Zabbix MCP Server

from fastapi import APIRouter, HTTPException, Depends, Request from fastapi.security import APIKeyHeader from typing import Dict, Any from ..models.event import WebhookPayload, ZabbixEvent from ..services.analysis import EventAnalysisService from ..services.cache_service import CacheService import os import time from ..core.logging import api_logger router = APIRouter() api_key_header = APIKeyHeader(name="X-API-Key") cache = CacheService() async def verify_api_key(api_key: str = Depends(api_key_header)) -> bool: """Xác thực API key""" expected_key = os.getenv("ZABBIX_WEBHOOK_API_KEY") if not expected_key: api_logger.warning("ZABBIX_WEBHOOK_API_KEY not set in environment") return False return api_key == expected_key async def check_rate_limit(request: Request) -> None: """Kiểm tra rate limit""" client_ip = request.client.host current_time = time.time() # Lấy số request trong 1 phút gần nhất key = f"rate_limit:{client_ip}" requests = await cache.get(key) or [] # Lọc các request trong 1 phút gần nhất requests = [req_time for req_time in requests if current_time - req_time < 60] # Kiểm tra số lượng request if len(requests) >= 60: # Giới hạn 60 request/phút raise HTTPException( status_code=429, detail="Too many requests. Please try again later." ) # Thêm request mới requests.append(current_time) await cache.set(key, requests, expire=60) def get_analysis_service() -> EventAnalysisService: use_ollama = os.getenv("USE_OLLAMA", "false").lower() == "true" if use_ollama: return EventAnalysisService(use_ollama=True) else: return EventAnalysisService( openai_api_key=os.getenv("OPENAI_API_KEY"), model=os.getenv("OPENAI_MODEL", "gpt-4-turbo-preview") ) @router.post("/webhook/zabbix") async def zabbix_webhook( request: Request, payload: WebhookPayload, analysis_service: EventAnalysisService = Depends(get_analysis_service) ) -> Dict[str, Any]: try: # Kiểm tra xác thực if not await verify_api_key(): raise HTTPException( status_code=401, detail="Invalid API key" ) # Kiểm tra rate limit await check_rate_limit(request) # Log request api_logger.info(f"Received webhook from {request.client.host}") api_logger.debug(f"Webhook payload: {payload.dict()}") # Phân tích sự kiện analysis = await analysis_service.analyze_event(payload.event) # Log kết quả phân tích api_logger.info(f"Analysis completed for event {payload.event.event_id}") api_logger.debug(f"Analysis result: {analysis.dict()}") return { "status": "success", "event_id": payload.event.event_id, "analysis": analysis.dict() } except Exception as e: api_logger.error(f"Error processing webhook: {str(e)}") raise HTTPException(status_code=500, detail=str(e))