fix: resolve all ruff lint errors

mlflow_utils/client.py

@@ -5,10 +5,10 @@ Provides a configured MLflow client for all integrations in the LLM workflows.
 Supports both in-cluster and external access patterns.
 """
 
-import os
 import logging
+import os
 from dataclasses import dataclass, field
-from typing import Optional, Dict, Any
+from typing import Any, Dict, Optional
 
 import mlflow
 from mlflow.tracking import MlflowClient
@@ -19,7 +19,7 @@ logger = logging.getLogger(__name__)
 @dataclass
 class MLflowConfig:
     """Configuration for MLflow integration."""
-    
+
     # Tracking server URIs
     tracking_uri: str = field(
         default_factory=lambda: os.environ.get(
@@ -33,7 +33,7 @@ class MLflowConfig:
             "https://mlflow.lab.daviestechlabs.io"
         )
     )
-    
+
     # Artifact storage (NFS PVC mount)
     artifact_location: str = field(
         default_factory=lambda: os.environ.get(
@@ -41,7 +41,7 @@ class MLflowConfig:
             "/mlflow/artifacts"
         )
     )
-    
+
     # Default experiment settings
     default_experiment: str = field(
         default_factory=lambda: os.environ.get(
@@ -49,7 +49,7 @@ class MLflowConfig:
             "llm-workflows"
         )
     )
-    
+
     # Service identification
     service_name: str = field(
         default_factory=lambda: os.environ.get(
@@ -57,10 +57,10 @@ class MLflowConfig:
             "unknown-service"
         )
     )
-    
+
     # Additional tags to add to all runs
     default_tags: Dict[str, str] = field(default_factory=dict)
-    
+
     def __post_init__(self):
         """Add default tags based on environment."""
         env_tags = {
@@ -74,10 +74,10 @@ class MLflowConfig:
 def get_tracking_uri(external: bool = False) -> str:
     """
     Get the appropriate MLflow tracking URI.
-    
+
     Args:
         external: If True, return the external URI for outside-cluster access
-        
+
     Returns:
         The MLflow tracking URI string
     """
@@ -91,20 +91,20 @@ def get_mlflow_client(
 ) -> MlflowClient:
     """
     Get a configured MLflow client.
-    
+
     Args:
         tracking_uri: Override the default tracking URI
         configure_global: If True, also set mlflow.set_tracking_uri()
-        
+
     Returns:
         Configured MlflowClient instance
     """
     uri = tracking_uri or get_tracking_uri()
-    
+
     if configure_global:
         mlflow.set_tracking_uri(uri)
         logger.info(f"MLflow tracking URI set to: {uri}")
-    
+
     client = MlflowClient(tracking_uri=uri)
     return client
 
@@ -116,21 +116,21 @@ def ensure_experiment(
 ) -> str:
     """
     Ensure an experiment exists, creating it if necessary.
-    
+
     Args:
         experiment_name: Name of the experiment
         artifact_location: Override default artifact location
         tags: Additional tags for the experiment
-        
+
     Returns:
         The experiment ID
     """
     config = MLflowConfig()
     client = get_mlflow_client()
-    
+
     # Check if experiment exists
     experiment = client.get_experiment_by_name(experiment_name)
-    
+
     if experiment is None:
         # Create the experiment
         artifact_loc = artifact_location or f"{config.artifact_location}/{experiment_name}"
@@ -143,7 +143,7 @@ def ensure_experiment(
     else:
         experiment_id = experiment.experiment_id
         logger.debug(f"Using existing experiment '{experiment_name}' with ID: {experiment_id}")
-    
+
     return experiment_id
 
 
@@ -154,17 +154,17 @@ def get_or_create_registered_model(
 ) -> str:
     """
     Get or create a registered model in the Model Registry.
-    
+
     Args:
         model_name: Name of the model to register
         description: Model description
         tags: Tags for the model
-        
+
     Returns:
         The registered model name
     """
     client = get_mlflow_client()
-    
+
     try:
         # Check if model exists
         client.get_registered_model(model_name)
@@ -177,14 +177,14 @@ def get_or_create_registered_model(
             tags=tags or {}
         )
         logger.info(f"Created registered model: {model_name}")
-    
+
     return model_name
 
 
 def health_check() -> Dict[str, Any]:
     """
     Check MLflow server connectivity and return status.
-    
+
     Returns:
         Dictionary with health status information
     """
@@ -195,7 +195,7 @@ def health_check() -> Dict[str, Any]:
         "connected": False,
         "error": None,
     }
-    
+
     try:
         client = get_mlflow_client(configure_global=False)
         # Try to list experiments as a health check
@@ -205,5 +205,5 @@ def health_check() -> Dict[str, Any]:
     except Exception as e:
         result["error"] = str(e)
         logger.error(f"MLflow health check failed: {e}")
-    
+
     return result