feat(advisor): add optimization advisor module

- rag_eval/advisor/: new package with rules engine, LLM analyzer, writer
  - rules.py: 7-metric diagnostic rules (warning/critical thresholds, top-3 low samples)
  - llm_analyzer.py: Chinese optimization report via judge_model, graceful fallback
  - writer.py: writes optimization_advice.md + log summary
  - __init__.py: run_advisor() entry point (no-op when optimization_advisor=False)
- Scenario.optimization_advisor: new bool field (default False)
- ScenarioModel: same field added, loader.py透传
- RunArtifactPaths.advice_md: new path field
- factory.py: build_models() now public; build_metric_pipeline() accepts pre-built llm/embeddings
- runner.py: lifts llm, passes to pipeline and advisor; calls run_advisor() at end
- siemens online YAML: optimization_advisor: true enabled
- tests: 9 rules tests + 6 writer tests, all pass
- docs: advisor section added to engine-flow.md and architecture.md

Co-Authored-By: Claude <noreply@anthropic.com>

rag_eval/shared/models.py

@@ -76,6 +76,7 @@ class Scenario:
     runtime: RuntimeConfig = field(default_factory=RuntimeConfig)
     app_adapter: AppAdapterConfig | None = None
     source_path: Path | None = None
+    optimization_advisor: bool = False
 
     def snapshot(self) -> dict[str, Any]:
         """Serialize the scenario into a reporting-friendly dictionary snapshot."""
@@ -159,3 +160,4 @@ class RunArtifactPaths:
     invalid_csv: Path
     summary_md: Path
     metadata_json: Path
+    advice_md: Path | None = None