fix: upsert in bounded chunks, recreate Qdrant indexes on rebuild, larger maintenance tmpfs

harness/tht/adapters/vector/qdrant.py

@@ -25,6 +25,7 @@ from tht.vectorstore.store import VectorHit, hit_from_metadata
 _GENERATION = re.compile(r"gen:[0-9a-f]{32}")
 _WORKSPACE = re.compile(r"[a-z][a-z0-9_-]{0,63}")
 _KEYWORD_INDEXES = (
+
     "content_hash",
     "document_id",
     "kind",
@@ -35,6 +36,8 @@ _KEYWORD_INDEXES = (
     "workspace_revision",
 )
 
+UPSERT_BATCH_SIZE = 256
+
 
 def point_id(workspace_id: str, kind: str, record_key: str, workspace_revision: str | None = None) -> str:
     # P3: schema/Evidence points are revision-scoped; memory/solved remain workspace-wide.
@@ -215,11 +218,14 @@ class QdrantVectorStore:
                     ),
                 }
             )
-        self._call(
-            "PUT",
-            f"/collections/{self._collection}/points?wait=true",
-            {"points": points},
-        )
+        # Qdrant rejects request bodies larger than its JSON limit (32 MiB by default).
+        # A large schema/Evidence corpus therefore must be upserted in bounded chunks.
+        for start in range(0, len(points), UPSERT_BATCH_SIZE):
+            self._call(
+                "PUT",
+                f"/collections/{self._collection}/points?wait=true",
+                {"points": points[start:start + UPSERT_BATCH_SIZE]},
+            )
         return len(records)
 
     def delete_kinds(self, collection: str, kinds: list[str]) -> int: