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/config/loader.py

@@ -61,6 +61,7 @@ def load_scenario(path: str | Path) -> Scenario:
             max_samples=model.runtime.max_samples,
         ),
         source_path=scenario_path,
+        optimization_advisor=model.optimization_advisor,
     )
     # Run cross-field checks after all relative paths have been resolved.
     validate_scenario(scenario)

rag_eval/config/schema.py

@@ -54,6 +54,7 @@ class ScenarioModel(BaseModel):
     metrics: list[str]
     output_dir: str
     runtime: RuntimeConfigModel = Field(default_factory=RuntimeConfigModel)
+    optimization_advisor: bool = False
 
     @field_validator("metrics")
     @classmethod