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/execution/runner.py

@@ -8,8 +8,9 @@ from pathlib import Path
 
 from rag_eval.adapters.http import HttpAppAdapter
 from rag_eval.adapters.python import PythonFunctionAdapter
+from rag_eval.advisor import run_advisor
 from rag_eval.config.loader import load_scenario
-from rag_eval.metrics.factory import build_metric_pipeline
+from rag_eval.metrics.factory import build_models, build_metric_pipeline
 from rag_eval.reporting.writers import write_run_artifacts
 from rag_eval.settings import EvaluationSettings
 from rag_eval.shared.models import Scenario
@@ -67,10 +68,17 @@ def run_scenario(
     logger.info("[runner] scenario loaded: name=%s  mode=%s  max_samples=%s",
                 scenario.scenario_name, scenario.mode, scenario.runtime.max_samples)
 
+    # Build models once; reuse llm in both MetricPipeline and advisor.
+    llm, embeddings = build_models(scenario.judge_model, scenario.embedding_model, settings)
+
     adapter = build_adapter(scenario)
-    pipeline = build_metric_pipeline(scenario, settings)
+    pipeline = build_metric_pipeline(scenario, settings, llm=llm, embeddings=embeddings)
     evaluator = Evaluator(scenario=scenario, metric_pipeline=pipeline, app_adapter=adapter)
     result = evaluator.evaluate()
     write_run_artifacts(result)
     logger.info("[runner] artifacts written for run_id=%s", result.run_id)
+
+    # Optimization advisor — runs only if scenario.optimization_advisor is True.
+    run_advisor(result, scenario, llm)
+
     return result