diff --git a/docsgpt/graphrag/store.py b/docsgpt/graphrag/store.py
index ceb0fc48..116d3611 100644
--- a/docsgpt/graphrag/store.py
+++ b/docsgpt/graphrag/store.py
@@ -175,19 +175,24 @@ class GraphStore:
 
         Returns:
             Whatever ``operation`` returns.
+
+        Raises:
+            Exception: Anything ``operation`` raises that is not connection
+                loss, and anything the single retry raises.
         """
-        for attempt in (1, 2):
-            conn = self._get_connection()
-            try:
-                return operation(conn)
-            except Exception as exc:
-                if attempt == 2 or not _is_connection_lost(exc):
-                    raise
-                logging.warning(
-                    "Graph write lost its connection (%s); reconnecting and retrying once.",
-                    exc,
-                )
-                self.close()
+        try:
+            return operation(self._get_connection())
+        except Exception as exc:
+            if not _is_connection_lost(exc):
+                raise
+            logging.warning(
+                "Graph write lost its connection (%s); reconnecting and retrying once.",
+                exc,
+            )
+            self.close()
+        # Second and final attempt, on a connection freshly checked out by
+        # ``_get_connection``. A failure here belongs to the caller.
+        return operation(self._get_connection())
 
     def _register_pgvector_types(self, conn) -> None:
         """Register pgvector's adapters, tolerating a not-yet-created extension.
diff --git a/docsgpt/retriever/graph_rag.py b/docsgpt/retriever/graph_rag.py
index 5593a9b2..ef4b0ea5 100644
--- a/docsgpt/retriever/graph_rag.py
+++ b/docsgpt/retriever/graph_rag.py
@@ -103,7 +103,7 @@ def _personalized_pagerank(
 
     # Row-normalized transitions. An undirected edge is traversable from both
     # endpoints, so each node normalizes over its own incident weights.
-    transitions: Dict[Any, List[Any]] = {}
+    transitions: Dict[Any, List[tuple[Any, float]]] = {}
     for node in nodes:
         neighbors = []
         total = 0.0
@@ -134,6 +134,13 @@ def _personalized_pagerank(
             ranks[node] += (leaked + 1.0 - alpha) * restart[node]
         if sum(abs(ranks[node] - previous[node]) for node in nodes) < node_count * tol:
             break
+    else:
+        logging.debug(
+            "Personalized PageRank hit its %s-iteration cap on a %s-node "
+            "subgraph; ranking with the last iterate.",
+            max_iter,
+            node_count,
+        )
     return ranks