diff --git a/app/core/retriever.py b/app/core/retriever.py
new file mode 100644
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+++ b/app/core/retriever.py
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+"""
+app/core/retriever.py — Semantischer/Edge-Aware/Hybrid Retriever (WP-04)
+
+Zweck:
+    Kandidatenfindung via Vektorsuche in *_chunks, optionale Edge-Expansion und
+    kombiniertes Ranking zur Rückgabe von Top-K Treffern.
+Kompatibilität:
+    Python 3.12+, qdrant-client 1.x
+Version:
+    0.1.0  (Erstanlage)
+Stand:
+    2025-10-07
+Bezug:
+    - app/core/graph_adapter.py (expand)
+    - app/core/ranking.py (combine_scores)
+    - app/core/qdrant_points.py (search_chunks_by_vector)
+Nutzung:
+    from app.core.retriever import hybrid_retrieve
+Änderungsverlauf:
+    0.1.0 (2025-10-07) – Erstanlage.
+"""
+
+from __future__ import annotations
+import time
+from typing import Dict, List, Optional, Tuple
+from qdrant_client import QdrantClient
+
+from app.models.dto import QueryRequest, QueryResponse, QueryHit
+from app.core.ranking import combine_scores
+from app.core.graph_adapter import expand
+from app.core import qdrant_points as qp
+from app.config import get_settings
+
+
+def _require_query_vector(req: QueryRequest) -> List[float]:
+    """
+    Für den Schnelltest ohne eingebundene Embeddings muss query_vector gesetzt sein.
+    Später kann hier der Embed-Aufruf (Text → 384d) angebunden werden.
+    """
+    if not req.query_vector:
+        raise ValueError(
+            "query_vector fehlt. Für den Quick-Test ohne Embeddings bitte einen 384d-Vektor übergeben."
+        )
+    return req.query_vector
+
+
+def semantic_retrieve(req: QueryRequest) -> QueryResponse:
+    """Nur semantische Kandidaten, keine Edge-Expansion (depth=0)."""
+    t0 = time.time()
+    s = get_settings()
+    client = QdrantClient(url=s.QDRANT_URL, api_key=s.QDRANT_API_KEY)
+
+    q_vec = _require_query_vector(req)
+    raw_hits = qp.search_chunks_by_vector(client, s.COLLECTION_PREFIX, q_vec, top=req.top_k, filters=req.filters)
+    id2payload = {pid: payload for (pid, score, payload) in raw_hits}
+
+    results: List[QueryHit] = []
+    for pid, s_score, payload in raw_hits:
+        results.append(QueryHit(
+            node_id=pid,
+            note_id=payload.get("note_id"),
+            semantic_score=float(s_score),
+            edge_bonus=0.0,
+            centrality_bonus=0.0,
+            total_score=float(s_score),  # hier un-normalisiert; ok für schnelle Prüfung
+            paths=None,
+            source={"path": payload.get("path"), "section": payload.get("section_title")}
+        ))
+
+    dt = int((time.time() - t0) * 1000)
+    return QueryResponse(results=results, used_mode="semantic", latency_ms=dt)
+
+
+def hybrid_retrieve(req: QueryRequest) -> QueryResponse:
+    """Semantik + Edge-Expansion + kombiniertes Ranking."""
+    t0 = time.time()
+    s = get_settings()
+    client = QdrantClient(url=s.QDRANT_URL, api_key=s.QDRANT_API_KEY)
+
+    q_vec = _require_query_vector(req)
+
+    # 1) Semantische Seeds (top_k * 3 für breitere Basis)
+    raw_hits = qp.search_chunks_by_vector(client, s.COLLECTION_PREFIX, q_vec, top=req.top_k * 3, filters=req.filters)
+    id2payload = {pid: payload for (pid, score, payload) in raw_hits}
+    seeds = [pid for (pid, _, _) in raw_hits]
+
+    # 2) Edge-Expansion
+    edge_types = req.expand.get("edge_types") if req.expand else None
+    depth = req.expand.get("depth", 1) if req.expand else 1
+    sg = expand(client, s.COLLECTION_PREFIX, seeds, depth=depth, edge_types=edge_types)
+
+    edge_bonus_map = {pid: sg.aggregate_edge_bonus(pid) for pid in seeds}
+    centrality_map = {pid: sg.centrality_bonus(pid) for pid in seeds}
+
+    # 3) Combined Ranking
+    scored = combine_scores(raw_hits, edge_bonus_map, centrality_map,
+                            w_sem=s.RETRIEVER_W_SEM,
+                            w_edge=s.RETRIEVER_W_EDGE,
+                            w_cent=s.RETRIEVER_W_CENT)
+
+    # 4) Antwortobjekte (Chunk-Ebene)
+    results: List[QueryHit] = []
+    for pid, total, e, c, s_score in scored[: req.top_k]:
+        payload = id2payload[pid]
+        results.append(QueryHit(
+            node_id=pid,
+            note_id=payload.get("note_id"),
+            semantic_score=float(s_score),
+            edge_bonus=float(e),
+            centrality_bonus=float(c),
+            total_score=float(total),
+            paths=None,
+            source={"path": payload.get("path"), "section": payload.get("section_title")}
+        ))
+
+    dt = int((time.time() - t0) * 1000)
+    return QueryResponse(results=results, used_mode="hybrid", latency_ms=dt)