chore: bump version to 0.9.0-rc3

Rc/sientiapde 2165

laborious/activities/gates.py

@@ -78,6 +78,7 @@ input_path_confidence: Mapping[str, int] = {
     'REPEAT': -1,
 }
 
+
 # MLFlow response filter function mappings
 mlflow_response_filter_functions: dict[str, ResponseFilterFunc] = {
     'API_ERROR': api_error_filter,
@@ -320,7 +321,9 @@ class Gates(SientiaMonitoring):
         return None, 0, '', None
 
     @activity.defn(name='mlflow_response_gate')
-    def mlflow_response_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
+    def mlflow_response_gate(
+        self, input_data: dict[str, Any]
+    ) -> tuple[str | None, int, str, dict[str, Any] | None]:
         """
         Validate MLFlow API response quality and integrity.
 
@@ -334,6 +337,9 @@ class Gates(SientiaMonitoring):
         3. Determines path decisions based on response validation results
         4. Provides confidence scores and detailed validation comments
         5. Handles API errors and response validation failures
+        6. Builds a metadata document describing which filter(s) triggered, with their policy
+           and a counter, whenever one does — this activity only computes it, it never writes it
+           (see `PredictionMetadata.write_prediction_metadata` for the actual write)
 
         Args:
             input_data: Configuration and data for response validation
@@ -345,10 +351,14 @@ class Gates(SientiaMonitoring):
                     - path_priority (list[str]): Priority order for path decisions
 
         Returns:
-            tuple: (path_flag, confidence, comment)
+            tuple: (path_flag, confidence, comment, document)
                 - path_flag (str | None): Decision path (STOP, CONTINUE, REPEAT, or None)
                 - confidence (int): Confidence score for the decision
                 - comment (str): Detailed explanation of the decision
+                - document (dict | None): Metadata describing the filter(s) that produced
+                    `path_flag`, shaped `{'filters': [{'name', 'policy', 'counter'}, ...]}`;
+                    `None` when nothing was filtered. Not written anywhere by this activity —
+                    the caller decides whether and how to persist it.
 
         Raises:
             Exception: If response validation fails or configuration is invalid
@@ -369,10 +379,11 @@ class Gates(SientiaMonitoring):
         gate_type = input_data['type']
         path_priority = input_data['path_priority']
 
-        filter_output = []
-
         comments = []
 
+        # (filter_name, policy, filter_config) per filter that triggered, in configured order
+        triggered_filters: list[tuple[str, str, dict[str, Any]]] = []
+
         status = payload.status or {}
 
         for fil, config in filters.items():
@@ -381,15 +392,21 @@ class Gates(SientiaMonitoring):
             policy, filter_config = self._read_filter_entry(config)
             try:
                 if mlflow_response_filter_functions[fil](status, filter_config):
-                    filter_output.append(policy)
-                    comments.append(status.get('message', 'Unknown MLFlow API error'))
+                    triggered_filters.append((fil, policy, filter_config))
+                    # The real error lives one level in, under `content` — `request_transform`/
+                    # `request_predict` nest `message`/`traceback` there on failure (mlflow.py),
+                    # reusing the same `content` key the success path uses for the DataFrame
+                    # (review finding, 2026-10-08: reading `status['message']` directly always
+                    # fell back to the generic default, in every real production occurrence).
+                    error_content = status.get('content') or {}
+                    comments.append(error_content.get('message', 'Unknown MLFlow API error'))
                     self.send_notification(
                         metadata=metadata,
                         notification_id=f'{gate_type.upper()}_GATE_RESPONSE_FILTER__{fil}',
-                        message=status.get('message', 'Unknown MLFlow API error'),
+                        message=error_content.get('message', 'Unknown MLFlow API error'),
                         block='mlflow_gate',
                         level=NotificationLevel.ERROR,
-                        attachment_content=status.get('traceback'),
+                        attachment_content=error_content.get('traceback'),
                     )
             except Exception as e:
                 trace = traceback.format_exc()
@@ -402,16 +419,42 @@ class Gates(SientiaMonitoring):
                     attachment_content=trace,
                 )
 
+        triggered_policies = [policy for _, policy, _ in triggered_filters]
+
         for path_flag in path_priority:
-            if path_flag in filter_output:
+            if path_flag in triggered_policies:
                 self.info(f'Mlflow response gate result: {path_flag}', metadata)
-                return path_flag, mlflow_response_path_confidence[path_flag], ', '.join(comments)
+
+                matched = [fil for fil, policy, _ in triggered_filters if policy == path_flag]
+                document = {
+                    'filters': [
+                        {
+                            'name': fil,
+                            'policy': path_flag,
+                            # No per-row/per-item unit to count here — `api_error_filter`
+                            # evaluates a single API response as a whole, not tabular data —
+                            # so, unlike `input_gate`'s per-filter counters, this is a fixed
+                            # value: 1 (one API error event). Inlined rather than a counter
+                            # function/dict, since there's only one response filter today and
+                            # this isn't an override of anything (review finding).
+                            'counter': 1,
+                        }
+                        for fil in matched
+                    ],
+                }
+
+                return (
+                    path_flag,
+                    mlflow_response_path_confidence[path_flag],
+                    ', '.join(comments),
+                    document,
+                )
 
         self.info('Nothing was filtered by the mlflow response gate', metadata)
 
         del data
 
-        return None, 0, ''
+        return None, 0, '', None
 
     @activity.defn(name='mlflow_content_gate')
     def mlflow_content_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:

laborious/workflows/sub_workflows/prediction_process.py

@@ -156,7 +156,7 @@ class PredictionProcess:
         )
 
