Trigger Airflow DAG from Python source code, cannot find Sqlite table "dag"

I want to invoke an Airflow DAG definition from within a Python script (a Jupyter notebook actually).

I made sure the environment variables that I use for the airflow command are set (e.g. AIRFLOW_HOME) with python-dotenv to load a .env file inside the Jupyter notebook. This .env file contains various environment variables included AIRFLOW_HOME:

%load_ext dotenv
# the AIRFLOW_HOME env var must be an absolute file system path 
# if the Notebook is relative to where the `airflow.db` Sqlite DB is.
%dotenv ../path-to-env-file/.env

I do see the expected file system path, so AIRFLOW_HOME points to the place where the Sqlite database airflow.db is - the Jupyter environment prints the correct path:

print(os.getenv("AIRFLOW_HOME"))

The Sqlite DB was previously created with commands like airflow db init (same old AIRFLOW_HOME), also I added some connections I use in the DAG with airflow connections add ....

All this stuff works either via the Airflow Webserver + scheduler or via the command:

airflow tasks test \
    --subdir ../dags \
    'my_dag_id' 'my_task_id' "${TODAY}"

• All these configuration details did go from the airflow command invocations into the Sqlite DB.
• I do see all the Airflow DB tables in that Sqlite DB.
• I do see the rows in those tables.
• All the commands work as expected.

Now I want to do the same as above, but with a DAG defined in a Jupyter notebook, so going back to that, when I do this in Python:

from airflow.api.client.local_client import Client

c = Client(None, None)
c.trigger_dag(dag_id='my_dag_id', run_id='test_run_id', conf={})

Then I see a long stacktrace with at the beginning this message:

OperationalError                          Traceback (most recent call last)

and at the bottom this message:

OperationalError: (sqlite3.OperationalError) no such table: dag
[SQL: SELECT dag.dag_id AS dag_dag_id, dag.root_dag_id AS dag_root_dag_id, dag.is_paused AS dag_is_paused, dag.is_subdag AS dag_is_subdag, dag.is_active AS dag_is_active, dag.last_parsed_time AS dag_last_parsed_time, dag.last_pickled AS dag_last_pickled, dag.last_expired AS dag_last_expired, dag.scheduler_lock AS dag_scheduler_lock, dag.pickle_id AS dag_pickle_id, dag.fileloc AS dag_fileloc, dag.owners AS dag_owners, dag.description AS dag_description, dag.default_view AS dag_default_view, dag.schedule_interval AS dag_schedule_interval, dag.max_active_tasks AS dag_max_active_tasks, dag.max_active_runs AS dag_max_active_runs, dag.has_task_concurrency_limits AS dag_has_task_concurrency_limits, dag.has_import_errors AS dag_has_import_errors, dag.next_dagrun AS dag_next_dagrun, dag.next_dagrun_data_interval_start AS dag_next_dagrun_data_interval_start, dag.next_dagrun_data_interval_end AS dag_next_dagrun_data_interval_end, dag.next_dagrun_create_after AS dag_next_dagrun_create_after 
FROM dag 
WHERE dag.dag_id = ?
 LIMIT ? OFFSET ?]
[parameters: ('my_dag_id', 1, 0)]

But the dag table is there, AIRFLOW_HOME is pointing to that Sqlite DB and the table contains the expected row:

-- this shows the expected row
SELECT * FROM dag WHERE dag_id = 'my_dag_id'

How do I run the DAG programmatically from Python or Jupyter?