fix: preserve nodes with unassigned community level in filtering

packages/graphrag/graphrag/query/indexer_adapters.py

@@ -221,5 +221,5 @@ def _filter_under_community_level(
 ) -> pd.DataFrame:
     return cast(
         "pd.DataFrame",
-        df[df.level <= community_level],
+        df[(df.level <= community_level) | df.level.isna()],
     )

tests/unit/query/test_filter_community_level.py

@@ -0,0 +1,42 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Tests for _filter_under_community_level preserving NaN-level nodes."""
+
+import numpy as np
+import pandas as pd
+
+from graphrag.query.indexer_adapters import _filter_under_community_level
+
+
+def test_filter_preserves_nan_level_nodes():
+    """Nodes with level=NaN should not be discarded by the filter.
+
+    Regression test for issue #1808 where isolated nodes without a community
+    assignment (level=None) were incorrectly dropped.
+    """
+    df = pd.DataFrame({
+        "id": ["a", "b", "c", "d"],
+        "level": [0, 1, 2, np.nan],
+        "community": [1, 2, 3, np.nan],
+    })
+
+    result = _filter_under_community_level(df, community_level=1)
+
+    # Should keep level 0, 1 (<=1) and NaN (unassigned)
+    assert len(result) == 3
+    assert set(result["id"].tolist()) == {"a", "b", "d"}
+
+
+def test_filter_excludes_higher_level_nodes():
+    """Nodes with level > community_level should be excluded."""
+    df = pd.DataFrame({
+        "id": ["a", "b", "c"],
+        "level": [0, 2, 3],
+        "community": [1, 2, 3],
+    })
+
+    result = _filter_under_community_level(df, community_level=1)
+
+    assert len(result) == 1
+    assert result["id"].tolist() == ["a"]