refactor(vector): define store contract

harness/tht/vectorstore/reader.py

@@ -9,8 +9,10 @@ Entrambe mappano i `kind` sulle tabelle per-dominio dello schema `vectors`.
 
 from sqlalchemy import Engine
 
+from tht.adapters.vector.legacy_direct import LegacyDirectVectorStore
+from tht.adapters.vector.thoth_http import ThothHttpVectorStore
 from tht.vectorstore.rest_client import VectorRestClient
-from tht.vectorstore.store import VectorHit, VectorStore, hit_from_metadata
+from tht.vectorstore.store import VectorHit
 
 # kind Thoth → tabella dello schema `vectors`.
 KIND_TO_TABLE = {
@@ -30,30 +32,19 @@ def tables_for_kinds(kinds: list[str] | None) -> list[str]:
     return sorted({KIND_TO_TABLE[k] for k in kinds if k in KIND_TO_TABLE})
 
 
-def _merge(hits: list[VectorHit], top_n: int) -> list[VectorHit]:
-    return sorted(hits, key=lambda h: h.similarity, reverse=True)[:top_n]
-
-
 class RestSearcher:
     """Similarity search via REST: una chiamata `search_similar` per tabella, poi fusione."""
 
     def __init__(self, client: VectorRestClient):
         self.client = client
+        self._store = ThothHttpVectorStore(reader=client, writer=None)
 
     def search(
         self, query_vec: list[float], top_n: int = 10, kinds: list[str] | None = None
     ) -> list[VectorHit]:
-        hits: list[VectorHit] = []
-        for table in tables_for_kinds(kinds):
-            for row in self.client.search_similar(table, query_vec, top_n, kinds=kinds):
-                hits.append(hit_from_metadata(row.get("similarity", 0.0), row.get("metadata")))
-        # Il filtro per kind avviene server-side (RPC con `kinds`); il post-filter resta
-        # come difesa per il fallback legacy (server pre-migrazione: 404 -> query senza
-        # filtro) e per parita' col path diretto (#25).
-        if kinds:
-            allowed = set(kinds)
-            hits = [h for h in hits if h.kind in allowed]
-        return _merge(hits, top_n)
+        return self._store.search(
+            tables_for_kinds(kinds), query_vec, limit=top_n, kinds=kinds
+        )
 
 
 class DirectSearcher:
@@ -63,13 +54,11 @@ class DirectSearcher:
         self.engine = engine
         self.schema = schema
         self.dim = dim
+        self._store = LegacyDirectVectorStore(engine, schema=schema, dim=dim)
 
     def search(
         self, query_vec: list[float], top_n: int = 10, kinds: list[str] | None = None
     ) -> list[VectorHit]:
-        hits: list[VectorHit] = []
-        for table in tables_for_kinds(kinds):
-            store = VectorStore(self.engine, schema=self.schema, table=table, dim=self.dim)
-            # passa kinds: dentro schema_records filtra schema_table vs schema_column (#25).
-            hits.extend(store.search(query_vec, top_n=top_n, kinds=kinds))
-        return _merge(hits, top_n)
+        return self._store.search(
+            tables_for_kinds(kinds), query_vec, limit=top_n, kinds=kinds
+        )