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完善系统健康面板并接入AI运行态观测

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liuwei
2026-04-30 15:12:47 +08:00
parent 83910b287b
commit 4ddab01b8d
4 changed files with 206 additions and 9 deletions
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71
admin/dashboard/blueprints/system.py
View File

@@ -15,6 +15,7 @@ import toml
from utils.markdown_to_image import get_md2img_health_snapshot, warmup_md2img_browser_sync
from utils.ai.llm_registry import LLMRegistry
from base.plugin_common.plugin_interface import PluginStatus
from utils.ai.unified_llm import UnifiedLLMClient
# 创建系统信息蓝图
system_bp = Blueprint('system', __name__)
@@ -403,6 +404,33 @@ def api_system_health_summary():
# 错误数量直接复用现有统计库，避免为了首页卡片再单独写一套 SQL。
_, recent_error_count = server.stats_db.get_error_logs(days=1, page=1, limit=1)
# 基础设施健康：
# 1. MySQL 用最轻量的 SELECT 1 做可用性探测；
# 2. Redis 用 PING 验证连接池当前是否可拿到可用连接；
# 3. 即使探测失败也只反馈到看板，不影响主接口整体返回。
mysql_status = "healthy"
mysql_summary = "连接正常"
try:
mysql_conn = server.db_manager.get_mysql_connection()
try:
with mysql_conn.cursor() as cursor:
cursor.execute("SELECT 1")
cursor.fetchone()
finally:
mysql_conn.close()
except Exception as mysql_error:
mysql_status = "danger"
mysql_summary = f"MySQL 探测失败: {mysql_error}"
redis_status = "healthy"
redis_summary = "连接正常"
try:
redis_conn = server.db_manager.get_redis_connection()
redis_conn.ping()
except Exception as redis_error:
redis_status = "danger"
redis_summary = f"Redis 探测失败: {redis_error}"
# md2img 健康快照已经有现成实现，这里只做聚合，不主动预热运行时。
md2img_snapshot = get_md2img_health_snapshot(ensure_runtime=False) or {}
browser_ready = bool(
@@ -452,6 +480,28 @@ def api_system_health_summary():
md2img_status = "danger"
md2img_summary = "运行时未就绪，相关转图能力可能不可用"
# AI 运行态：
# 1. 统一从 UnifiedLLMClient 最近调用窗口读取，避免各插件单独维护监控数据；
# 2. 若当前窗口还没有调用记录，就明确返回“暂无调用”，避免误判成异常。
ai_runtime = UnifiedLLMClient.get_runtime_snapshot()
ai_total_calls = int(ai_runtime.get("total_calls") or 0)
ai_failed_calls = int(ai_runtime.get("failed_calls") or 0)
if ai_total_calls <= 0:
ai_status = "warning"
ai_summary = "最近窗口内暂无统一 LLM 调用记录"
elif ai_failed_calls > 0:
ai_status = "warning"
ai_summary = (
f"最近 {ai_total_calls} 次调用中失败 {ai_failed_calls} 次，"
f"平均耗时 {ai_runtime.get('avg_latency_ms', 0)}ms"
)
else:
ai_status = "healthy"
ai_summary = (
f"最近 {ai_total_calls} 次调用全部成功，"
f"平均耗时 {ai_runtime.get('avg_latency_ms', 0)}ms"
)
return jsonify({
"success": True,
"data": {
@@ -473,6 +523,27 @@ def api_system_health_summary():
"recent_24h_count": recent_error_count,
"summary": error_summary,
},
"infrastructure": {
"status": "healthy" if mysql_status == "healthy" and redis_status == "healthy" else "danger",
"summary": (
"MySQL / Redis 均正常"
if mysql_status == "healthy" and redis_status == "healthy"
else "存在基础设施连接异常"
),
"mysql": {
"status": mysql_status,
"summary": mysql_summary,
},
"redis": {
"status": redis_status,
"summary": redis_summary,
},
},
"ai_runtime": {
"status": ai_status,
"summary": ai_summary,
**ai_runtime,
},
"md2img": {
"status": md2img_status,
"healthy": md2img_healthy,

