QTZPOC-20

QTZPOC-20: Implement .sientia model bundle import workflow

Introduces a new Temporal workflow and activities to import .sientia model bundles. This enables:
- Secure decryption and validation of bundles through a seven-gate process.
- Provisioning imported models in MLflow and updating the MongoDB model listing.
- Comprehensive status tracking in the public.experiment_run table with new IMPORT_SUCCESS/IMPORT_ERROR states.
- Dedicated metrics, robust error handling, and configurable settings.
- Includes updated e2e database fixtures and extensive unit/integration tests for bundle format, reader, and overall workflow.

laborious/metrics.py

@@ -183,6 +183,45 @@ MODEL_ANALYZE_ERROR_COUNT = Counter(
 )
 
 
+# ================== Model import metrics ==================
+#
+# The failing step and the error code travel in `operation_type`, the one label the shared
+# `CORE_LABELS` leaves free. Nothing else about a failure is a label: an error reason is a sentence
+# and an exception is a log line, neither of which belongs in a metric's cardinality.
+
+MODEL_IMPORT_STARTED_COUNT = Counter(
+    'laborious_model_import_started_count',
+    'Number of model imports that claimed their import log row',
+    CORE_LABELS,
+)
+
+MODEL_IMPORT_COMPLETED_COUNT = Counter(
+    'laborious_model_import_completed_count',
+    'Number of model imports that reached a registered version and a model document',
+    CORE_LABELS,
+)
+
+MODEL_IMPORT_ERROR_COUNT = Counter(
+    'laborious_model_import_error_count',
+    'Number of failed model imports, with the failing step or error code in operation_type',
+    CORE_LABELS,
+)
+
+MODEL_IMPORT_KDF_DURATION = Histogram(
+    'laborious_model_import_kdf_duration_seconds',
+    'Time spent deriving the bundle key with Argon2id',
+    CORE_LABELS,
+    buckets=[0.5, 1.0, 2.0, 3.0, 5.0, 8.0, 13.0, 21.0],
+)
+
+MODEL_IMPORT_STEP_DURATION = Histogram(
+    'laborious_model_import_step_duration_seconds',
+    'Duration of each model import step, named in operation_type',
+    CORE_LABELS,
+    buckets=[0.1, 0.5, 1.0, 5.0, 10.0, 30.0, 60.0, 300.0],
+)
+
+
 # ================== PI Web API metrics ==================
 
 PI_WEB_API_LABELS = [*CORE_LABELS, 'tag_name']