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>

scripts/smoke_advisor.py

@@ -0,0 +1,59 @@
+"""Offline smoke-check for the advisor module wiring (no network required)."""
+import math
+import sys
+import tempfile
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from rag_eval.advisor.rules import diagnose
+from rag_eval.advisor.writer import write_advice, _format_log_summary
+
+# Simulate score_rows with low faithfulness and high noise_sensitivity
+rows = [
+    {
+        "sample_id": f"s{i}",
+        "question": f"问题{i}：西门子CT扫描的Flash技术原理是什么？",
+        "answer": f"答案{i}：Flash技术采用双源CT扫描",
+        "ground_truth": f"标准答案{i}：Flash扫描利用双源CT和大螺距实现超低辐射剂量扫描",
+        "faithfulness": 0.3 + i * 0.05,
+        "noise_sensitivity": 0.4 + i * 0.02,
+        "context_recall": 0.75,
+        "semantic_similarity": 0.65,
+    }
+    for i in range(5)
+]
+
+diags = diagnose(rows, metrics=["faithfulness", "noise_sensitivity", "context_recall", "semantic_similarity"])
+print(f"Diagnosed {len(diags)} metric(s):")
+for d in diags:
+    print(f"  {d.metric}: mean={d.mean_score}, severity={d.severity}, low_samples={len(d.low_samples)}")
+
+assert len(diags) >= 2, f"Expected at least 2 diagnoses, got {len(diags)}"
+metrics_hit = {d.metric for d in diags}
+assert "faithfulness" in metrics_hit, "faithfulness should be triggered"
+assert "noise_sensitivity" in metrics_hit, "noise_sensitivity should be triggered"
+
+with tempfile.TemporaryDirectory() as tmp:
+    path = Path(tmp) / "optimization_advice.md"
+    write_advice(
+        diagnoses=diags,
+        llm_markdown="",  # fallback mode (no LLM)
+        advice_path=path,
+        scenario_name="smoke-test-siemens",
+        run_id="2026-06-16T00-00-00",
+        judge_model="deepseek-v4-flash",
+    )
+    content = path.read_text(encoding="utf-8")
+    assert "smoke-test-siemens" in content, "scenario name missing from report"
+    assert "faithfulness" in content, "faithfulness missing from report"
+    assert "noise_sensitivity" in content, "noise_sensitivity missing from report"
+    print(f"\nAdvice file ({len(content)} chars) — assertions OK")
+
+# Verify log summary format
+summary = _format_log_summary(diags, Path("optimization_advice.md"))
+print(f"\nLog summary length: {len(summary)} chars, faithfulness present: {'faithfulness' in summary}")
+assert "触发诊断" in summary
+assert "faithfulness" in summary
+
+print("\nSmoke check PASSED")