Spring AI框架結(jié)合Sentinel實(shí)現(xiàn)限流功能

更新時(shí)間：2025年06月26日 09:18:41 作者：一只愛擼貓的程序猿

這篇文章主要為大家詳細(xì)介紹了如何實(shí)現(xiàn)一個(gè)簡單的使用Spring AI框架結(jié)合Sentinel限流的功能,場景是一個(gè)智能客服系統(tǒng),感興趣的小伙伴可以了解下

場景描述

構(gòu)建一個(gè)AI智能客服系統(tǒng)，需要對(duì)AI接口調(diào)用進(jìn)行限流保護(hù)，防止惡意請(qǐng)求或突發(fā)流量導(dǎo)致系統(tǒng)崩潰。

項(xiàng)目結(jié)構(gòu)和依賴

1. Maven依賴配置

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 
         http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    
    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>3.2.0</version>
        <relativePath/>
    </parent>
    
    <groupId>com.example</groupId>
    <artifactId>ai-customer-service</artifactId>
    <version>1.0.0</version>
    
    <dependencies>
        <!-- Spring Boot Starter -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        
        <!-- Spring AI -->
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
            <version>0.8.1</version>
        </dependency>
        
        <!-- Sentinel核心庫 -->
        <dependency>
            <groupId>com.alibaba.csp</groupId>
            <artifactId>sentinel-core</artifactId>
            <version>1.8.6</version>
        </dependency>
        
        <!-- Sentinel Spring Boot Starter -->
        <dependency>
            <groupId>com.alibaba.cloud</groupId>
            <artifactId>spring-cloud-starter-alibaba-sentinel</artifactId>
            <version>2022.0.0.0</version>
        </dependency>
        
        <!-- Sentinel Dashboard -->
        <dependency>
            <groupId>com.alibaba.csp</groupId>
            <artifactId>sentinel-transport-simple-http</artifactId>
            <version>1.8.6</version>
        </dependency>
        
        <!-- Redis for rate limiting -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-redis</artifactId>
        </dependency>
        
        <!-- JSON處理 -->
        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-databind</artifactId>
        </dependency>
    </dependencies>
</project>

2. 應(yīng)用配置

# application.yml
server:
  port: 8080

spring:
  application:
    name: ai-customer-service
  
  # Spring AI OpenAI配置
  ai:
    openai:
      api-key: ${OPENAI_API_KEY:your-api-key-here}
      base-url: https://api.openai.com
      chat:
        options:
          model: gpt-3.5-turbo
          temperature: 0.7
          max-tokens: 1000
  
  # Redis配置
  redis:
    host: localhost
    port: 6379
    database: 0
    timeout: 2000ms
    jedis:
      pool:
        max-active: 8
        max-idle: 8
        min-idle: 0

# Sentinel配置
management:
  endpoints:
    web:
      exposure:
        include: "*"

logging:
  level:
    com.alibaba.csp.sentinel: DEBUG
    com.example: DEBUG

3. Sentinel配置類

package com.example.config;

import com.alibaba.csp.sentinel.annotation.aspectj.SentinelResourceAspect;
import com.alibaba.csp.sentinel.slots.block.RuleConstant;
import com.alibaba.csp.sentinel.slots.block.flow.FlowRule;
import com.alibaba.csp.sentinel.slots.block.flow.FlowRuleManager;
import com.alibaba.csp.sentinel.slots.block.flow.param.ParamFlowRule;
import com.alibaba.csp.sentinel.slots.block.flow.param.ParamFlowRuleManager;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

import javax.annotation.PostConstruct;
import java.util.ArrayList;
import java.util.List;

@Configuration
public class SentinelConfig {

    @Bean
    public SentinelResourceAspect sentinelResourceAspect() {
        return new SentinelResourceAspect();
    }

    @PostConstruct
    public void initFlowRules() {
        initBasicFlowRules();
        initParamFlowRules();
    }

