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Telegram Bot + Webhook 实战指南:从用 Python 造机器人到 Kubernetes 部署
- Authors

- Name
- Youngju Kim
- @fjvbn20031
- 1. Telegram Bot API 基础
- 2. Long Polling 方式(开发用)
- 3. Webhook 方式(生产环境)
- 4. 高级功能
- 5. Kubernetes 部署
- 6. 安全最佳实践
- 7. 小测验
1. Telegram Bot API 基础
1.1 创建机器人
1. 在 Telegram 中搜索 @BotFather
2. 输入 /newbot 命令
3. 输入机器人名称: "My DevOps Bot"
4. 输入机器人用户名: "my_devops_bot" (必须以 _bot 结尾)
5. 领取 API 令牌: 123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11
1.2 Polling vs Webhook
| 方式 | Polling | Webhook |
|---|---|---|
| 工作方式 | 机器人定期向服务器请求更新 | Telegram 向机器人服务器 HTTP POST |
| 基础设施 | 无需服务器(可本地运行) | 需要 HTTPS 服务器 |
| 延迟 | 等于轮询间隔 | 即时 |
| 扩展性 | 单实例 | 可水平扩展 |
| 用途 | 开发/测试 | 生产环境 |
2. Long Polling 方式(开发用)
2.1 基本机器人实现
pip install python-telegram-bot==21.8
`
# bot_polling.py
from telegram import Update, InlineKeyboardButton, InlineKeyboardMarkup
from telegram.ext import (
ApplicationBuilder, CommandHandler, MessageHandler,
CallbackQueryHandler, ContextTypes, filters
)
TOKEN = "YOUR_BOT_TOKEN"
# /start 命令
async def start(update: Update, context: ContextTypes.DEFAULT_TYPE):
keyboard = [
[InlineKeyboardButton("🔍 服务器状态", callback_data="status")],
[InlineKeyboardButton("📊 指标", callback_data="metrics")],
[InlineKeyboardButton("🚀 部署", callback_data="deploy")],
]
reply_markup = InlineKeyboardMarkup(keyboard)
await update.message.reply_text(
"欢迎来到 DevOps Bot! 🤖\n请选择你想执行的操作:",
reply_markup=reply_markup
)
# /status 命令
async def status(update: Update, context: ContextTypes.DEFAULT_TYPE):
import subprocess
result = subprocess.run(
["kubectl", "get", "nodes", "-o", "wide"],
capture_output=True, text=True, timeout=10
)
await update.message.reply_text(
f"```\n{result.stdout}\n```",
parse_mode="MarkdownV2"
)
# 内联按钮回调
async def button_callback(update: Update, context: ContextTypes.DEFAULT_TYPE):
query = update.callback_query
await query.answer()
if query.data == "status":
await query.edit_message_text("🔍 正在检查服务器状态...")
# 执行 kubectl
result = check_cluster_status()
await query.edit_message_text(f"✅ 集群状态:\n```\n{result}\n```",
parse_mode="MarkdownV2")
elif query.data == "metrics":
await query.edit_message_text("📊 正在采集指标...")
metrics = get_prometheus_metrics()
await query.edit_message_text(f"📊 当前指标:\n{metrics}")
elif query.data == "deploy":
keyboard = [
[InlineKeyboardButton("✅ 批准", callback_data="deploy_approve")],
[InlineKeyboardButton("❌ 取消", callback_data="deploy_cancel")],
]
await query.edit_message_text(
"🚀 确认要执行部署吗?",
reply_markup=InlineKeyboardMarkup(keyboard)
)
# 处理普通消息
async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE):
text = update.message.text
# AI 回复 (对接 OpenAI)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": "You are a DevOps assistant."},
{"role": "user", "content": text}
]
)
await update.message.reply_text(response.choices[0].message.content)
# 运行应用
app = ApplicationBuilder().token(TOKEN).build()
app.add_handler(CommandHandler("start", start))
app.add_handler(CommandHandler("status", status))
app.add_handler(CallbackQueryHandler(button_callback))
app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message))
print("Bot is running...")
