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/advisor/llm_analyzer.py

@@ -0,0 +1,99 @@
+"""LLM-powered analysis of rule diagnostics and low-score samples."""
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from .rules import Diagnosis
+
+logger = logging.getLogger("rag_eval.advisor")
+
+_PROMPT_TEMPLATE = """\
+你是一个 RAG 系统优化专家，正在分析西门子医疗 CT 文档问答系统的评测结果。
+请用中文撰写一份优化建议报告，格式为 Markdown。
+
+## 评测诊断摘要
+
+{diagnosis_summary}
+
+## 低分样本示例
+
+{low_sample_text}
+
+## 报告要求
+
+1. 按指标分节（## 指标名  [severity]），先解释"为什么低"（结合低分样本具体分析），再给出"具体怎么改"
+2. "具体怎么改"要结合低分样本的实际内容，而不只是泛泛建议
+3. 最后写一节 **## 优先优化次序**，按性价比排序（不增加 LLM 调用次数的优化优先）
+4. 语言简洁，面向工程师，不要废话，不要重复列表内容
+
+只输出 Markdown 报告正文，不要任何前置说明。
+"""
+
+
+def _build_diagnosis_summary(diagnoses: list[Diagnosis]) -> str:
+    lines = []
+    for d in diagnoses:
+        direction = "（越低越好）" if d.metric == "noise_sensitivity" else ""
+        lines.append(
+            f"- **{d.metric}** {direction} 均值={d.mean_score:.4f}，"
+            f"阈值={d.threshold}，严重程度={d.severity}"
+        )
+        lines.append(f"  - 可能原因：{'; '.join(d.root_causes)}")
+        lines.append(f"  - 建议动作：{'; '.join(d.suggested_actions)}")
+    return "\n".join(lines)
+
+
+def _build_low_sample_text(diagnoses: list[Diagnosis]) -> str:
+    lines = []
+    for d in diagnoses:
+        if not d.low_samples:
+            continue
+        lines.append(f"### {d.metric} 低分样本（最多 3 条）")
+        for i, s in enumerate(d.low_samples, 1):
+            score = s.get(d.metric, "N/A")
+            lines.append(f"\n**样本 {i}**（分数={score}）")
+            lines.append(f"- 问题：{s.get('question', '')}")
+            lines.append(f"- 回答：{s.get('answer', '')[:300]}")
+            lines.append(f"- 标准答案：{s.get('ground_truth', '')[:200]}")
+    return "\n".join(lines)
+
+
+async def analyze(
+    diagnoses: list[Diagnosis],
+    llm: Any,
+    scenario_name: str,
+) -> str:
+    """Call the judge LLM to generate a Chinese optimization report.
+
+    Args:
+        diagnoses: Non-empty list of Diagnosis from rules.diagnose().
+        llm: RAGAS LLM wrapper (has .agenerate() method).
+        scenario_name: Used only for logging.
+
+    Returns:
+        LLM-generated Markdown string, or "" on failure (triggers writer fallback).
+    """
+    if not diagnoses:
+        return ""
+
+    diagnosis_summary = _build_diagnosis_summary(diagnoses)
+    low_sample_text = _build_low_sample_text(diagnoses)
+    prompt = _PROMPT_TEMPLATE.format(
+        diagnosis_summary=diagnosis_summary,
+        low_sample_text=low_sample_text,
+    )
+
+    try:
+        logger.info("[advisor] calling LLM for optimization analysis  scenario=%s", scenario_name)
+        from langchain_core.messages import HumanMessage
+        result = await llm.agenerate(texts=[[HumanMessage(content=prompt)]])
+        text = result.generations[0][0].text.strip()
+        logger.info("[advisor] LLM analysis complete  chars=%d", len(text))
+        return text
+    except Exception as exc:
+        logger.warning(
+            "[advisor] LLM analysis failed (%s: %s) — falling back to rule report",
+            type(exc).__name__, exc,
+        )
+        return ""