    /**
     * 初始化基礎(chǔ)限流規(guī)則
     */
    private void initBasicFlowRules() {
        List<FlowRule> rules = new ArrayList<>();

        // AI聊天接口限流規(guī)則 - 令牌桶算法
        FlowRule chatRule = new FlowRule();
        chatRule.setResource("ai-chat");
        chatRule.setGrade(RuleConstant.FLOW_GRADE_QPS);
        chatRule.setCount(10); // 每秒10個(gè)請(qǐng)求
        chatRule.setControlBehavior(RuleConstant.CONTROL_BEHAVIOR_RATE_LIMITER);
        rules.add(chatRule);

        // AI問答接口限流規(guī)則 - 預(yù)熱算法
        FlowRule qaRule = new FlowRule();
        qaRule.setResource("ai-qa");
        qaRule.setGrade(RuleConstant.FLOW_GRADE_QPS);
        qaRule.setCount(20);
        qaRule.setControlBehavior(RuleConstant.CONTROL_BEHAVIOR_WARM_UP);
        qaRule.setWarmUpPeriodSec(30); // 30秒預(yù)熱時(shí)間
        rules.add(qaRule);

        // 知識(shí)庫查詢接口 - 排隊(duì)等待
        FlowRule knowledgeRule = new FlowRule();
        knowledgeRule.setResource("knowledge-search");
        knowledgeRule.setGrade(RuleConstant.FLOW_GRADE_QPS);
        knowledgeRule.setCount(15);
        knowledgeRule.setControlBehavior(RuleConstant.CONTROL_BEHAVIOR_RATE_LIMITER);
        knowledgeRule.setMaxQueueingTimeMs(500); // 最大等待500ms
        rules.add(knowledgeRule);

        FlowRuleManager.loadRules(rules);
    }

    /**
     * 初始化熱點(diǎn)參數(shù)限流規(guī)則
     */
    private void initParamFlowRules() {
        List<ParamFlowRule> rules = new ArrayList<>();

        // 基于用戶ID的限流
        ParamFlowRule userRule = new ParamFlowRule();
        userRule.setResource("ai-chat");
        userRule.setParamIdx(0); // 第一個(gè)參數(shù)是用戶ID
        userRule.setGrade(RuleConstant.FLOW_GRADE_QPS);
        userRule.setCount(3); // 單個(gè)用戶每秒最多3次請(qǐng)求
        userRule.setControlBehavior(RuleConstant.CONTROL_BEHAVIOR_RATE_LIMITER);
        rules.add(userRule);

        ParamFlowRuleManager.loadRules(rules);
    }
}

4. 自定義限流異常處理

package com.example.handler;

import com.alibaba.csp.sentinel.slots.block.BlockException;
import com.alibaba.csp.sentinel.slots.block.flow.FlowException;
import com.alibaba.csp.sentinel.slots.block.flow.param.ParamFlowException;
import lombok.extern.slf4j.Slf4j;
import org.springframework.stereotype.Component;

@Slf4j
@Component
public class SentinelBlockHandler {

    /**
     * AI聊天接口限流降級(jí)處理
     */
    public static String handleAiChatBlock(String userId, String message, BlockException ex) {
        log.warn("AI聊天接口被限流，用戶ID: {}, 異常類型: {}", userId, ex.getClass().getSimpleName());
        
        if (ex instanceof FlowException) {
            return "系統(tǒng)繁忙，請(qǐng)稍后再試。我們正在為您排隊(duì)處理...";
        } else if (ex instanceof ParamFlowException) {
            return "您的請(qǐng)求過于頻繁，請(qǐng)稍后再試。";
        }
        
        return "系統(tǒng)暫時(shí)無法處理您的請(qǐng)求，請(qǐng)稍后重試。";
    }

    /**
     * AI問答接口限流降級(jí)處理
     */
    public static String handleAiQaBlock(String question, BlockException ex) {
        log.warn("AI問答接口被限流，問題: {}, 異常類型: {}", question, ex.getClass().getSimpleName());
        return "當(dāng)前咨詢?nèi)藬?shù)較多，系統(tǒng)正在預(yù)熱中，請(qǐng)稍后再試。";
    }

