Files
Profeto/src/api/routes/admin_llm.py
T
shangfangjian 45497d2112 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)。
2026-09-21 23:28:58 +08:00

127 lines
4.4 KiB
Python

"""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}