         # Validate MLFlow transform response
-        path_flag, confidence, comment = await workflow.execute_local_activity_method(
+        path_flag, confidence, comment, document = await workflow.execute_local_activity_method(
             Activities.mlflow_response_gate,
             {
                 **metadata,
@@ -171,7 +171,7 @@ class PredictionProcess:
 
         # Handle path decision based on transform validation
         if await self.path_flag_handler(
-            data, path_flag, input_data, confidence, last_timestamp, comment
+            data, path_flag, input_data, confidence, last_timestamp, comment, document
         ):
             return
 
@@ -206,7 +206,7 @@ class PredictionProcess:
         )
 
         # Validate MLFlow prediction response
-        path_flag, confidence, comment = await workflow.execute_local_activity_method(
+        path_flag, confidence, comment, document = await workflow.execute_local_activity_method(
             Activities.mlflow_response_gate,
             {
                 **metadata,
@@ -221,7 +221,7 @@ class PredictionProcess:
 
         # Handle path decision based on prediction validation
         if await self.path_flag_handler(
-            data, path_flag, input_data, confidence, last_timestamp, comment
+            data, path_flag, input_data, confidence, last_timestamp, comment, document
         ):
             return
 
@@ -271,9 +271,9 @@ class PredictionProcess:
         """
         if not document:
             # Skip the remote activity call entirely rather than dispatching it just to have
-            # `write_prediction_metadata` decide there's nothing to write — `mlflow_response_gate`
-            # and `mlflow_content_gate` don't build a `document` yet, so this is the common case
-            # today, not a rare edge case (review finding, 2026-10-01).
+            # `write_prediction_metadata` decide there's nothing to write — `mlflow_content_gate`
+            # doesn't build a `document` yet, so this is the common case today, not a rare edge
+            # case (review finding, 2026-10-01).
             return None
 
         try:

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "laborious"
-version = "0.9.0-rc2"
+version = "0.9.0-rc3"
 description = "Sientia DataOps Laborious - ML Model Orchestration System"
 readme = "README.md"
 requires-python = ">=3.11"

tests/laborious/activities/test_gates.py

@@ -314,7 +314,7 @@ def test_mlflow_response_gate_invalid_filter(gates_activity):
     result = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
-    assert result == (None, 0, '')
+    assert result == (None, 0, '', None)
 
 
 @patch('laborious.activities.gates.mlflow_response_filter_functions')
@@ -341,7 +341,7 @@ def test_mlflow_response_gate_filter_exception(
     result = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
-    assert result == (None, 0, '')
+    assert result == (None, 0, '', None)
     gates_activity.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
         notification_id='MLFLOW_GATE_RESPONSE_FILTER__INVALID_FILTER',
@@ -369,7 +369,7 @@ def test_mlflow_response_gate_no_filters(gates_activity):
     result = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
-    assert result == (None, 0, '')
+    assert result == (None, 0, '', None)
     gates_activity.debug.assert_called()
 
 
@@ -380,19 +380,30 @@ def test_mlflow_response_gate_with_filter(gates_activity):
         'filters': {'API_ERROR': {'POLICY': 'STOP'}},
         'data': _minio_payload(
             {'content': {'message': 'API error occurred', 'traceback': 'error trace'}},
-            status={'success': False, 'message': 'API error occurred'},
+            status={
+                'success': False,
+                'content': {'message': 'API error occurred', 'traceback': 'error trace'},
+            },
         ),
         'type': 'test',
         'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
     }
 
     # Act
-    result = gates_activity.mlflow_response_gate(input_data)
+    path_flag, confidence, comment, document = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
-    assert result == ('STOP', -1, 'API error occurred')
+    assert (path_flag, confidence, comment) == ('STOP', -1, 'API error occurred')
+    assert document == {'filters': [{'name': 'API_ERROR', 'policy': 'STOP', 'counter': 1}]}
     gates_activity.debug.assert_called()
-    gates_activity.send_notification.assert_called()
+    gates_activity.send_notification.assert_called_once_with(
+        metadata=metadata['metadata'],
+        notification_id='TEST_GATE_RESPONSE_FILTER__API_ERROR',
+        message='API error occurred',
+        block='mlflow_gate',
+        level=NotificationLevel.ERROR,
+        attachment_content='error trace',
+    )
 
