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>

docs/rag-eval-architecture.md

@@ -318,6 +318,10 @@ metrics:
   - answer_relevancy
   - context_recall
   - context_precision
+  # 可选：鲁棒性 / 端到端指标（需数据集含 ground_truth），完整列表见 §9.4
+  # - noise_sensitivity
+  # - factual_correctness
+  # - semantic_similarity
 output_dir: runs/legal-assistant-offline-baseline
 runtime:
   batch_size: 4
@@ -338,7 +342,7 @@ runtime:
 - `embedding_model`
   - 负责向量相关指标的模型
 - `metrics`
-  - 本次启用的指标列表
+  - 本次启用的指标列表（完整可选项与依赖见 §9.4）
 - `output_dir`
   - 本次运行结果输出目录
 - `runtime.batch_size`
@@ -399,6 +403,32 @@ app_adapter:
 - embedding model
 - 指标实例
 
+当前支持的指标（`rag_eval/metrics/registry.py` 中的 `SUPPORTED_METRICS`）：
+
+| 指标名 | 层面 | 依赖 |
+|---|---|---|
+| `faithfulness` | 生成 | judge model |
+| `answer_relevancy` | 生成 | judge model + embedding |
+| `context_recall` | 检索 | judge model + ground_truth |
+| `context_precision` | 检索 | judge model + ground_truth |
+| `noise_sensitivity` | 鲁棒性 | judge model + ground_truth |
+| `factual_correctness` | 端到端 | judge model + ground_truth |
+| `semantic_similarity` | 端到端 | embedding + ground_truth（无 LLM 调用） |
+
+后四项以 `ground_truth`（标准答案）为参照，数据集必须提供该字段。新增指标统一在 `registry.py` / `factory.py` / `pipeline.py` 三处对齐装配。
+
+**Optimization Advisor（§11 优化策略落地）：**
+
+评测结束后，若场景配置 `optimization_advisor: true`，则自动调用 `rag_eval/advisor/` 模块：
+- 规则引擎（`rules.py`）对 7 个指标各自设阈值，识别触发项并选取 top-3 低分样本
+- LLM 分析器（`llm_analyzer.py`）结合低分样本生成中文 Markdown 优化建议（复用 judge_model，失败自动降级为纯规则报告）
+- 写出层（`writer.py`）输出 `optimization_advice.md` 并打日志摘要
+
+```yaml
+# 场景配置示例
+optimization_advisor: true
+```
+
 ### 9.5 并发控制
 
 执行层负责并发上限，不把并发策略散落到各指标实现中。