app.run_polling()
3. Webhook 方式(生产环境)
3.1 FastAPI + Webhook
# bot_webhook.py
from fastapi import FastAPI, Request
from telegram import Update, Bot
from telegram.ext import Application, CommandHandler, MessageHandler, filters
import uvicorn
TOKEN = "YOUR_BOT_TOKEN"
WEBHOOK_URL = "https://bot.example.com/webhook"
# FastAPI 应用
fastapi_app = FastAPI()
# Telegram Application
tg_app = Application.builder().token(TOKEN).build()
# 注册处理器
async def start(update: Update, context):
await update.message.reply_text("Hello! 🤖")
async def echo(update: Update, context):
await update.message.reply_text(f"Echo: {update.message.text}")
tg_app.add_handler(CommandHandler("start", start))
tg_app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, echo))
@fastapi_app.on_event("startup")
async def on_startup():
await tg_app.initialize()
await tg_app.start()
# 设置 Webhook
await tg_app.bot.set_webhook(
url=f"{WEBHOOK_URL}/{TOKEN}",
allowed_updates=["message", "callback_query"],
drop_pending_updates=True,
)
print(f"Webhook set to {WEBHOOK_URL}/{TOKEN}")
@fastapi_app.on_event("shutdown")
async def on_shutdown():
await tg_app.stop()
await tg_app.shutdown()
@fastapi_app.post(f"/webhook/{TOKEN}")
async def webhook(request: Request):
data = await request.json()
update = Update.de_json(data, tg_app.bot)
await tg_app.process_update(update)
return {"ok": True}
@fastapi_app.get("/health")
async def health():
return {"status": "ok"}
if __name__ == "__main__":
uvicorn.run(fastapi_app, host="0.0.0.0", port=8080)
3.2 确认 Webhook 配置
# 查看 Webhook 状态
curl "https://api.telegram.org/bot${TOKEN}/getWebhookInfo" | jq
# 删除 Webhook (切换回 Polling 模式时)
curl "https://api.telegram.org/bot${TOKEN}/deleteWebhook"
4. 高级功能
4.1 对话流程(ConversationHandler)
from telegram.ext import ConversationHandler
# 定义状态
NAME, EMAIL, CONFIRM = range(3)
async def ticket_start(update, context):
await update.message.reply_text("开始创建工单。请输入标题:")
return NAME
async def ticket_name(update, context):
context.user_data["title"] = update.message.text
await update.message.reply_text("请输入描述:")
return EMAIL
async def ticket_desc(update, context):
context.user_data["description"] = update.message.text
title = context.user_data["title"]
desc = context.user_data["description"]
keyboard = [
[InlineKeyboardButton("✅ 创建", callback_data="confirm_yes")],
[InlineKeyboardButton("❌ 取消", callback_data="confirm_no")],
]
await update.message.reply_text(
f"📋 工单确认:\n标题: {title}\n描述: {desc}",
reply_markup=InlineKeyboardMarkup(keyboard)
)
return CONFIRM
conv_handler = ConversationHandler(
entry_points=[CommandHandler("ticket", ticket_start)],
states={
NAME: [MessageHandler(filters.TEXT & ~filters.COMMAND, ticket_name)],
EMAIL: [MessageHandler(filters.TEXT & ~filters.COMMAND, ticket_desc)],
CONFIRM: [CallbackQueryHandler(ticket_confirm)],
},
fallbacks=[CommandHandler("cancel", cancel)],
)
4.2 定时任务(定期通知)
from telegram.ext import ApplicationBuilder
async def daily_report(context: ContextTypes.DEFAULT_TYPE):
"""每天上午 9 点的报告"""
chat_id = context.job.data["chat_id"]
# 采集指标
cpu = get_cluster_cpu()
memory = get_cluster_memory()
pods = get_pod_count()
message = (
"📊 Daily Cluster Report\n"
f"━━━━━━━━━━━━━━\n"
f"🖥 CPU: {cpu}%\n"
f"💾 Memory: {memory}%\n"
f"📦 Pods: {pods}\n"
f"━━━━━━━━━━━━━━"
)
await context.bot.send_message(chat_id=chat_id, text=message)
# 注册定时任务
async def setup_schedule(update, context):
chat_id = update.effective_chat.id
context.job_queue.run_daily(
daily_report,
time=datetime.time(hour=9, minute=0, tzinfo=ZoneInfo("Asia/Seoul")),
data={"chat_id": chat_id},
name=f"daily_report_{chat_id}"
)
await update.message.reply_text("✅ 每天上午 9 点会给你发送报告!")