40
admin/dashboard/templates/index.html
View File

@@ -355,6 +355,26 @@
recent_24h_count: 0,
summary: '加载中...'
},
infrastructure: {
status: 'warning',
summary: '加载中...',
mysql: {
status: 'warning',
summary: '加载中...'
},
redis: {
status: 'warning',
summary: '加载中...'
}
},
ai_runtime: {
status: 'warning',
total_calls: 0,
failed_calls: 0,
avg_latency_ms: 0,
summary: '加载中...',
last_call: {}
},
md2img: {
status: 'warning',
healthy: false,
@@ -401,6 +421,8 @@
const robot = this.healthSummary.robot || {};
const plugins = this.healthSummary.plugins || {};
const errors = this.healthSummary.errors || {};
const infrastructure = this.healthSummary.infrastructure || {};
const aiRuntime = this.healthSummary.ai_runtime || {};
const md2img = this.healthSummary.md2img || {};
return [
{
@@ -427,6 +449,22 @@
summary: errors.summary || '暂无状态',
extra: '统计窗口：近 24 小时'
},
{
key: 'infrastructure',
title: '基础设施',
status: infrastructure.status || 'warning',
value: infrastructure.status === 'healthy' ? '正常' : '异常',
summary: infrastructure.summary || '暂无状态',
extra: `MySQL：${((infrastructure.mysql || {}).status === 'healthy') ? '正常' : '异常'} / Redis：${((infrastructure.redis || {}).status === 'healthy') ? '正常' : '异常'}`
},
{
key: 'ai_runtime',
title: 'AI 运行态',
status: aiRuntime.status || 'warning',
value: `${aiRuntime.avg_latency_ms || 0} ms`,
summary: aiRuntime.summary || '暂无状态',
extra: `最近调用 ${aiRuntime.total_calls || 0} 次，失败 ${aiRuntime.failed_calls || 0} 次`
},
{
key: 'md2img',
title: 'Markdown 转图',
@@ -978,7 +1016,7 @@
.health-grid {
display: grid;
grid-template-columns: repeat(4, minmax(0, 1fr));
grid-template-columns: repeat(3, minmax(0, 1fr));
gap: 16px;
margin-top: 18px;
}

2
docs/工程优化与Feature清单.md
View File

@@ -18,6 +18,7 @@
- 已将插件调用统计改为主链路直接埋点，降低维护复杂度
- 已在消息主链路接入 `trace_id`，用于串联消息处理、插件统计与异常日志
- 已在后台首页补充“系统健康快照”，可集中查看机器人连接、插件运行、近 24 小时异常与 md2img 运行状态
- 已补充 MySQL / Redis 连接探测与统一 LLM 最近调用快照，基础设施与 AI 运行态可直接在首页查看
## 2. 项目现状判断
@@ -314,6 +315,7 @@
当前进展：
- 第一阶段已完成：首页已增加系统健康快照，可快速查看核心运行状态
- 第二阶段已完成：已补充基础设施连通性与 AI 最近调用耗时/成功率快照
- 后续可继续补充更细粒度的吞吐、延迟、存储连接与 AI 调用链指标
建议内容：

