SIENTIAPDE-1646

Refactor worker task queue management and update README

- Introduced runtime-scoped task queues for workflows, replacing legacy queue names.
- Updated worker implementation to utilize `sientia_do.temporal.worker.prepare_worker`.
- Added `RUNTIME` environment variable to configure task queue suffixes.
- Enhanced README documentation to reflect changes in task queue structure and worker setup.

README.md

@@ -126,10 +126,15 @@ Laborious uses a Temporal-based architecture with strong separation of concerns
 ### Key Components
 
 #### **Worker (`laborious/worker/worker.py`)**
-- Temporal client setup, worker lifecycle, task queues
+- Temporal client setup, four workers via `sientia_do.temporal.worker.prepare_worker`
+- Runtime-scoped task queues: `{workflow}-{RUNTIME}-queue` for all workflows
 - Metrics server initialization, notification handler setup
 - Graceful shutdown and autoscaling-friendly behavior
 
+**Breaking (schedulers):** drift and simple_metrics queues are no longer `drift-queue` /
+`simple_metrics-queue`. Use `drift-{RUNTIME}-queue` and `simple_metrics-{RUNTIME}-queue`
+matching the worker pod `RUNTIME` env (same as `predictions_batch` / `minimal_retrain`).
+
 #### **Workflows (`laborious/workflows/`)**
 - `predictions_batch.py`: Batch prediction entry point
 - `sub_workflows/prediction_process.py`: Core prediction pipeline
@@ -802,6 +807,7 @@ See [OPC UA Communication](#opc-ua-communication) for semantics, concurrency, an
 |----------|-------------|---------|----------|
 | `TEMPORAL_HOST` | Temporal server address | `localhost:7233` | Yes |
 | `TEMPORAL_NAMESPACE` | Temporal namespace | `laborious` | No |
+| `RUNTIME` | Task queue suffix for all workflows (`{workflow}-{RUNTIME}-queue`) | _(none)_ | Yes |
 | `POSTGRES_HOST` | PostgreSQL hostname | `localhost` | Yes |
 | `POSTGRES_PORT` | PostgreSQL port | `5432` | Yes |
 | `POSTGRES_USER` | PostgreSQL username | `sientia` | Yes |
@@ -1097,8 +1103,7 @@ laborious/
 │       ├── prediction_process.py # Core prediction workflow
 │       └── format_and_export_prediction.py # Export workflow
 ├── worker/                  # Worker implementation
-│   ├── worker.py           # Main worker orchestrator
-│   └── prepare_worker.py   # Worker factory with autoscaling config
+│   └── worker.py           # Main worker orchestrator (uses sientia_do prepare_worker)
 ├── utils/                   # Utility functions
 │   ├── connectors_config.py # Environment-driven config builders
 │   ├── models/             # Data models
@@ -1179,7 +1184,9 @@ export LOG_LEVEL=DEBUG
 ### Scaling Considerations
 
 - **Horizontal Scaling**: Deploy multiple worker instances
-- **Task Queue Distribution**: Use multiple task queues for different workflow types
+- **Task Queue Distribution**: One worker pod per `RUNTIME`; queues are
+  `predictions_batch-{RUNTIME}-queue`, `minimal_retrain-{RUNTIME}-queue`,
+  `drift-{RUNTIME}-queue`, `simple_metrics-{RUNTIME}-queue`
 - **Database Performance**: Optimize indexes and connection pooling
 - **MLFlow Performance**: Configure appropriate model serving resources