chore: update configuration and imports for improved functionality

- Updated image tag in values.yaml from "1.0.0" to "1.0.2" for the latest version.
- Modified STORE_BASE_URL to include port 3000 for proper service access.
- Changed import from MinioRepository to MinioRepositorySync for synchronization support.
- Updated import from Postgres to PostgresSync to enhance experiment tracking capabilities.
- Renamed TRAIN_TASK_QUEUE from "train_model-single-queue" to "train_model-basic-queue" for clarity.

model_manager/activities/activities.py

@@ -7,7 +7,7 @@ with workflow.unsafe.imports_passed_through():
     from sientia_do.observability.logger import Logger
     from sientia_do.observability.metrics_controller import MetricsController
     from sientia_do.observability.sientia_monitoring import SientiaMonitoring
-    from sientia_do.repository.minio_repository import MinioRepository
+    from sientia_do.repository.minio_repository_sync import MinioRepository
     from sientia_model.model_repository.mlflow_repository import SientiaMLflowRepository
     from sientia_model.model_repository.plugin_store import PluginStore
 

model_manager/activities/experiment_tracking.py

@@ -2,7 +2,7 @@
 Experiment tracking activities for managing ML experiment lifecycle.
 
 This module provides activities for tracking and updating experiment run status
-in the PostgreSQL database, extending the base Postgres activity with specialized
+in the PostgreSQL database, extending the synchronous Postgres client with specialized
 methods for experiment management.
 """
 
@@ -20,7 +20,7 @@ with workflow.unsafe.imports_passed_through():
     from sientia_do.notifications.models import NotificationLevel
     from sientia_do.observability.logger import Logger
     from sientia_do.observability.metrics_controller import MetricsController
-    from sientia_do.temporal.activities.postgres import Postgres
+    from sientia_do.temporal.activities.postgres_sync import Postgres
     from sqlalchemy import text
 
 
@@ -41,7 +41,7 @@ class ExperimentTracking(Postgres):
     model registration. It maintains the experiment lifecycle from initialization
     through training, model saving, and cleanup.
 
-    The activity uses the existing Postgres connection pool and adds experiment-specific
+    The activity uses a SQLAlchemy engine / connection pool and adds experiment-specific
     operations with proper error handling and notifications.
     """