refactor: remove pgvector runtime

harness/tht/cli/evidence_cmd.py

@@ -2,9 +2,9 @@ from pathlib import Path
 
 import typer
 
+from tht.cli._guards import require_vector_write_allowed
 from tht.cli.config_cmd import CONFIG_OPT
 from tht.cli.schema_cmd import _load_config_or_exit
-from tht.cli._guards import require_vector_write_allowed
 
 evidence_app = typer.Typer(help="Generazione e gestione delle evidence")
 
@@ -56,12 +56,13 @@ def extract_cmd(config: Path = CONFIG_OPT) -> None:
 
 @evidence_app.command("index")
 def index_cmd(config: Path = CONFIG_OPT) -> None:
-    """Embedda e sincronizza su pgvector tutte le evidence presenti in artifacts/."""
+    """Embedda e sincronizza nel semantic store tutte le evidence presenti in artifacts/."""
+    from tht.adapters.factory import build_vector_store
     from tht.cli.vector_cmd import (
         _print_stats,
         make_embedder,
-        open_store,
         require_vector_cfg,
+        sync_canonical_records,
     )
     from tht.evidence.model import load_evidence_dir
     from tht.vectorstore.records import evidence_records
@@ -71,6 +72,10 @@ def index_cmd(config: Path = CONFIG_OPT) -> None:
     require_vector_cfg(cfg)
     docs = load_evidence_dir(evidence_root(cfg))
     records = evidence_records(docs, cfg.vector.max_chunk_chars)
-    store = open_store(cfg, "evidence")
-    stats = store.sync(records, make_embedder(cfg.embeddings), kinds={"evidence"})
+    stats = sync_canonical_records(
+        "evidence",
+        records,
+        store=build_vector_store(cfg, require_write=True),
+        embedder=make_embedder(cfg.embeddings),
+    )
     _print_stats(stats)

harness/tht/cli/memory_cmd.py

@@ -11,7 +11,6 @@ import typer
 from sqlalchemy.exc import OperationalError, ProgrammingError
 
 from tht.cli._guards import (
-    has_vector_write_rest,
     require_server_profile,
     require_vector_write_allowed,
 )
@@ -45,15 +44,8 @@ def _resync_memory(cfg):
 
 def clear_memory_index(cfg):
     from tht.adapters.factory import build_vector_store
-    from tht.cli.vector_cmd import make_embedder, open_store, require_direct_vector_cfg
 
-    if cfg.vectors is not None and cfg.vectors.type == "qdrant":
-        return build_vector_store(cfg, require_write=True).delete_kinds("memory", ["memory"])
-
-    require_direct_vector_cfg(cfg)
-    legacy_store = open_store(cfg, "memory")
-    legacy_store.sync([], make_embedder(cfg.embeddings), kinds={"memory"})
-    return 0
+    return build_vector_store(cfg, require_write=True).delete_kinds("memory", ["memory"])
 
 
 @memory_app.command("promote")
@@ -451,19 +443,13 @@ def search_cmd(
 def index_solved_session(cfg, session_id: str) -> int:
     """Indicizza la coppia domanda->SQL della sessione (kind solved_question).
 
-    Solleva RuntimeError se manca la writer key e SolvedIndexError se mancano gli
-    artefatti: il finalize li degrada a warning, il comando CLI li converte in
-    errori espliciti."""
+    Solleva SolvedIndexError se mancano gli artefatti: il finalize lo degrada a warning,
+    il comando CLI lo converte in errore esplicito."""
     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
 
-    if not has_vector_write_rest(cfg):
-        raise RuntimeError(
-            "vector_write_rest assente: la coppia domanda->SQL si indicizza solo con la "
-            "writer key configurata nel workspace yaml"
-        )
     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(
@@ -518,10 +504,9 @@ def solved_search_cmd(
     from rich.table import Table
 
     from tht.cli.vector_cmd import make_embedder, open_searcher
-    from tht.ports.vector import VectorReadUnavailable
+    from tht.ports.vector import VectorReadUnavailable, VectorStoreError
     from tht.solved import SOLVED_KIND
     from tht.vectorstore.embeddings import EmbeddingsError
-    from tht.vectorstore.rest_client import VectorRestError
 
     cfg = _load_config_or_exit(config)
     require_vector_cfg(cfg)
@@ -532,7 +517,7 @@ def solved_search_cmd(
         searcher = open_searcher(cfg)
         embedder = make_embedder(cfg.embeddings)
         hits = searcher.search(embedder.embed_query(question), top_n=top, kinds=[SOLVED_KIND])
-    except (VectorRestError, VectorReadUnavailable, EmbeddingsError, OperationalError) as e:
+    except (VectorStoreError, VectorReadUnavailable, EmbeddingsError, OperationalError) as e:
         typer.secho(
             f"ATTENZIONE: exemplar non disponibili ({e}). Prosegui senza.",
             fg=typer.colors.YELLOW, err=True,

harness/tht/cli/search_cmd.py

@@ -255,11 +255,10 @@ def pack_cmd(
     from sqlalchemy.exc import OperationalError
 