102
utils/ai/unified_llm.py
View File

@@ -5,6 +5,8 @@ import binascii
import json
import mimetypes
import time
from collections import deque
from threading import Lock
from typing import Any, Dict, List, Optional, Tuple
from urllib.parse import urlparse
@@ -18,6 +20,13 @@ from utils.ai.llm_registry import LLMRegistry
class UnifiedLLMClient:
"""统一的 LLM 调用客户端，兼容 OpenAI-compatible 与 Dify。"""
# 运行时观测快照：
# 1. 只保留最近一小段调用窗口，避免无限增长；
# 2. 放在统一客户端层，所有复用该客户端的插件天然受益；
# 3. 这里存的不是业务明细，而是运维看板需要的轻量健康指标。
_runtime_metrics = deque(maxlen=50)
_runtime_lock = Lock()
def __init__(self, config: Optional[Dict[str, Any]] = None):
self.LOG = logger
self.raw_config = config or {}
@@ -41,6 +50,62 @@ class UnifiedLLMClient:
self.default_system_prompt = str(self.config.get("system_prompt", "")).strip()
self.last_error = ""
@classmethod
def _record_runtime_metric(
cls,
*,
provider: str,
backend: str,
scene: str,
model: str,
success: bool,
latency_ms: float,
error: str = "",
) -> None:
"""记录最近一次 LLM 调用结果，供后台健康面板聚合展示。"""
with cls._runtime_lock:
cls._runtime_metrics.append({
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
"provider": str(provider or "").strip(),
"backend": str(backend or "").strip(),
"scene": str(scene or "").strip(),
"model": str(model or "").strip(),
"success": bool(success),
"latency_ms": round(float(latency_ms or 0.0), 2),
"error": str(error or "").strip()[:300],
})
@classmethod
def get_runtime_snapshot(cls) -> Dict[str, Any]:
"""返回最近调用窗口的聚合快照，供后台可观测性接口直接复用。"""
with cls._runtime_lock:
rows = list(cls._runtime_metrics)
total_calls = len(rows)
success_calls = sum(1 for item in rows if item.get("success"))
failed_calls = total_calls - success_calls
avg_latency_ms = round(
sum(float(item.get("latency_ms") or 0.0) for item in rows) / total_calls,
2
) if total_calls else 0.0
last_call = rows[-1] if rows else {}
last_error = ""
for item in reversed(rows):
if not item.get("success") and item.get("error"):
last_error = str(item.get("error") or "").strip()
break
return {
"window_size": cls._runtime_metrics.maxlen,
"total_calls": total_calls,
"success_calls": success_calls,
"failed_calls": failed_calls,
"success_rate": round((success_calls / total_calls) * 100, 2) if total_calls else 0.0,
"avg_latency_ms": avg_latency_ms,
"last_call": last_call,
"last_error": last_error,
}
def is_available(self) -> bool:
if not self.enabled:
return False
@@ -168,29 +233,50 @@ class UnifiedLLMClient:
image_urls: Optional[List[str]] = None,
files: Optional[List[Dict[str, Any]]] = None,
) -> Optional[Dict[str, Any]]:
started_at = time.monotonic()
self.last_error = ""
result: Optional[Dict[str, Any]] = None
if not self.is_available():
self.last_error = "client_unavailable"
return None
if self.provider == "dify":
return self._generate_dify(
elif self.provider == "dify":
result = self._generate_dify(
prompt=prompt,
user=user,
inputs=inputs or {},
tag=tag,
files=files or [],
)
if self.provider == "openai_compatible":
return self._generate_openai(
elif self.provider == "openai_compatible":
result = self._generate_openai(
system_prompt=system_prompt,
user_prompt=user_prompt or prompt,
user=user,
image_urls=image_urls or [],
)
else:
self.last_error = f"unsupported_provider:{self.provider}"
self.last_error = f"unsupported_provider:{self.provider}"
return None
# 统一在出口记录运行时快照，避免每种 provider 都重复埋点逻辑。
usage = (result or {}).get("usage", {}) if isinstance(result, dict) else {}
latency_ms = 0.0
if isinstance(usage, dict) and usage.get("latency") not in (None, ""):
try:
latency_ms = float(usage.get("latency")) * 1000
except Exception:
latency_ms = 0.0
if latency_ms <= 0:
latency_ms = (time.monotonic() - started_at) * 1000
self._record_runtime_metric(
provider=self.provider,
backend=str(self.config.get("backend", "") or ""),
scene=str(self.config.get("scene", "") or ""),
model=self.model or str(self.mode or ""),
success=bool(result and result.get("text")),
latency_ms=latency_ms,
error=self.last_error,
)
return result
def _generate_openai(
self,