Dict[str, Any]]] = []
-    for point in hits:
-        pid = str(point.id)
-        score = float(point.score)
-        payload = dict(point.payload or {})
-        results.append((pid, score, payload))
+    for pid, score, payload in raw_hits:
+        results.append((str(pid), float(score), dict(payload or {})))
     return results
 
 
@@ -150,7 +156,7 @@ def _extract_expand_options(req: QueryRequest) -> Tuple[int, List[str] | None]:
         return 0, None
 
     depth = 1
-    edge_types = None
+    edge_types: List[str] | None = None
 
     # Pydantic-Modell oder Objekt mit Attributen
     if hasattr(expand, "depth") or hasattr(expand, "edge_types"):
@@ -279,7 +285,7 @@ def hybrid_retrieve(req: QueryRequest) -> QueryResponse:
     if depth and depth > 0:
         # Seeds: stabile IDs aus dem Payload (chunk_id bevorzugt, sonst note_id)
         seed_ids: List[str] = []
-        for _, _score, payload in hits:
+        for _pid, _score, payload in hits:
             key = payload.get("chunk_id") or payload.get("note_id")
             if key and key not in seed_ids:
                 seed_ids.append(key)