Keep assistant prompts focused on semantic metrics

backend/app/services/geo_assistant_service.py

@@ -171,6 +171,15 @@ class GeoAssistantService:
             return round(value, 4)
         return round(value, 2)
 
+    @staticmethod
+    def model_context_metrics(metrics: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        meaningful_metrics = [
+            metric
+            for metric in metrics
+            if str(metric.get("metric_unit") or "").casefold().strip() not in {"objecten", "features"}
+        ]
+        return meaningful_metrics or metrics
+
     @staticmethod
     def normalize_plain_text(answer: str) -> str:
         lines: list[str] = []
@@ -418,6 +427,7 @@ class GeoAssistantService:
                             "is_estimate": summary.get("is_estimate", False),
                         }
                     ]
+                model_metric_ids = {id(metric) for metric in self.model_context_metrics(metrics)}
                 serialized_metrics: list[dict[str, Any]] = []
                 for metric in metrics:
                     if not isinstance(metric, dict):
@@ -433,6 +443,8 @@ class GeoAssistantService:
                         is_estimate=bool(metric.get("is_estimate")),
                     )
                     context_metrics.append(item)
+                    if id(metric) not in model_metric_ids:
+                        continue
                     serialized_metrics.append(item.model_dump(mode="json"))
                     serialized_metrics[-1]["value"] = self.rounded_context_value(item.value, item.unit)
                     serialized_metrics[-1]["measurement_quality"] = (