feat(harness): arricchisci metadata memory (subject/detail/rationale) — Onda 3.1 TDD

Correzione del gap ereditato (resosi NECESSARIO dal drop del registry, spec 5): il
metadata del VectorRecord memory ora porta subject/detail/rationale oltre a
type/session_id/tables/concepts. pack_metadata li serializza nel jsonb via
**record.metadata. search_similar proietta metadata completo -> la F2 ricostruisce
la decisione direttamente dall'hit, senza lookup registro.

L1: 4 test (subject/detail/rationale presenti, campi esistenti preservati,
no cross-contamination multi-record, save_one_memory propaga il metadata alla riga).
Suite: 165 passed.

harness/tests/test_memory_metadata.py

@@ -0,0 +1,67 @@
+"""L1: arricchimento metadata memory — subject/detail/rationale nel jsonb (decisione spec 6).
+
+Una volta che il registry e' droppato (decisione spec 5), il vectordb e' l'unica
+fonte delle memory. L'hit di search_similar deve bastare per ricostruire la decisione
+completa. Per questo memory_vector_records mette subject/detail/rationale nel metadata
+del VectorRecord (pack_metadata li serializza nel jsonb via **record.metadata).
+"""
+from datetime import datetime
+
+from tht.memory import MemoryRecord, memory_vector_records
+
+
+def _record(**kw) -> MemoryRecord:
+    base = dict(
+        id="mem-x", ts=datetime(2025, 1, 1), session_id="s", decision_seq=1,
+        type="table_promoted", subject="dim_pazienti", detail="promossa",
+        rationale="perche' serve", question_context="dammi pazienti",
+        tables=["t"], concepts=[],
+    )
+    base.update(kw)
+    return MemoryRecord(**base)
+
+
+def test_memory_vector_record_has_subject_detail_rationale_in_metadata():
+    vr = memory_vector_records([_record()])[0]
+    assert vr.metadata["subject"] == "dim_pazienti"
+    assert vr.metadata["detail"] == "promossa"
+    assert vr.metadata["rationale"] == "perche' serve"
+
+
+def test_memory_vector_record_metadata_keeps_existing_fields():
+    vr = memory_vector_records([_record()])[0]
+    # i campi che gia' c'erano restano (backward compat)
+    assert vr.metadata["type"] == "table_promoted"
+    assert vr.metadata["tables"] == ["t"]
+    assert vr.metadata["concepts"] == []
+    assert vr.metadata["session_id"] == "s"
+
+
+def test_each_record_carries_its_own_subject():
+    # piu' record, ciascuno con il proprio subject (no cross-contamination)
+    recs = [
+        _record(id="m1", subject="t_a", detail="d_a", rationale="r_a"),
+        _record(id="m2", subject="t_b", detail="d_b", rationale="r_b"),
+    ]
+    out = memory_vector_records(recs)
+    assert {v.metadata["subject"] for v in out} == {"t_a", "t_b"}
+    assert {v.metadata["detail"] for v in out} == {"d_a", "d_b"}
+
+
+def test_save_one_memory_preserves_subject_through_upsert_row():
+    # end-to-end: save_one_memory costruisce la riga upsert; il metadata deve arrivare
+    # con subject/detail/rationale (pack_metadata lo prende da record.metadata).
+    from unittest.mock import MagicMock
+
+    from tht.memory import save_one_memory
+
+    writer = MagicMock()
+    writer.upsert_records.return_value = 1
+    embedder = MagicMock()
+    embedder.embed_documents.return_value = [[0.0] * 8]
+    save_one_memory([_record(decision_seq=1)], decision_seq=1, writer=writer, embedder=embedder)
+    row = writer.upsert_records.call_args[0][1][0]
+    md = row["metadata"]
+    assert md["subject"] == "dim_pazienti"
+    assert md["detail"] == "promossa"
+    assert md["rationale"] == "perche' serve"

harness/tht/memory.py

@@ -207,6 +207,7 @@ def memory_vector_records(records: list[MemoryRecord]) -> list[VectorRecord]:
                 metadata={
                     "type": r.type, "session_id": r.session_id,
                     "tables": r.tables, "concepts": r.concepts,
+                    "subject": r.subject, "detail": r.detail, "rationale": r.rationale,
                 },
             )
         )