133 lines
5.9 KiB
Python
133 lines
5.9 KiB
Python
from temporalio import activity, workflow
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with workflow.unsafe.imports_passed_through():
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from typing import Any
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from bson import ObjectId
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from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
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from sientia_do.observability.logger import Logger
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from sientia_do.observability.metrics_controller import MetricsController
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from sientia_do.observability.sientia_monitoring import SientiaMonitoring
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from sientia_do.repository.mongodb_repository_sync import MongoDBRepository
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class PredictionMetadata(SientiaMonitoring):
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"""
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Centralized, best-effort writer for prediction gate metadata (triggered filter, policy,
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counter) to MongoDB.
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Gates only compute and return a metadata document; they never write it themselves. Keeping
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the write here, instead of in each gate, puts the one real write point in the subworkflow
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that's about to persist a `predictions` row — the only place that reliably knows whether one
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is being written at all — and lets other metadata sources reuse the same activity instead of
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duplicating write logic wherever they originate.
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"""
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mongo_repository: MongoDBRepository | None = None
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_PREDICTION_METADATA_COLLECTION = 'prediction_metadata'
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def __init__(
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self,
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mongo_repository: MongoDBRepository | None = None,
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logger: Logger | None = None,
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notification_handler: NotificationHandler | None = None,
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metrics_controller: MetricsController | None = None,
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):
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"""
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Initialize the prediction metadata writer.
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Args:
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mongo_repository: MongoDB, where gate metadata (triggered filter, policy, counter) is
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written best-effort. `None` is a valid state (see `write_prediction_metadata`).
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Raises:
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Exception: If BaseActivity initialization fails
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"""
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self.mongo_repository = mongo_repository
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SientiaMonitoring.__init__(
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self,
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logger=logger,
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notification_handler=notification_handler,
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metrics_controller=metrics_controller,
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)
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def close(self) -> None:
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"""
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Close the prediction metadata writer and clean up resources.
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"""
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if self.mongo_repository is not None:
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try:
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self.mongo_repository.close()
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finally:
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self.mongo_repository = None
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SientiaMonitoring.shutdown(self)
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def __del__(self):
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self.close()
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@activity.defn(name='write_prediction_metadata')
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def write_prediction_metadata(self, input_data: dict[str, Any]) -> str | None:
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"""
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Best-effort write of one or more gate metadata documents to Mongo, combined into a single
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`prediction_metadata` document.
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Implements both business rules for this feature in one place:
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- The prediction write must never be blocked or failed by a metadata write. A failed
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`insert(...)` is swallowed, never re-raised, logged here with call-site context (which
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`mongo_repository.insert(...)` itself can't know) — matching how `storage.py`'s
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`cleanup_minio_objects_expired` also logs again at the call site even though the
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repository already logs internally and emits its own Prometheus metric
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(`MONGODB_WRITE_ERROR_COUNT`); the generic library log is for the metric/trace, this
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one is for whoever is reading logs for this specific call.
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- Nothing is written when there's nothing relevant to report. An empty/missing
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`documents` list (or one full of falsy/empty entries) skips Mongo entirely, so callers
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can call this unconditionally and get `None` back for "no metadata this time" instead
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of deciding upfront whether there's something worth saving.
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Takes a *list* of documents, not a single one: each caller contributes its own document
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(shaped like `{'filters': [...]}`), and this flattens every `filters` entry across all of
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them into one `prediction_metadata` document before the single insert. Today only one
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gate ever contributes a document per prediction cycle (the gate chain short-circuits on
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the first non-`None` `path_flag`), but the list shape means this activity won't need to
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change again once more than one source contributes metadata for the same cycle.
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`insert(...)` doesn't hand back the inserted document's `_id`, so it is generated here,
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client-side, before the insert, as a plain string (not a BSON ObjectId) — matching this
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codebase's convention of string `_id`s (e.g. the `models` collection) and the 24-hex-char
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size of the `predictions.metadata_id` Postgres column that will reference it.
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Args:
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input_data: Dict with:
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- documents (list[dict[str, Any]]): Metadata payloads to combine, each shaped
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like `{'filters': [...]}`. Missing/empty, or entries with no `filters`, skip
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the write entirely.
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- metadata (dict): Workflow metadata for contextual logging.
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Return:
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The generated id, or `None` when there's nothing to write, Mongo isn't configured, or
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the insert failed.
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"""
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metadata = input_data['metadata']
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documents = input_data.get('documents') or []
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filters = [
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entry for document in documents if document for entry in document.get('filters', [])
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]
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if not filters or self.mongo_repository is None:
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return None
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generated_id = str(ObjectId())
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try:
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self.mongo_repository.insert(
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self._PREDICTION_METADATA_COLLECTION,
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{'filters': filters, '_id': generated_id},
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metadata,
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)
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except Exception as e:
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self.error(f'Failed to write prediction metadata: {e}', metadata)
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return None
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return generated_id
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