    /**
     * 知識(shí)庫搜索限流降級(jí)處理
     */
    public static String handleKnowledgeSearchBlock(String keyword, BlockException ex) {
        log.warn("知識(shí)庫搜索被限流，關(guān)鍵詞: {}", keyword);
        return "知識(shí)庫查詢繁忙，請(qǐng)稍后再試或聯(lián)系人工客服。";
    }
}

5. AI服務(wù)層

package com.example.service;

import com.alibaba.csp.sentinel.annotation.SentinelResource;
import com.alibaba.csp.sentinel.slots.block.BlockException;
import com.example.handler.SentinelBlockHandler;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.chat.ChatClient;
import org.springframework.ai.chat.ChatResponse;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

import java.time.LocalDateTime;
import java.util.concurrent.CompletableFuture;

@Service
@Slf4j
public class AiCustomerService {

    @Autowired
    private ChatClient chatClient;

    /**
     * AI聊天服務(wù) - 使用熱點(diǎn)參數(shù)限流
     */
    @SentinelResource(
        value = "ai-chat",
        blockHandler = "handleAiChatBlock",
        blockHandlerClass = SentinelBlockHandler.class
    )
    public String chatWithAi(String userId, String message) {
        log.info("用戶 {} 發(fā)起聊天請(qǐng)求: {}", userId, message);
        
        try {
            // 構(gòu)建聊天上下文
            String systemPrompt = "你是一個(gè)專業(yè)的客服助手，請(qǐng)禮貌、準(zhǔn)確地回答用戶問題。";
            String fullPrompt = systemPrompt + "\n用戶問題: " + message;
            
            Prompt prompt = new Prompt(new UserMessage(fullPrompt));
            ChatResponse response = chatClient.call(prompt);
            
            String aiResponse = response.getResult().getOutput().getContent();
            log.info("AI回復(fù)用戶 {}: {}", userId, aiResponse);
            
            return aiResponse;
        } catch (Exception e) {
            log.error("AI聊天服務(wù)異常，用戶ID: {}", userId, e);
            return "抱歉，AI服務(wù)暫時(shí)不可用，請(qǐng)聯(lián)系人工客服。";
        }
    }

    /**
     * AI問答服務(wù) - 使用預(yù)熱限流
     */
    @SentinelResource(
        value = "ai-qa",
        blockHandler = "handleAiQaBlock",
        blockHandlerClass = SentinelBlockHandler.class
    )
    public String answerQuestion(String question) {
        log.info("收到問答請(qǐng)求: {}", question);
        
        try {
            String prompt = String.format(
                "作為客服專家，請(qǐng)簡潔準(zhǔn)確地回答以下問題：%s\n" +
                "要求：1. 回答要專業(yè)且易懂 2. 控制在200字以內(nèi) 3. 如果不確定請(qǐng)說明", 
                question
            );
            
            ChatResponse response = chatClient.call(new Prompt(prompt));
            return response.getResult().getOutput().getContent();
        } catch (Exception e) {
            log.error("AI問答服務(wù)異常", e);
            return "抱歉，暫時(shí)無法回答您的問題，請(qǐng)稍后再試。";
        }
    }

    /**
     * 知識(shí)庫搜索 - 使用排隊(duì)等待限流
     */
    @SentinelResource(
        value = "knowledge-search",
        blockHandler = "handleKnowledgeSearchBlock",
        blockHandlerClass = SentinelBlockHandler.class
    )
    public String searchKnowledge(String keyword) {
        log.info("知識(shí)庫搜索: {}", keyword);
        
        try {
            // 模擬知識(shí)庫搜索
            Thread.sleep(100); // 模擬搜索耗時(shí)
            
