tests/test_retriever_basic.py hinzugefügt

tests/test_retriever_basic.py

@@ -0,0 +1,64 @@
+from app.core import retriever as r
+from app.models.dto import QueryRequest
+import app.core.qdrant as qdr
+import app.core.qdrant_points as qp
+import app.services.embeddings_client as ec
+
+
+class _DummyClient:
+    """Minimaler Platzhalter für QdrantClient in Unit-Tests."""
+    pass
+
+
+def _fake_get_client(cfg):
+    return _DummyClient()
+
+
+def _fake_embed_text(text: str):
+    # Liefert stabilen Vektor; Größe ist für diesen Test egal,
+    # da search_chunks_by_vector ebenfalls gefaked wird.
+    return [0.0] * 16
+
+
+def _fake_search_chunks_by_vector(client, prefix, vector, top=10, filters=None):
+    # einfache Hitliste in absteigender Score-Reihenfolge
+    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", 0.4, {"note_id": "note:3", "path": "c.md", "section_title": "S3"}),
+    ]
+
+
+def test_semantic_retrieve_basic(monkeypatch):
+    # Qdrant- und Embedding-Aufrufe faken
+    monkeypatch.setattr(qdr, "get_client", _fake_get_client)
+    monkeypatch.setattr(ec, "embed_text", _fake_embed_text)
+    monkeypatch.setattr(qp, "search_chunks_by_vector", _fake_search_chunks_by_vector)
+
+    req = QueryRequest(mode="semantic", query="karate trainingsplan", top_k=2)
+    resp = r.semantic_retrieve(req)
+
+    assert resp.used_mode == "semantic"
+    assert len(resp.results) == 2
+
+    # Scores absteigend sortiert
+    scores = [h.total_score for h in resp.results]
+    assert scores[0] >= scores[1]
+    # Korrekte Zuordnung von note_id und Quelle
+    assert resp.results[0].note_id == "note:1"
+    assert resp.results[0].source["path"] == "a.md"
+
+
+def test_hybrid_retrieve_basic(monkeypatch):
+    # Qdrant- und Embedding-Aufrufe faken
+    monkeypatch.setattr(qdr, "get_client", _fake_get_client)
+    monkeypatch.setattr(qp, "search_chunks_by_vector", _fake_search_chunks_by_vector)
+
+    # Im Hybrid-Modus arbeiten wir im Step-1-Stand nur mit query_vector
+    req = QueryRequest(mode="hybrid", query_vector=[0.0] * 16, top_k=2)
+    resp = r.hybrid_retrieve(req)
+
+    assert resp.used_mode == "hybrid"
+    assert len(resp.results) == 2
+    scores = [h.total_score for h in resp.results]
+    assert scores[0] >= scores[1]