Make _get_template_context an RPC call

airflow/api_internal/endpoints/rpc_api_endpoint.py

@@ -25,6 +25,7 @@
 from flask import Response
 
 from airflow.jobs.job import Job, most_recent_job
+from airflow.models.taskinstance import _get_template_context
 from airflow.serialization.serialized_objects import BaseSerialization
 from airflow.utils.session import create_session
 
@@ -47,6 +48,7 @@ def _initialize_map() -> dict[str, Callable]:
     from airflow.secrets.metastore import MetastoreBackend
 
     functions: list[Callable] = [
+        _get_template_context,
         DagFileProcessor.update_import_errors,
         DagFileProcessor.manage_slas,
         DagFileProcessorManager.deactivate_stale_dags,

airflow/models/taskinstance.py

@@ -596,6 +596,7 @@ def _clear_next_method_args(*, task_instance: TaskInstance | TaskInstancePydanti
     task_instance.next_kwargs = None
 
 
+@internal_api_call
 def _get_template_context(
     *,
     task_instance: TaskInstance | TaskInstancePydantic,
@@ -622,10 +623,30 @@ def _get_template_context(
 
     task = task_instance.task
     if TYPE_CHECKING:
+        assert task_instance.task
         assert task
         assert task.dag
-    dag: DAG = task.dag
+    try:
+        dag: DAG = task.dag
+    except AirflowException:
+        from airflow.serialization.pydantic.taskinstance import TaskInstancePydantic
 
+        if isinstance(task_instance, TaskInstancePydantic):
+            ti = session.scalar(
+                select(TaskInstance).where(
+                    TaskInstance.task_id == task_instance.task_id,
+                    TaskInstance.dag_id == task_instance.dag_id,
+                    TaskInstance.run_id == task_instance.run_id,
+                    TaskInstance.map_index == task_instance.map_index,
+                )
+            )
+            dag = ti.dag_model.serialized_dag.dag
+            if hasattr(task_instance.task, "_dag"):  # BaseOperator
+                task_instance.task._dag = dag
+            else:  # MappedOperator
+                task_instance.task.dag = dag
+        else:
+            raise
     dag_run = task_instance.get_dagrun(session)
     data_interval = dag.get_run_data_interval(dag_run)
 

airflow/serialization/enums.py

@@ -61,4 +61,5 @@ class DagAttributeTypes(str, Enum):
     DATA_SET = "data_set"
     LOG_TEMPLATE = "log_template"
     CONNECTION = "connection"
+    TASK_CONTEXT = "task_context"
     ARG_NOT_SET = "arg_not_set"

airflow/serialization/serialized_objects.py

@@ -67,6 +67,7 @@
     airflow_priority_weight_strategies_classes,
 )
 from airflow.utils.code_utils import get_python_source
+from airflow.utils.context import Context
 from airflow.utils.docs import get_docs_url
 from airflow.utils.module_loading import import_string, qualname
 from airflow.utils.operator_resources import Resources
@@ -602,6 +603,12 @@ def serialize(
             )
         elif isinstance(var, Connection):
             return cls._encode(var.to_dict(validate=True), type_=DAT.CONNECTION)
+        elif var.__class__ == Context:
+            d = {}
+            for k, v in var._context.items():
+                obj = cls.serialize(v, strict=strict, use_pydantic_models=use_pydantic_models)
+                d[str(k)] = obj
+            return cls._encode(d, type_=DAT.TASK_CONTEXT)
         elif use_pydantic_models and _ENABLE_AIP_44:
 
             def _pydantic_model_dump(model_cls: type[BaseModel], var: Any) -> dict[str, Any]:
@@ -648,7 +655,14 @@ def deserialize(cls, encoded_var: Any, use_pydantic_models=False) -> Any:
             raise ValueError(f"The encoded_var should be dict and is {type(encoded_var)}")
         var = encoded_var[Encoding.VAR]
         type_ = encoded_var[Encoding.TYPE]
-
+        if type_ == DAT.TASK_CONTEXT:
+            d = {}
+            for k, v in var.items():
+                if k == "task":  # todo: add TaskPydantic so we don't need this?
+                    continue
+                d[k] = cls.deserialize(v, use_pydantic_models=True)
+            d["task"] = d["task_instance"].task  # todo: add TaskPydantic so we don't need this?
+            return Context(**d)
         if type_ == DAT.DICT:
             return {k: cls.deserialize(v, use_pydantic_models) for k, v in var.items()}
         elif type_ == DAT.DAG: