perf+calitate sugestii k-NN: matvec numpy, vot top-5 cu prag calibrat, indicatori import

- embeddings: corpus ca matrice numpy cu norme precalculate; suggest_nearest
  = un matvec (~0.6ms/query fata de ~500ms cosine pur-Python la 17k vectori)
- enrich_suggestions: vot ponderat cu similaritatea pe top-5 vecini (NUL =
  eticheta proprie); prag 0.5 -> 0.88, calibrat LOO pe corpusul SILVER
  (tools/mapare-llm/knn_calibrate.py): precizie 90.5% -> 93.1%, cod gresit
  preselectat 7.2% -> 4.7%; sub prag abtinere -> preselectie fuzzy
- UI: codul sugerat de sistem afisat explicit cu sursa si scorul, separat de
  lista fuzzy ("potrivire text"); indicator de progres reparat pe upload
  (display:inline anula .htmx-indicator) si adaugat pe pasii 2->3 si
  "Salveaza maparile"

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

tests/test_enrich_corpus_etichetat.py

@@ -113,6 +113,58 @@ def test_abtinere_sub_prag(conn, monkeypatch):
     assert out["sugestie_principala"] is None
 
 
+def _mock_embedding_multi(monkeypatch, vecini):
+    """Mock suggest_nearest cu o lista de vecini [(cod, sim, is_nul), ...]."""
+    import app.embeddings as emb
+    monkeypatch.setattr(emb, "has_corpus", lambda: True)
+    monkeypatch.setattr(
+        emb, "suggest_nearest",
+        lambda text, top_k=1: [
+            {"cod": c, "is_nul": n, "similaritate": s} for c, s, n in vecini
+        ][:top_k],
+    )
+
+
+def test_vot_topk_bate_top1_pe_etichete_contradictorii(conn, monkeypatch):
+    """Corpus cu etichete contradictorii pe denumiri aproape identice:
+    top-1 ar da OE-1, dar votul ponderat (2x OE-8 vs 1x OE-1) da OE-8."""
+    from app.mapping import enrich_suggestions
+    _mock_embedding_multi(monkeypatch, [
+        ("OE-1", 0.94, False),
+        ("OE-8", 0.93, False),
+        ("OE-8", 0.92, False),
+    ])
+    out = enrich_suggestions(conn, "INLOCUIRE ANVELOPE")
+    assert out["surse"]["embedding"] == "OE-8"
+    assert out["surse"]["embedding_similaritate"] == 0.93
+
+
+def test_vot_vecini_sub_prag_nu_voteaza(conn, monkeypatch):
+    """Vecinii sub EMB_MIN_SIMILARITATE nu intra in vot, chiar daca sunt majoritari."""
+    from app.mapping import enrich_suggestions, EMB_MIN_SIMILARITATE
+    _mock_embedding_multi(monkeypatch, [
+        ("OE-3", EMB_MIN_SIMILARITATE + 0.01, False),
+        ("OE-1", EMB_MIN_SIMILARITATE - 0.05, False),
+        ("OE-1", EMB_MIN_SIMILARITATE - 0.05, False),
+    ])
+    out = enrich_suggestions(conn, "CEVA NEVAZUT")
+    assert out["surse"]["embedding"] == "OE-3"
+
+
+def test_vot_nul_majoritar_supreseaza(conn, monkeypatch):
+    """Majoritate NUL in vecinatate -> supresie, chiar daca top-1 e un cod."""
+    from app.mapping import enrich_suggestions
+    _mock_embedding_multi(monkeypatch, [
+        ("OE-1", 0.93, False),
+        (None, 0.92, True),
+        (None, 0.92, True),
+    ])
+    out = enrich_suggestions(conn, "CEVA CARE SEAMANA CU GUNOI")
+    assert out["surse"]["embedding"] is None
+    assert out["surse"]["nul"] is True
+    assert out["sugestie_principala"] is None
+
+
 def test_vecin_knn_nul_supreseaza(conn, monkeypatch):
     from app.mapping import enrich_suggestions
     _mock_embedding(monkeypatch, None, 0.99, is_nul=True)   # vecin NUL peste prag