            String searchPrompt = String.format(
                "基于關(guān)鍵詞 '%s' 搜索相關(guān)知識(shí)，提供簡潔的信息摘要。", 
                keyword
            );
            
            ChatResponse response = chatClient.call(new Prompt(searchPrompt));
            return response.getResult().getOutput().getContent();
        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
            return "搜索被中斷，請(qǐng)重試。";
        } catch (Exception e) {
            log.error("知識(shí)庫搜索異常", e);
            return "知識(shí)庫搜索失敗，請(qǐng)聯(lián)系技術(shù)支持。";
        }
    }

    /**
     * 異步AI處理 - 用于處理復(fù)雜請(qǐng)求
     */
    public CompletableFuture<String> processComplexRequest(String userId, String request) {
        return CompletableFuture.supplyAsync(() -> {
            try {
                return chatWithAi(userId, request);
            } catch (BlockException e) {
                return "請(qǐng)求過于頻繁，已加入處理隊(duì)列，請(qǐng)稍后查看結(jié)果。";
            }
        });
    }
}

6. 控制器層

package com.example.controller;

import com.example.service.AiCustomerService;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.*;

import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.CompletableFuture;

@RestController
@RequestMapping("/api/ai")
@Slf4j
public class AiCustomerController {

    @Autowired
    private AiCustomerService aiCustomerService;

    /**
     * AI聊天接口
     */
    @PostMapping("/chat")
    public ResponseEntity<Map<String, Object>> chat(
            @RequestParam String userId,
            @RequestBody Map<String, String> request) {
        
        String message = request.get("message");
        log.info("接收到聊天請(qǐng)求 - 用戶: {}, 消息: {}", userId, message);
        
        Map<String, Object> response = new HashMap<>();
        
        try {
            String aiResponse = aiCustomerService.chatWithAi(userId, message);
            response.put("success", true);
            response.put("data", aiResponse);
            response.put("timestamp", System.currentTimeMillis());
            return ResponseEntity.ok(response);
        } catch (Exception e) {
            log.error("聊天接口異常", e);
            response.put("success", false);
            response.put("error", "服務(wù)暫時(shí)不可用");
            return ResponseEntity.internalServerError().body(response);
        }
    }

    /**
     * AI問答接口
     */
    @PostMapping("/qa")
    public ResponseEntity<Map<String, Object>> qa(@RequestBody Map<String, String> request) {
        String question = request.get("question");
        log.info("接收到問答請(qǐng)求: {}", question);
        
        Map<String, Object> response = new HashMap<>();
        
        try {
            String answer = aiCustomerService.answerQuestion(question);
            response.put("success", true);
            response.put("question", question);
            response.put("answer", answer);
            response.put("timestamp", System.currentTimeMillis());
            return ResponseEntity.ok(response);
        } catch (Exception e) {
            log.error("問答接口異常", e);
            response.put("success", false);
            response.put("error", "問答服務(wù)暫時(shí)不可用");
            return ResponseEntity.internalServerError().body(response);
        }
    }

    /**
     * 知識(shí)庫搜索接口
     */
    @GetMapping("/knowledge/search")
    public ResponseEntity<Map<String, Object>> searchKnowledge(@RequestParam String keyword) {
        log.info("接收到知識(shí)庫搜索請(qǐng)求: {}", keyword);
        
        Map<String, Object> response = new HashMap<>();
        
        try {
            String result = aiCustomerService.searchKnowledge(keyword);
            response.put("success", true);
            response.put("keyword", keyword);
            response.put("result", result);
            response.put("timestamp", System.currentTimeMillis());
            return ResponseEntity.ok(response);
        } catch (Exception e) {
            log.error("知識(shí)庫搜索異常", e);
            response.put("success", false);
            response.put("error", "知識(shí)庫搜索服務(wù)暫時(shí)不可用");
            return ResponseEntity.internalServerError().body(response);
        }
    }

