diff --git a/src/datachain/lib/arrow.py b/src/datachain/lib/arrow.py
index f9d77c23a..abbe30c56 100644
--- a/src/datachain/lib/arrow.py
+++ b/src/datachain/lib/arrow.py
@@ -126,7 +126,16 @@ def _process_record(
             if isinstance(kwargs.get("format"), CsvFileFormat):
                 kwargs["format"] = "csv"
             arrow_file = ArrowRow(file=file, index=index, kwargs=kwargs)
+
+            if self.output_schema and hasattr(vals[0], "source"):
+                # if we are reading parquet file written by datachain it might have
+                # source inside of it already, so we should not duplicate it, instead
+                # we are re-creating it of the self.source flag
+                vals[0].source = arrow_file  # type: ignore[attr-defined]
+
+                return vals
             return [arrow_file, *vals]
+
         return vals
 
     def _process_non_datachain_record(
diff --git a/tests/unit/lib/test_datachain.py b/tests/unit/lib/test_datachain.py
index 954c94d28..dcf817f95 100644
--- a/tests/unit/lib/test_datachain.py
+++ b/tests/unit/lib/test_datachain.py
@@ -1760,6 +1760,22 @@ def test_read_parquet(tmp_dir, test_session):
     assert df_equal(df1, df)
 
 
+def test_read_parquet_exported_with_source(test_session, tmp_dir):
+    path = tmp_dir / "df.parquet"
+    path2 = tmp_dir / "df2.parquet"
+    df = pd.DataFrame(DF_DATA)
+
+    df.to_parquet(path)
+    dc.read_parquet(path, source=True).to_parquet(path2)
+    df1 = (
+        dc.read_parquet(path2, source=True)
+        .select("first_name", "age", "city")
+        .to_pandas()
+    )
+
+    assert df_equal(df1, df)
+
+
 @skip_if_not_sqlite
 def test_read_parquet_in_memory(tmp_dir):
     df = pd.DataFrame(DF_DATA)