feat(ai-services): dedicated GET /avm/v2/feature-importance endpoint (TEC-2760)

Exposes ensemble feature importance as a standalone endpoint per R5.1 spec.
Aggregates XGBoost (0.4) + LightGBM (0.35) + CatBoost (0.25) gain when trained
boosters are loaded; falls back to the curated heuristic ranking otherwise, so
callers can depend on the endpoint during scaffold/heuristic-only runs.

- Factored heuristic drivers into a shared constant (_HEURISTIC_DRIVERS)
- Added AVMv2FeatureImportanceResponse model (model_version + source + drivers)
- Added service.get_feature_importance() public method
- Added tests/test_avm_v2.py::test_feature_importance_heuristic (24 total pass)

Co-Authored-By: Paperclip <noreply@paperclip.ing>

libs/ai-services/app/models/avm_v2.py

@@ -213,6 +213,19 @@ class AVMv2RollbackRequest(BaseModel):
     target_version: str = Field(..., min_length=1, description="Model version to roll back to")
 
 
+class AVMv2FeatureImportanceResponse(BaseModel):
+    """Global feature importance across the loaded ensemble.
+
+    `source` is `"model"` when importances come from the trained boosters
+    (weighted XGBoost gain + LightGBM gain + CatBoost importance), or
+    `"heuristic"` when the service is running without trained artifacts.
+    """
+
+    model_version: str
+    source: str = Field(..., description="One of: model, heuristic")
+    drivers: list[AVMv2FeatureImportance] = Field(default_factory=list)
+
+
 class AVMv1Summary(BaseModel):
     """Compact summary of a v1 prediction for comparison."""