fix: scope workspace registry smoke cleanup

harness/tht/memory.py

@@ -299,10 +299,10 @@ def memory_vector_record_for_decision(
 def save_one_memory(
     records: list[MemoryRecord], decision_seq: int, *, store, embedder
 ) -> int:
-    """Targeted one-row upsert of a promoted decision to pgvector via the writer key
+    """Targeted one-row upsert of a promoted decision to the configured semantic store
     (spec D11). This is NOT a full vectorstore resync: it embeds and pushes a single
-    record, so a workstation with a writer key can publish one memory without
-    rebuilding the index. Returns the upsert count (0 if no record matched or the
+    record, so a memory can be published without rebuilding the index.
+    Returns the upsert count (0 if no record matched or the
     record is already up to date).
 
     Hash dedup client-side (spec §5.4): the SHA-256 of the content is compared with

harness/tht/search/__init__.py

@@ -59,8 +59,8 @@ def aggregate_lsh_multi(hits: list[dict]) -> dict[str, list[dict]]:
     grouped: dict[str, list[dict]] = {}
     for (table, _), row in best.items():
         grouped.setdefault(table, []).append(row)
-    for table in grouped:
-        grouped[table].sort(key=lambda r: r["score"], reverse=True)
+    for rows in grouped.values():
+        rows.sort(key=lambda r: r["score"], reverse=True)
     return grouped
 
 
@@ -99,7 +99,7 @@ def combined_search(
     kinds: list[str] | None,
     query_vec: list[float] | None = None,
 ) -> list[SearchResult]:
-    """Fonde LSH (valori di campo) e pgvector con Reciprocal Rank Fusion.
+    """Fonde LSH (valori di campo) e ricerca semantica con Reciprocal Rank Fusion.
 
     `query_vec` permette di riusare un embedding gia' calcolato della stessa
     keyword (es. `tht search pack`, che fa piu' ricerche sulla stessa domanda)."""

harness/tht/vectorstore/store.py

@@ -52,11 +52,13 @@ def hit_from_metadata(similarity: float, metadata: dict | None) -> VectorHit:
 
 
 class VectorStore:
-    """Tabella pgvector table-scoped: scrittura diretta (loading) su una tabella dello schema
-    `vectors`. La lettura via REST avviene su `search_similar`; questo store serve al loading e
-    alla lettura diretta (dev/test). Il contratto della tabella remota richiede `id` (BIGSERIAL),
-    `embedding vector(N)` e `metadata jsonb`; le colonne extra (`record_key`, `kind`,
-    `content_hash`) servono solo al loader e non sono esposte dalla REST."""
+    """Legacy table-scoped vector store retained for compatibility fixtures.
+
+    New operational semantic storage is handled by the Qdrant adapter. This class preserves
+    the older SQL-table contract used by historical tests and migration checks: `id`
+    (BIGSERIAL), `embedding vector(N)`, and `metadata jsonb`; the extra columns
+    (`record_key`, `kind`, `content_hash`) serve only the loader and are not exposed by REST.
+    """
 
     def __init__(
         self, engine: Engine, schema: str = "vectors", table: str = "records", dim: int = 768

harness/tht/workspace.py

@@ -4,8 +4,9 @@ Reads workspaces/<name>.yaml, expands ${VAR} from env, validates via the Config
 (ported from the reference implementation). Future migration to a DB store would replace only this module.
 
 La struttura YAML rispecchia esattamente tht/config.py:
-  database + rest (DWH), vector_rest + vector_write_rest (pgvector, doppia key top-level),
-  vector_db (loading diretto, server-only), embeddings, evidence, execution.
+  database/rest o resources.dwh per il DWH, resources.vector/resources.embeddings
+  per Qdrant/Ollama interni, più evidence ed execution. I vecchi campi vector_db e
+  vector_rest restano solo per leggere fixture legacy durante la migrazione.
 """
 from __future__ import annotations