refactor(memory): own solved-question lifecycle (#24)

harness/tht/cli/memory_cmd.py

@@ -403,12 +403,12 @@ def index_solved_session(cfg, session_id: str) -> int:
     from tht.adapters.factory import build_vector_store
     from tht.cli.sql_cmd import promoted_tables_for
     from tht.cli.vector_cmd import make_embedder
-    from tht.solved import build_solved_snapshot, save_solved_question
+    from tht.memory import index_solved_question
 
     store = build_vector_store(cfg, require_write=True)
-    record = build_solved_snapshot(load_snapshot_or_exit(cfg, session_id), promoted_tables_for(cfg, session_id))
-    return save_solved_question(
-        record,
+    return index_solved_question(
+        load_snapshot_or_exit(cfg, session_id),
+        promoted_tables_for(cfg, session_id),
         store=store,
         embedder=make_embedder(cfg.embeddings),
     )
@@ -423,7 +423,7 @@ def solved_index_cmd(
     """Indicizza la coppia domanda->SQL nel semantic store (backfill; il finalize lo fa da solo)."""
     import json as _json
 
-    from tht.solved import SolvedIndexError
+    from tht.memory import SolvedIndexError
 
     cfg = _load_config_or_exit(config)
     require_vector_write_allowed(cfg, "memory solved-index")
@@ -460,7 +460,7 @@ def solved_search_cmd(
 
     from tht.cli.vector_cmd import make_embedder, open_searcher
     from tht.ports.vector import VectorReadUnavailable, VectorStoreError
-    from tht.solved import SOLVED_KIND
+    from tht.memory import search_solved_questions
     from tht.vectorstore.embeddings import EmbeddingsError
 
     cfg = _load_config_or_exit(config)
@@ -471,7 +471,12 @@ def solved_search_cmd(
     try:
         searcher = open_searcher(cfg)
         embedder = make_embedder(cfg.embeddings)
-        hits = searcher.search(embedder.embed_query(question), top_n=top, kinds=[SOLVED_KIND])
+        results = search_solved_questions(
+            question,
+            searcher=searcher,
+            embedder=embedder,
+            top=top,
+        )
     except (VectorStoreError, VectorReadUnavailable, EmbeddingsError, OperationalError) as e:
         typer.secho(
             f"ATTENZIONE: exemplar non disponibili ({e}). Prosegui senza.",
@@ -480,16 +485,6 @@ def solved_search_cmd(
         if json_out:
             typer.echo("[]")
         return
-    results = [
-        {
-            "session_id": h.metadata.get("session_id", h.ref),
-            "question": h.metadata.get("question", h.content),
-            "sql": h.metadata.get("sql", ""),
-            "tables": h.metadata.get("tables", []),
-            "score": round(h.similarity, 4),
-        }
-        for h in hits
-    ]
     if json_out:
         typer.echo(json.dumps(results, ensure_ascii=False, indent=2))
         return

harness/tht/cli/search_cmd.py

@@ -257,7 +257,7 @@ def pack_cmd(
     from tht.cli.vector_cmd import make_embedder, open_searcher, require_vector_cfg
     from tht.ports.vector import VectorReadUnavailable, VectorStoreError
     from tht.search import combined_search, schema_tables
-    from tht.solved import SOLVED_KIND
+    from tht.memory import SOLVED_KIND
     from tht.vectorstore.embeddings import EmbeddingsError
 
     cfg = _load_config_or_exit(config)

harness/tht/cli/session_cmd.py

@@ -578,10 +578,11 @@ def finalize_cmd(session_id: str = typer.Argument(...), config: Path = CONFIG_OP
 
     # --- batteria di validazione su sql_final.sql ---
     assert sql is not None
+    promoted_tables = promoted_tables_for(cfg, session_id)
     check = validate_sql(
         sql,
         physical=_load_physical_or_exit(cfg),
-        promoted_tables=promoted_tables_for(cfg, session_id),
+        promoted_tables=promoted_tables,
         forbidden_functions=set(cfg.execution.forbidden_functions),
     )
     if not check.ok:
@@ -623,14 +624,29 @@ def finalize_cmd(session_id: str = typer.Argument(...), config: Path = CONFIG_OP
         repository, session_id, validation_report=report, evidence=evidence
     )
     # --- memoria attiva (parte B): indicizza la coppia domanda->SQL, best-effort ---
-    # Import lazy: memory_cmd importa da session_cmd (un import top-level qui sarebbe
-    # circolare). Qualunque errore (writer key assente, VPN giu', Ollama spento) NON
-    # deve bloccare il finalize: l'indice e' derivato e recuperabile con
-    # `tht memory solved-index <id>`.
+    # Qualunque errore (writer key assente, VPN giu', Ollama spento) NON deve
+    # bloccare il finalize: l'indice e' derivato e recuperabile con
+    # `tht memory solved-index <id>`. Memory owns this best-effort policy; core
+    # has already committed the authoritative finalized snapshot above.
     try:
-        from tht.cli.memory_cmd import index_solved_session
+        from tht.adapters.factory import build_vector_store
+        from tht.cli.vector_cmd import make_embedder
+        from tht.memory import index_solved_question_best_effort
 
-        if index_solved_session(cfg, session_id):
+        finalized_snapshot = repository.get(session_id)
+        outcome = index_solved_question_best_effort(
+            finalized_snapshot,
+            promoted_tables,
+            store_factory=lambda: build_vector_store(cfg, require_write=True),
+            embedder_factory=lambda: make_embedder(cfg.embeddings),
+        )
+        if outcome.error is not None:
+            typer.secho(
+                f"ATTENZIONE: coppia domanda->SQL non indicizzata ({outcome.error}). "
+                f"Recupera con `tht memory solved-index {session_id}`.",
+                fg=typer.colors.YELLOW, err=True,
+            )
+        elif outcome.upserted:
             typer.secho(
                 "OK: coppia domanda->SQL indicizzata nel vectordb (solved_question).",
                 fg=typer.colors.GREEN,