feat: BossAi 老板AI助手初始版本

包含功能:
- 法务参谋/经营决策/生活助理/情绪中心四大AI对话模块
- 专家咨询系统(用户选定→专家确认→咨询→回复→评价)
- 角色登录(用户/专家/管理员)+ ProtectedRoute 权限控制
- 管理后台(用户管理/专家管理/AI使用统计/专家活跃度)
- 专家工作台(待确认服务/待回复咨询/已回复咨询/服务中用户)
- 服务关系取消(用户和专家均可取消,需自己确认)
- 全页面移动端响应式适配
- 知识库RAG检索
- 对话历史(普通/保密模式)

Generated with [Devin](https://devin.ai)

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>

run_eval.py

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+"""法务评测集批量测试:用 assess() 分诊器跑全部评测问题,输出准确率报告。
+
+用法: python3 run_eval.py
+输出: eval_report.json + 控制台统计
+"""
+import json
+from collections import Counter
+from bossai import assess
+
+
+def run_eval(eval_file: str = "eval_set.json") -> dict:
+    """跑全部评测问题,对比预期风险等级和实际分诊结果。"""
+    with open(eval_file, encoding="utf-8") as f:
+        items = json.load(f)
+
+    results = []
+    correct = 0
+    total = len(items)
+
+    for item in items:
+        card = assess(item["question"])
+        actual = card.level
+        expected = item["expected_risk"]
+        is_correct = actual == expected
+        if is_correct:
+            correct += 1
+        results.append({
+            "id": item["id"],
+            "question": item["question"],
+            "category": item["category"],
+            "region": item["region"],
+            "expected_risk": expected,
+            "actual_risk": actual,
+            "correct": is_correct,
+        })
+
+    # 按分类统计
+    by_category = {}
+    for r in results:
+        cat = r["category"]
+        if cat not in by_category:
+            by_category[cat] = {"total": 0, "correct": 0}
+        by_category[cat]["total"] += 1
+        if r["correct"]:
+            by_category[cat]["correct"] += 1
+
+    # 按风险等级统计
+    by_risk = {}
+    for r in results:
+        risk = r["expected_risk"]
+        if risk not in by_risk:
+            by_risk[risk] = {"total": 0, "correct": 0}
+        by_risk[risk]["total"] += 1
+        if r["correct"]:
+            by_risk[risk]["correct"] += 1
+
+    # 混淆矩阵
+    confusion = Counter()
+    for r in results:
+        confusion[(r["expected_risk"], r["actual_risk"])] += 1
+
+    accuracy = correct / total if total else 0
+    report = {
+        "total": total,
+        "correct": correct,
+        "accuracy": round(accuracy, 4),
+        "by_category": {k: {**v, "accuracy": round(v["correct"] / v["total"], 4)} for k, v in by_category.items()},
+        "by_risk": {k: {**v, "accuracy": round(v["correct"] / v["total"], 4)} for k, v in by_risk.items()},
+        "confusion_matrix": {f"{k[0]}->{k[1]}": v for k, v in sorted(confusion.items())},
+        "results": results,
+    }
+
+    with open("eval_report.json", "w", encoding="utf-8") as f:
+        json.dump(report, f, ensure_ascii=False, indent=2)
+
+    return report
+
+
+if __name__ == "__main__":
+    report = run_eval()
+    print(f"\n===== 法务评测报告 =====")
+    print(f"总数: {report['total']}  正确: {report['correct']}  准确率: {report['accuracy']:.1%}")
+    print(f"\n按分类:")
+    for cat, stats in sorted(report["by_category"].items()):
+        print(f"  {cat:4} {stats['correct']}/{stats['total']} = {stats['accuracy']:.1%}")
+    print(f"\n按风险等级:")
+    for risk, stats in sorted(report["by_risk"].items()):
+        print(f"  {risk:6} {stats['correct']}/{stats['total']} = {stats['accuracy']:.1%}")
+    print(f"\n混淆矩阵(expected->actual: count):")
+    for k, v in sorted(report["confusion_matrix"].items()):
+        if not k.startswith(("green->green", "amber->amber", "orange->orange", "red->red")):
+            print(f"  {k}: {v}")
+    print(f"\n详细报告已写入 eval_report.json")