     from tht.cli.vector_cmd import make_embedder, open_searcher, require_vector_cfg
-    from tht.ports.vector import VectorReadUnavailable
+    from tht.ports.vector import VectorReadUnavailable, VectorStoreError
     from tht.search import combined_search, schema_tables
     from tht.solved import SOLVED_KIND
     from tht.vectorstore.embeddings import EmbeddingsError
-    from tht.vectorstore.rest_client import VectorRestError
 
     cfg = _load_config_or_exit(config)
     from tht.search.evidence import validate_corpus_workspace
@@ -273,7 +272,7 @@ def pack_cmd(
     evidence: list[dict] = []
     solved: list[dict] = []
     warnings: list[str] = []
-    degrade = (VectorRestError, VectorReadUnavailable, EmbeddingsError, OperationalError)
+    degrade = (VectorStoreError, VectorReadUnavailable, EmbeddingsError, OperationalError)
 
     vec = None
     searcher = embedder = None

harness/tht/cli/vector_cmd.py

@@ -2,11 +2,7 @@ from pathlib import Path
 
 import typer
 
-from tht.cli._guards import (
-    has_vector_write_rest,
-    require_server_profile,
-    require_vector_write_allowed,
-)
+from tht.cli._guards import require_server_profile, require_vector_write_allowed
 from tht.cli.config_cmd import CONFIG_OPT
 from tht.cli.schema_cmd import _load_config_or_exit, annotations_path, physical_path
 from tht.ports.vector import VectorWriteRecord
@@ -26,8 +22,8 @@ def require_vector_cfg(cfg):
     missing = []
     if cfg.embeddings is None:
         missing.append("embeddings")
-    if cfg.vectors is None and cfg.vector_db is None and not has_vector_write_rest(cfg):
-        missing.append("vectors o vector_db o vector_write_rest")
+    if cfg.vectors is None:
+        missing.append("vectors")
     if missing:
         typer.secho(
             f"ERRORE: sezioni mancanti nel workspace yaml: {', '.join(missing)}.",
@@ -36,27 +32,6 @@ def require_vector_cfg(cfg):
         raise typer.Exit(code=1)
 
 
-def require_direct_vector_cfg(cfg):
-    missing = [k for k in ("vector_db", "embeddings") if getattr(cfg, k) is None]
-    if missing:
-        typer.secho(
-            f"ERRORE: sezioni mancanti nel workspace yaml: {', '.join(missing)}.",
-            fg=typer.colors.RED, err=True,
-        )
-        raise typer.Exit(code=1)
-
-
-def open_store(cfg, table: str):
-    """Writer table-scoped per il LOADING.
-
-    Sul server preferisce la connessione diretta. In profilo workstation usa `vector_write_rest`
-    se configurato, con upsert remoto non distruttivo.
-    """
-    from tht.adapters.factory import build_vector_loader
-
-    return build_vector_loader(cfg, table)
-
-
 def open_searcher(cfg):
     """Searcher per la LETTURA (similarity search): via REST se `vector_rest` è configurato,
     altrimenti connessione diretta (dev/test)."""
@@ -115,20 +90,20 @@ def init_cmd(
         False, "--skip-ollama-check", help="Non verificare la raggiungibilita' di Ollama."
     ),
 ) -> None:
-    """Crea schema e tabella pgvector (idempotente) e verifica le connessioni."""
-    from sqlalchemy.exc import OperationalError
-
+    """Verifica il runtime Qdrant e la raggiungibilita' dell'embedder configurato."""
+    from tht.adapters.factory import build_vector_store
     from tht.vectorstore.embeddings import EmbeddingsError
-    from tht.vectorstore.reader import ALL_TABLES
 
     cfg = _load_config_or_exit(config)
     require_server_profile(cfg, "vector init")
-    require_direct_vector_cfg(cfg)
-    try:
-        for table in ALL_TABLES:
-            open_store(cfg, table).init_schema()
-    except OperationalError as e:
-        typer.secho(f"ERRORE connessione pgvector: {e.orig}", fg=typer.colors.RED, err=True)
+    require_vector_cfg(cfg)
+    health = build_vector_store(cfg, require_write=True).health()
+    if not health.ok:
+        typer.secho(
+            f"ERRORE runtime vettoriale: {health.detail or 'Qdrant non raggiungibile o incompatibile'}",
+            fg=typer.colors.RED,
+            err=True,
+        )
         raise typer.Exit(code=1)
     if not skip_ollama_check:
         try:
@@ -137,8 +112,8 @@ def init_cmd(
             typer.secho(f"ERRORE: {e}", fg=typer.colors.RED, err=True)
             raise typer.Exit(code=1)
     typer.secho(
-        f"OK: schema {cfg.vector_db.db_schema} pronto (tabelle: {', '.join(ALL_TABLES)}) su "
-        f"{cfg.vector_db.host}:{cfg.vector_db.port}", fg=typer.colors.GREEN,
+        f"OK: runtime Qdrant pronto per la collezione {cfg.vectors.collection}",
+        fg=typer.colors.GREEN,
     )