refactor: admin_settings.py 按职责拆分为 4 个模块

681 行单文件拆为(保留 admin_settings 为 include_router 聚合入口):
- admin_datasources  数据源列表/连通性测试/KeyRing/ingest status (5 路由)
- admin_config       settings CRUD + 运行日志 (4 路由)
- admin_llm          LLM agents/models/ping (3 路由)
- admin_quality      stats/data-completeness/data-quality (4 路由)

所有路由仍挂 /api/v1/admin 且带 dependencies=[Depends(require_admin)]。
app.py 注册方式不变(仍 import admin_settings.router)。

src/api/routes/admin_llm.py

@@ -0,0 +1,126 @@
+"""后台管理:LLM 专家/终裁配置、可用模型探测、连通性测试。
+
+所有接口需管理员鉴权(require_admin)。路由前缀 /api/v1/admin。
+"""
+from __future__ import annotations
+
+import logging
+import time
+
+import httpx
+from fastapi import APIRouter, Depends
+
+from src.api.deps import require_admin
+from src.core.config import settings
+from src.core.http_client import get_client
+from src.core.runtime_config import (
+    AGENT_META,
+    SETTING_DEFS,
+    get_runtime_value,
+    get_setting_origin,
+    mask_value,
+)
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/v1/admin", tags=["admin"], dependencies=[Depends(require_admin)])
+
+
+@router.get("/llm/agents")
+async def list_llm_agents():
+    """各专家/终裁的独立 LLM 配置状态(含当前生效模型的解析结果)。"""
+    out = []
+    for agent in AGENT_META:
+        aid = agent["id"].upper()
+        pfx = f"AGENT_{aid}_"
+        fields = {}
+        for suffix in ("MODEL", "BASE_URL", "API_KEY"):
+            origin, value = await get_setting_origin(f"{pfx}{suffix}")
+            defn = SETTING_DEFS[f"{pfx}{suffix}"]
+            fields[suffix.lower()] = {
+                "configured": origin != "none",
+                "masked": mask_value(value, defn.sensitive),
+                "origin": origin,
+            }
+        # 生效模型 = 覆盖 → 层级默认(专家/终裁 env) → 全局 LLM_MODEL
+        tier_default = (
+            settings.LLM_AGGREGATOR_MODEL if agent["id"] == "aggregator" else settings.LLM_SPECIALIST_MODEL
+        )
+        effective_model = (
+            fields["model"]["masked"]
+            if fields["model"]["configured"]
+            else (tier_default or await get_runtime_value("LLM_MODEL"))
+        )
+        out.append(
+            {
+                "id": agent["id"],
+                "label": agent["label"],
+                "fields": fields,
+                "effective_model": effective_model,
+            }
+        )
+    return out
+
+
+@router.get("/llm/models")
+async def list_llm_models():
+    """探测当前 LLM 服务可用的模型列表(OpenAI 兼容 GET /models)。
+
+    只读探测,不产生费用;配置缺失或服务不可达时返回 ok=false 与原因。
+    """
+    base_url = (await get_runtime_value("LLM_BASE_URL")).rstrip("/")
+    api_key = await get_runtime_value("LLM_API_KEY")
+    if not base_url or not api_key:
+        return {"ok": False, "models": [], "detail": "LLM_BASE_URL 或 LLM_API_KEY 未配置"}
+
+    client = get_client()
+    start = time.monotonic()
+    try:
+        resp = await client.get(
+            f"{base_url}/models",
+            headers={"Authorization": f"Bearer {api_key}"},
+            timeout=httpx.Timeout(connect=10.0, read=20.0, write=10.0, pool=10.0),
+        )
+    except Exception as e:
+        return {
+            "ok": False,
+            "models": [],
+            "latency_ms": int((time.monotonic() - start) * 1000),
+            "detail": f"无法连接 LLM 服务: {e}",
+        }
+
+    latency = int((time.monotonic() - start) * 1000)
+    if resp.status_code in (401, 403):
+        return {"ok": False, "models": [], "latency_ms": latency, "detail": "密钥无效或无权限(HTTP 401/403)"}
+    if resp.status_code != 200:
+        return {"ok": False, "models": [], "latency_ms": latency, "detail": f"服务返回 HTTP {resp.status_code}"}
+
+    try:
+        data = resp.json()
+    except Exception:
+        return {"ok": False, "models": [], "latency_ms": latency, "detail": "响应不是合法 JSON"}
+
+    models: list[str] = []
+    items = data.get("data") if isinstance(data, dict) else None
+    if isinstance(items, list):
+        models = sorted(
+            str(m.get("id")) for m in items if isinstance(m, dict) and m.get("id")
+        )
+    if not models:
+        return {"ok": False, "models": [], "latency_ms": latency, "detail": "服务未返回模型列表"}
+    return {"ok": True, "models": models, "latency_ms": latency, "detail": f"共 {len(models)} 个可用模型"}
+
+
+@router.post("/llm/ping")
+async def llm_ping():
+    """LLM 连通性测试(不依赖比赛)。只发一次 chat 请求验证配置。"""
+    from src.llm.provider import get_default_provider
+    p = await get_default_provider()
+    resp = await p.chat(
+        system="你是测试助手。",
+        user="ping",
+        max_tokens=10,
+    )
+    if resp.error:
+        return {"ok": False, "message": resp.error}
+    return {"ok": True, "message": "LLM 连接正常", "model": p.model}