Merge branch 'main' into migrate-unsafe-pow

Author: jialuoo

.github/.OwlBot.lock.yaml

@@ -0,0 +1,10 @@
+image: us-central1-docker.pkg.dev/cloud-sdk-librarian-prod/images-prod/python-librarian-generator@sha256:c8612d3fffb3f6a32353b2d1abd16b61e87811866f7ec9d65b59b02eb452a620
+libraries:
+  - id: bigframes
+    version: 2.28.0
+    apis: []
+    source_roots:
+      - .
+    preserve_regex: []
+    remove_regex: []
+    tag_format: v{version}

.github/.OwlBot.yaml

@@ -88,7 +88,7 @@ def generate(
             or pandas Series.
         connection_id (str, optional):
             Specifies the connection to use to communicate with the model. For example, `myproject.us.myconnection`.
-            If not provided, the connection from the current session will be used.
+            If not provided, the query uses your end-user credential.
         endpoint (str, optional):
             Specifies the Vertex AI endpoint to use for the model. For example `"gemini-2.5-flash"`. You can specify any
             generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and
@@ -131,7 +131,7 @@ def generate(
 
     operator = ai_ops.AIGenerate(
         prompt_context=tuple(prompt_context),
-        connection_id=_resolve_connection_id(series_list[0], connection_id),
+        connection_id=connection_id,
         endpoint=endpoint,
         request_type=request_type,
         model_params=json.dumps(model_params) if model_params else None,
@@ -186,7 +186,7 @@ def generate_bool(
             or pandas Series.
         connection_id (str, optional):
             Specifies the connection to use to communicate with the model. For example, `myproject.us.myconnection`.
-            If not provided, the connection from the current session will be used.
+            If not provided, the query uses your end-user credential.
         endpoint (str, optional):
             Specifies the Vertex AI endpoint to use for the model. For example `"gemini-2.5-flash"`. You can specify any
             generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and
@@ -216,7 +216,7 @@ def generate_bool(
 
     operator = ai_ops.AIGenerateBool(
         prompt_context=tuple(prompt_context),
-        connection_id=_resolve_connection_id(series_list[0], connection_id),
+        connection_id=connection_id,
         endpoint=endpoint,
         request_type=request_type,
         model_params=json.dumps(model_params) if model_params else None,
@@ -267,7 +267,7 @@ def generate_int(
             or pandas Series.
         connection_id (str, optional):
             Specifies the connection to use to communicate with the model. For example, `myproject.us.myconnection`.
-            If not provided, the connection from the current session will be used.
+            If not provided, the query uses your end-user credential.
         endpoint (str, optional):
             Specifies the Vertex AI endpoint to use for the model. For example `"gemini-2.5-flash"`. You can specify any
             generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and
@@ -297,7 +297,7 @@ def generate_int(
 
     operator = ai_ops.AIGenerateInt(
         prompt_context=tuple(prompt_context),
-        connection_id=_resolve_connection_id(series_list[0], connection_id),
+        connection_id=connection_id,
         endpoint=endpoint,
         request_type=request_type,
         model_params=json.dumps(model_params) if model_params else None,
@@ -348,7 +348,7 @@ def generate_double(
             or pandas Series.
         connection_id (str, optional):
             Specifies the connection to use to communicate with the model. For example, `myproject.us.myconnection`.
-            If not provided, the connection from the current session will be used.
+            If not provided, the query uses your end-user credential.
         endpoint (str, optional):
             Specifies the Vertex AI endpoint to use for the model. For example `"gemini-2.5-flash"`. You can specify any
             generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and
@@ -378,7 +378,7 @@ def generate_double(
 
     operator = ai_ops.AIGenerateDouble(
         prompt_context=tuple(prompt_context),
-        connection_id=_resolve_connection_id(series_list[0], connection_id),
+        connection_id=connection_id,
         endpoint=endpoint,
         request_type=request_type,
         model_params=json.dumps(model_params) if model_params else None,

.github/auto-approve.yml

@@ -450,13 +450,15 @@ def project_window_expr(
         )
 
     def isin(
-        self, other: ArrayValue, lcol: str, rcol: str
+        self,
+        other: ArrayValue,
+        lcol: str,
     ) -> typing.Tuple[ArrayValue, str]:
+        assert len(other.column_ids) == 1
         node = nodes.InNode(
             self.node,
             other.node,
             ex.deref(lcol),
-            ex.deref(rcol),
             indicator_col=ids.ColumnId.unique(),
         )
         return ArrayValue(node), node.indicator_col.name

.github/release-please.yml

@@ -2326,7 +2326,7 @@ def isin(self, other: Block):
         return block
 
     def _isin_inner(self: Block, col: str, unique_values: core.ArrayValue) -> Block:
-        expr, matches = self._expr.isin(unique_values, col, unique_values.column_ids[0])
+        expr, matches = self._expr.isin(unique_values, col)
 
         new_value_cols = tuple(
             val_col if val_col != col else matches for val_col in self.value_columns