fix: harden reviewer workflow and memory handling

harness/tests/test_solved_search_cli.py

@@ -7,10 +7,12 @@ puro (`[]` in modalita' --json) ed exit 0, cosi' il modello prosegue senza
 exemplar. Il finalize-hook gestisce gia' lo stesso scenario in modo analogo.
 """
 import json
+from datetime import datetime
 
 from typer.testing import CliRunner
 
 from tht.cli import app
+from tht.memory import MemoryRecord, save_registry
 from tht.ports.vector import VectorReadUnavailable
 from tht.vectorstore.rest_client import VectorRestError
 from tht.vectorstore.store import VectorHit
@@ -97,3 +99,44 @@ def test_solved_search_json_maps_hit_metadata(tmp_path, monkeypatch):
         "session_id": "s1", "question": "quante ablazioni nel 2023",
         "sql": "SELECT 1", "tables": ["fact_seeablazione"], "score": 0.91,
     }]
+
+
+def test_memory_search_excludes_legacy_table_records(tmp_path, monkeypatch):
+    records = [
+        MemoryRecord(
+            id="mem-0001", ts=datetime(2026, 1, 1), session_id="s1",
+            decision_seq=1, type="table_promoted", subject="fact_pazienti",
+        ),
+        MemoryRecord(
+            id="mem-0002", ts=datetime(2026, 1, 1), session_id="s1",
+            decision_seq=2, type="concept_clarified", subject="paziente attivo",
+            detail="flag_attivo = TRUE",
+        ),
+    ]
+    cfg = _cfg(tmp_path)
+    save_registry(records, tmp_path / "a" / "memory" / "registry.jsonl")
+
+    class FakeSearcher:
+        def search(self, vec, top_n=10, kinds=None):
+            return [
+                VectorHit(
+                    id=f"memory:{record.id}", kind="memory", ref=record.id,
+                    title=record.subject, content=record.detail, metadata={},
+                    similarity=0.9,
+                )
+                for record in records
+            ]
+
+    class FakeEmbedder:
+        def embed_query(self, text):
+            return [0.1] * 8
+
+    monkeypatch.setattr("tht.cli.vector_cmd.open_searcher", lambda workspace: FakeSearcher())
+    monkeypatch.setattr("tht.cli.vector_cmd.make_embedder", lambda embeddings: FakeEmbedder())
+
+    res = CliRunner().invoke(
+        app, ["memory", "search", "pazienti", "--json", "-c", str(cfg)]
+    )
+
+    assert res.exit_code == 0, res.output
+    assert [record["id"] for record in json.loads(res.stdout)] == ["mem-0002"]