    /**
     * 異步處理復(fù)雜請(qǐng)求
     */
    @PostMapping("/chat/async")
    public ResponseEntity<Map<String, Object>> chatAsync(
            @RequestParam String userId,
            @RequestBody Map<String, String> request) {
        
        String message = request.get("message");
        Map<String, Object> response = new HashMap<>();
        
        CompletableFuture<String> future = aiCustomerService.processComplexRequest(userId, message);
        
        response.put("success", true);
        response.put("message", "請(qǐng)求已提交，正在處理中...");
        response.put("userId", userId);
        response.put("taskId", System.currentTimeMillis());
        
        // 實(shí)際應(yīng)用中可以返回任務(wù)ID，客戶端輪詢結(jié)果
        return ResponseEntity.ok(response);
    }
}

7. 啟動(dòng)類

package com.example;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.scheduling.annotation.EnableAsync;

@SpringBootApplication
@EnableAsync
public class AiCustomerServiceApplication {
    public static void main(String[] args) {
        // 設(shè)置Sentinel Dashboard地址
        System.setProperty("csp.sentinel.dashboard.server", "localhost:8080");
        System.setProperty("project.name", "ai-customer-service");
        
        SpringApplication.run(AiCustomerServiceApplication.class, args);
    }
}

8. 測(cè)試用例

package com.example.test;

import org.junit.jupiter.api.Test;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.test.context.junit.jupiter.SpringJUnitConfig;

import java.util.concurrent.CountDownLatch;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

@SpringJUnitConfig
@SpringBootTest
public class RateLimitTest {

    @Test
    public void testConcurrentRequests() throws InterruptedException {
        ExecutorService executor = Executors.newFixedThreadPool(20);
        CountDownLatch latch = new CountDownLatch(50);
        
        for (int i = 0; i < 50; i++) {
            final int requestId = i;
            executor.submit(() -> {
                try {
                    // 模擬并發(fā)請(qǐng)求
                    System.out.println("請(qǐng)求 " + requestId + " 開始");
                    Thread.sleep(100);
                    System.out.println("請(qǐng)求 " + requestId + " 完成");
                } catch (InterruptedException e) {
                    Thread.currentThread().interrupt();
                } finally {
                    latch.countDown();
                }
            });
        }
        
        latch.await();
        executor.shutdown();
    }
}

使用示例

1. 啟動(dòng)應(yīng)用

mvn spring-boot:run

2. 測(cè)試接口

聊天接口測(cè)試：

curl -X POST "http://localhost:8080/api/ai/chat?userId=user123" \
     -H "Content-Type: application/json" \
     -d '{"message": "你好，我想了解產(chǎn)品信息"}'

問答接口測(cè)試：

curl -X POST "http://localhost:8080/api/ai/qa" \
     -H "Content-Type: application/json" \
     -d '{"question": "如何退換貨？"}'

知識(shí)庫搜索測(cè)試：

curl "http://localhost:8080/api/ai/knowledge/search?keyword=退款政策"

關(guān)鍵特性說明

多種限流算法: 演示了令牌桶、預(yù)熱、排隊(duì)等待等不同算法的使用場景
熱點(diǎn)參數(shù)限流: 基于用戶ID進(jìn)行個(gè)性化限流
優(yōu)雅降級(jí): 提供友好的限流提示而非直接拒絕
異步處理: 對(duì)于被限流的復(fù)雜請(qǐng)求提供異步處理選項(xiàng)
監(jiān)控友好: 集成Sentinel Dashboard進(jìn)行實(shí)時(shí)監(jiān)控

這個(gè)案例展示了如何在實(shí)際的AI應(yīng)用中合理使用Sentinel的各種限流算法，既保護(hù)了系統(tǒng)穩(wěn)定性，也提供了良好的用戶體驗(yàn)。

到此這篇關(guān)于Spring AI框架結(jié)合Sentinel實(shí)現(xiàn)限流功能的文章就介紹到這了,更多相關(guān)Spring AI Sentinel限流內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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