re-order track_metrics_of params to be more intuitive

Author: jsonbailey

ldai/chat/__init__.py

@@ -62,8 +62,8 @@ async def invoke(self, prompt: str) -> ChatResponse:
 
         # Delegate to provider-specific implementation with tracking
         response = await self._tracker.track_metrics_of(
-            lambda result: result.metrics,
             lambda: self._provider.invoke_model(all_messages),
+            lambda result: result.metrics,
         )
 
         # Start judge evaluations as async tasks (don't await them)

ldai/judge/__init__.py

@@ -80,8 +80,8 @@ async def evaluate(
 
             # Track metrics of the structured model invocation
             response = await self._ai_config_tracker.track_metrics_of(
+                lambda: self._ai_provider.invoke_structured_model(messages, self._evaluation_response_structure),
                 lambda result: result.metrics,
-                lambda: self._ai_provider.invoke_structured_model(messages, self._evaluation_response_structure)
             )
 
             success = response.metrics.success

ldai/tracker.py

@@ -157,7 +157,7 @@ def track_duration_of(self, func):
 
         return result
 
-    async def track_metrics_of(self, metrics_extractor, func):
+    async def track_metrics_of(self, func, metrics_extractor):
         """
         Track metrics for a generic AI operation.
 
@@ -168,8 +168,8 @@ async def track_metrics_of(self, metrics_extractor, func):
         In the case the provided function throws, this function will record the duration and an error.
         A failed operation will not have any token usage data.
 
-        :param metrics_extractor: Function that extracts LDAIMetrics from the operation result
         :param func: Async function which executes the operation
+        :param metrics_extractor: Function that extracts LDAIMetrics from the operation result
         :return: The result of the operation
         """
         start_time = time.time()