 
 def test_mlflow_response_gate_with_filter_capitalized_keys(gates_activity):
@@ -402,7 +413,10 @@ def test_mlflow_response_gate_with_filter_capitalized_keys(gates_activity):
         'filters': {'API_ERROR': {'Policy': 'STOP'}},
         'data': _minio_payload(
             {'content': {'message': 'API error occurred', 'traceback': 'error trace'}},
-            status={'success': False, 'message': 'API error occurred'},
+            status={
+                'success': False,
+                'content': {'message': 'API error occurred', 'traceback': 'error trace'},
+            },
         ),
         'type': 'test',
         'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
@@ -412,7 +426,12 @@ def test_mlflow_response_gate_with_filter_capitalized_keys(gates_activity):
     result = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
-    assert result == ('STOP', -1, 'API error occurred')
+    assert result == (
+        'STOP',
+        -1,
+        'API error occurred',
+        {'filters': [{'name': 'API_ERROR', 'policy': 'STOP', 'counter': 1}]},
+    )
 
 
 def test_mlflow_response_gate_with_filter_not_caught(gates_activity):
@@ -432,7 +451,7 @@ def test_mlflow_response_gate_with_filter_not_caught(gates_activity):
     result = gates_activity.mlflow_response_gate(input_data)
 
     # Assert
-    assert result == (None, 0, '')
+    assert result == (None, 0, '', None)
     gates_activity.debug.assert_called()
 
 

tests/laborious/workflows/subworkflows/test_prediction_process.py

@@ -59,11 +59,11 @@ async def test_run(workflow_mock, prediction_process):
     workflow_mock.execute_local_activity_method.side_effect = [
         ('continue', 0.95, 'Input data with bad quality', None),  # input_gate
         # mlflow_response_gate (transform)
-        ('continue', 0.95, 'Error'),
+        ('continue', 0.95, 'Error', None),
         # mlflow_content_gate (transform)
         ('continue', 0.95, 'Transformed data not passed the content filter'),
         # mlflow_response_gate (predict)
-        ('continue', 0.95, 'Error'),
+        ('continue', 0.95, 'Error', None),
     ]
 
     # Act
@@ -276,7 +276,7 @@ async def test_run_stop_at_first_mlflow_response_gate(workflow_mock, prediction_
     ]
     workflow_mock.execute_local_activity_method.side_effect = [
         ('repeat', 0.95, 'Input data with bad quality', None),  # input_gate
-        ('continue', 0.95, 'Error'),  # mlflow_response_gate (transform)
+        ('continue', 0.95, 'Error', None),  # mlflow_response_gate (transform)
     ]
 
     # Act
@@ -367,7 +367,7 @@ async def test_run_stop_at_mlflow_content_gate(workflow_mock, prediction_process
     workflow_mock.execute_local_activity_method.side_effect = [
         ('continue', 0.95, 'Input data with bad quality', None),  # input_gate
         # mlflow_response_gate (transform)
-        ('continue', 0.95, 'Error'),
+        ('continue', 0.95, 'Error', None),
         # mlflow_content_gate (transform)
         ('continue', 0.95, 'Transformed data not passed the content filter'),
     ]
@@ -478,10 +478,10 @@ async def test_run_stop_at_mlflow_last_response_gate(workflow_mock, prediction_p
     workflow_mock.execute_local_activity_method.side_effect = [
         ('continue', 0.95, 'Input data with bad quality', None),  # input_gate
         # mlflow_response_gate (transform)
-        ('continue', 0.95, 'Error'),
+        ('continue', 0.95, 'Error', None),
         # mlflow_content_gate (transform)
         ('continue', 0.95, 'Transformed data not passed the content filter'),
-        ('continue', 0.95, 'Error'),  # mlflow_response_gate (predict)
+        ('continue', 0.95, 'Error', None),  # mlflow_response_gate (predict)
     ]
 
     # Act
@@ -696,9 +696,9 @@ async def test_path_flag_handler_repeat_no_document_skips_write_activity_call(
     workflow_mock, prediction_process
 ):
     # Arrange: no document (e.g. REPEAT triggered by a gate that doesn't build one yet — today,
-    # `mlflow_response_gate`/`mlflow_content_gate`) must not even dispatch the remote
-    # write_prediction_metadata activity — that would be a wasted Temporal round-trip on every
-    # REPEAT caused by those two gates, not a rare edge case (review finding, 2026-10-01).
+    # `mlflow_content_gate`) must not even dispatch the remote write_prediction_metadata
+    # activity — that would be a wasted Temporal round-trip on every REPEAT caused by that
+    # gate, not a rare edge case (review finding, 2026-10-01).
     data = {'test': 'data'}
 
     # Act
@@ -1075,9 +1075,9 @@ async def test_run_with_cleanup_prefixes(workflow_mock, prediction_process):
     ]
     workflow_mock.execute_local_activity_method.side_effect = [
         ('continue', 0.95, 'ok', None),  # input_gate
-        ('continue', 0.95, ''),
-        ('continue', 0.95, ''),
-        ('continue', 0.95, ''),
+        ('continue', 0.95, '', None),  # mlflow_response_gate (transform)
+        ('continue', 0.95, ''),  # mlflow_content_gate (transform)
+        ('continue', 0.95, '', None),  # mlflow_response_gate (predict)
     ]
 
     await prediction_process.run(input_data)