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>

scenarios/online/siemens-pdf-question-bank-online.yaml

@@ -1,13 +1,19 @@
 scenario_name: siemens-pdf-question-bank-online
 mode: online
 dataset: ../../datasets/raw/generated/siemens-pdf-question-bank.csv
+# judge_model: qwen3.5-flash
 judge_model: deepseek-v4-flash
 embedding_model: text-embedding-v3
+optimization_advisor: true      # 评测结束后自动生成优化建议报告
 metrics:
   - faithfulness
   - answer_relevancy
   - context_recall
   - context_precision
+  # 已启用：鲁棒性 / 端到端指标（数据集已含 ground_truth）
+  - noise_sensitivity        # 鲁棒性：对检索噪声的敏感度
+  - factual_correctness      # 端到端：事实正确性（相对标准答案）
+  - semantic_similarity      # 端到端：语义相似度（embedding，无 LLM 调用）
 output_dir: ../../outputs/online/siemens-pdf-question-bank
 runtime:
   batch_size: 4