tests/test_query_text_embed_unit.py hinzugefügt

tests/test_query_text_embed_unit.py

@@ -0,0 +1,39 @@
+from fastapi.testclient import TestClient
+from app.main import create_app
+import app.services.embeddings_client as ec
+import app.core.qdrant_points as qp
+import app.core.graph_adapter as ga
+
+def _fake_embed_text(text: str):
+    # Liefert stabilen 384-d Vektor ohne echtes Modell
+    return [0.01] * 384
+
+def _fake_search_chunks_by_vector(client, prefix, vector, top=10, filters=None):
+    # einfache Hitliste
+    return [
+        ("chunk:1", 0.9, {"note_id":"note:1","path":"a.md","section_title":"S1"}),
+        ("chunk:2", 0.7, {"note_id":"note:2","path":"b.md","section_title":"S2"}),
+        ("chunk:3", 0.5, {"note_id":"note:3","path":"c.md","section_title":"S3"}),
+    ]
+
+def _fake_get_edges_for_sources(client, prefix, source_ids, edge_types=None, limit=2048):
+    out=[]
+    for sid in source_ids:
+        out.append({"source_id":sid,"target_id":f"{sid}-x","kind":"references","weight":0.2})
+    return out
+
+def test_query_with_text(monkeypatch):
+    # Patch Embedding + Qdrant-Aufrufe
+    monkeypatch.setattr(ec, "embed_text", _fake_embed_text)
+    monkeypatch.setattr(qp, "search_chunks_by_vector", _fake_search_chunks_by_vector)
+    monkeypatch.setattr(ga, "get_edges_for_sources", _fake_get_edges_for_sources, raising=False)
+
+    app = create_app()
+    with TestClient(app) as c:
+        payload = {"mode":"hybrid","query":"karate trainingsplan", "top_k":2,
+                   "expand":{"depth":1,"edge_types":["references"]}}
+        r = c.post("/query", json=payload)
+        assert r.status_code == 200, r.text
+        body = r.json()
+        assert body["used_mode"] == "hybrid"
+        assert len(body["results"]) == 2