4.3 文件上传/下载
async def handle_document(update: Update, context):
"""处理文件上传"""
doc = update.message.document
file = await context.bot.get_file(doc.file_id)
# 下载
local_path = f"/tmp/{doc.file_name}"
await file.download_to_drive(local_path)
await update.message.reply_text(f"📁 文件保存完成: {doc.file_name}")
async def send_file(update: Update, context):
"""发送文件"""
await context.bot.send_document(
chat_id=update.effective_chat.id,
document=open("/tmp/report.pdf", "rb"),
filename="cluster_report.pdf",
caption="📊 集群报告"
)
5. Kubernetes 部署
5.1 Dockerfile
FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8080
CMD ["uvicorn", "bot_webhook:fastapi_app", "--host", "0.0.0.0", "--port", "8080"]
5.2 Kubernetes 清单
apiVersion: apps/v1
kind: Deployment
metadata:
name: telegram-bot
namespace: chatbot
spec:
replicas: 2
selector:
matchLabels:
app: telegram-bot
template:
metadata:
labels:
app: telegram-bot
spec:
containers:
- name: bot
image: registry.example.com/telegram-bot:latest
ports:
- containerPort: 8080
env:
- name: BOT_TOKEN
valueFrom:
secretKeyRef:
name: telegram-bot-secret
key: token
- name: WEBHOOK_URL
value: 'https://bot.example.com'
resources:
requests:
cpu: 100m
memory: 128Mi
limits:
cpu: 500m
memory: 256Mi
livenessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 10
periodSeconds: 30
readinessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 5
periodSeconds: 10
---
apiVersion: v1
kind: Service
metadata:
name: telegram-bot
namespace: chatbot
spec:
selector:
app: telegram-bot
ports:
- port: 80
targetPort: 8080
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: telegram-bot
namespace: chatbot
annotations:
cert-manager.io/cluster-issuer: letsencrypt-prod
spec:
tls:
- hosts:
- bot.example.com
secretName: telegram-bot-tls
rules:
- host: bot.example.com
http:
paths:
- path: /webhook
pathType: Prefix
backend:
service:
name: telegram-bot
port:
number: 80
6. 安全最佳实践
# 1) 在 Webhook URL 中包含令牌 (防止未授权请求)
WEBHOOK_PATH = f"/webhook/{TOKEN}"
# 2) IP 白名单 (Telegram 服务器 IP)
TELEGRAM_IPS = ["149.154.160.0/20", "91.108.4.0/22"]
from fastapi import Request, HTTPException
import ipaddress
@fastapi_app.middleware("http")
async def check_telegram_ip(request: Request, call_next):
if request.url.path.startswith("/webhook"):
client_ip = ipaddress.ip_address(request.client.host)
allowed = any(
client_ip in ipaddress.ip_network(net)
for net in TELEGRAM_IPS
)
if not allowed:
raise HTTPException(status_code=403)
return await call_next(request)
# 3) Secret Token (Telegram Bot API 6.1+)
await bot.set_webhook(
url=WEBHOOK_URL,
secret_token="my-secret-token-123"
)
@fastapi_app.post(WEBHOOK_PATH)
async def webhook(request: Request):
if request.headers.get("X-Telegram-Bot-Api-Secret-Token") != "my-secret-token-123":
raise HTTPException(status_code=403)
# ...
7. 小测验
Q1. Polling 与 Webhook 最大的差别是什么?
Polling:机器人定期向 Telegram 服务器请求更新。Webhook:Telegram 以 HTTP POST 即时推送到机器人服务器。Webhook 延迟更低,服务器资源利用也更高效。
Q2. 要使用 Webhook,必须具备什么?
带 HTTPS 证书的公网服务器。Telegram 不允许 HTTP Webhook。可以使用 Let's Encrypt 等免费证书。
Q3. ConversationHandler 的用途是什么?
管理 多步骤对话流程。按状态(state)映射不同的处理器,实现随用户输入推进的对话场景(工单创建、配置向导等)。
Q4. InlineKeyboardButton 的 callback_data 起什么作用?
点击按钮时传给机器人的 标识字符串。在 CallbackQueryHandler 中基于该值做分支处理。最大 64 字节。
Q5. 为什么要在 Webhook URL 中包含机器人令牌?
防止未授权请求。阻止不知道令牌的外部人员发送任意 payload。此外还建议校验 X-Telegram-Bot-Api-Secret-Token 请求头。
Q6. 在 Kubernetes 上以 2 个以上 replica 运行 Telegram 机器人时要注意什么?
Webhook 方式下 所有 replica 共享同一个 Webhook URL,因此没有问题(由负载均衡器分发)。但 Polling 只能 由单个实例 运行(否则会重复接收)。
Q7. 为什么 job_queue.run_daily() 的 timezone 设置很重要?
服务器时间与用户时间可能不同。尤其 Kubernetes Pod 默认是 UTC。必须用 ZoneInfo("Asia/Seoul") 明确指定,才能按韩国时间准确运行。