diff --git a/src/datachain/lib/dc.py b/src/datachain/lib/dc.py
index a3437b18..f3e18a3b 100644
--- a/src/datachain/lib/dc.py
+++ b/src/datachain/lib/dc.py
@@ -895,7 +895,7 @@ def agg(
         2. Group-based UDF function input: Instead of individual rows, the function
            receives a list all rows within each group defined by `partition_by`.
 
-        Example:
+        Examples:
             ```py
             chain = chain.agg(
                 total=lambda category, amount: [sum(amount)],
@@ -904,6 +904,26 @@ def agg(
             )
             chain.save("new_dataset")
             ```
+
+            An alternative syntax, when you need to specify a more complex function:
+
+            ```py
+            # It automatically resolves which columns to pass to the function
+            # by looking at the function signature.
+            def agg_sum(
+                file: list[File], amount: list[float]
+            ) -> Iterator[tuple[File, float]]:
+                yield file[0], sum(amount)
+
+            chain = chain.agg(
+                agg_sum,
+                output={"file": File, "total": float},
+                # Alternative syntax is to use `C` (short for Column) to specify
+                # a column name or a nested column, e.g. C("file.path").
+                partition_by=C("category"),
+            )
+            chain.save("new_dataset")
+            ```
         """
         udf_obj = self._udf_to_obj(Aggregator, func, params, output, signal_map)
         return self._evolve(