Spring AI深度解析（11/50）：异常处理与容错机制实战

Spring AI学习2025-12-22 15:08

一、AI应用异常处理的特殊挑战

在Spring AI应用中，异常处理面临独特的复杂性，远超过传统Web应用：

多层异常来源：

  • 模型服务层：API限额、网络超时、认证失败

  • 数据处理层：输入格式错误、Token超限、内容过滤

  • 业务逻辑层：上下文过长、响应格式异常、逻辑冲突

  • 基础设施层：数据库连接、缓存失效、消息队列堆积

国内模型特有异常模式：

复制代码 
// 各厂商异常特征对比
public enum ProviderExceptionPattern {
    ALIYUN("频率限制、图片审核失败", "QPS超限、内容违规"),
    BYTE_DANCE("会话超时、参数校验", "session_timeout、invalid_param"), 
    DEEPSEEK("服务忙、认证失败", "service_busy、auth_failed"),
    ZHIPU("余额不足、模型不可用", "insufficient_balance、model_not_available")
}

二、Spring AI异常体系深度解析

2.1 核心异常类层次结构

复制代码 
// Spring AI异常基类体系
@RestControllerAdvice
public class AiExceptionHandler {
    
    // AI特定异常基类
    public abstract class AiException extends RuntimeException {
        private final String errorCode;
        private final String provider;
        private final Instant timestamp;
        
        public AiException(String message, String errorCode, String provider) {
            super(message);
            this.errorCode = errorCode;
            this.provider = provider;
            this.timestamp = Instant.now();
        }
    }
    
    // 模型服务异常
    public class ModelServiceException extends AiException {
        private final int statusCode;
        private final Map<String, Object> details;
        
        public ModelServiceException(String provider, int statusCode, 
                                   String message, Map<String, Object> details) {
            super(message, "MODEL_SERVICE_ERROR", provider);
            this.statusCode = statusCode;
            this.details = details != null ? details : Map.of();
        }
    }
    
    // 内容过滤异常
    public class ContentFilterException extends AiException {
        private final String filterType; // 敏感词、违法内容、偏见等
        private final String filteredContent;
        
        public ContentFilterException(String provider, String filterType, 
                                     String filteredContent) {
            super("内容审核未通过: " + filterType, "CONTENT_FILTERED", provider);
            this.filterType = filterType;
            this.filteredContent = filteredContent;
        }
    }
    
    // Token限制异常
    public class TokenLimitException extends AiException {
        private final int promptTokens;
        private final int maxTokens;
        private final String limitType; // 输入、输出、总长度
        
        public TokenLimitException(String provider, int promptTokens, 
                                 int maxTokens, String limitType) {
            super(String.format("Token限制超出: 输入%d tokens, 最大%d tokens", 
                promptTokens, maxTokens), "TOKEN_LIMIT_EXCEEDED", provider);
            this.promptTokens = promptTokens;
            this.maxTokens = maxTokens;
            this.limitType = limitType;
        }
    }
}

2.2 异常自动识别与分类

复制代码 
@Component
public class ExceptionClassifier {
    
    private final Map<String, Pattern> exceptionPatterns = Map.of(
        "RATE_LIMIT", Pattern.compile("rate.limit|quota.exceeded|请求过于频繁", Pattern.CASE_INSENSITIVE),
        "AUTH_FAILED", Pattern.compile("invalid.api.key|unauthorized|认证失败", Pattern.CASE_INSENSITIVE),
        "SERVICE_UNAVAILABLE", Pattern.compile("service.unavailable|internal.error|服务不可用", Pattern.CASE_INSENSITIVE),
        "CONTENT_FILTER", Pattern.compile("content.violation|sensitive.content|内容违规", Pattern.CASE_INSENSITIVE)
    );
    
    public AiException classifyException(String provider, Exception rawException) {
        String message = rawException.getMessage().toLowerCase();
        
        for (Map.Entry<String, Pattern> entry : exceptionPatterns.entrySet()) {
            if (entry.getValue().matcher(message).find()) {
                return createTypedException(entry.getKey(), provider, rawException);
            }
        }
        
        return new ModelServiceException(provider, 500, "未知错误", 
            Map.of("original_message", rawException.getMessage()));
    }
    
    private AiException createTypedException(String type, String provider, Exception cause) {
        switch (type) {
            case "RATE_LIMIT":
                return new RateLimitException(provider, extractRetryAfter(cause));
            case "AUTH_FAILED":
                return new AuthenticationException(provider, "API密钥无效或过期");
            case "CONTENT_FILTER":
                return new ContentFilterException(provider, "auto_detected", extractFilteredContent(cause));
            default:
                return new ModelServiceException(provider, 500, cause.getMessage(), null);
        }
    }
}

三、多层容错策略实战

3.1 重试机制与退避策略

复制代码 
@Configuration
@EnableRetry
public class RetryConfig {
    
    @Bean
    public RetryTemplate aiServiceRetryTemplate() {
        return RetryTemplate.builder()
            .maxAttempts(3)
            .exponentialBackoff(1000, 2, 5000) // 初始1s，倍数2，最大5s
            .retryOn(Exception.class)
            .notRetryOn(AuthenticationException.class) // 认证失败不重试
            .withListener(new AiRetryListener())
            .build();
    }
    
    // 智能重试监听器
    @Component
    public class AiRetryListener implements RetryListener {
        @Override
        public <T, E extends Throwable> boolean open(RetryContext context, 
                                                   RetryCallback<T, E> callback) {
            log.info("开始重试操作，重试上下文: {}", context);
            return true;
        }
        
        @Override
        public <T, E extends Throwable> void onError(RetryContext context,
                                                    RetryCallback<T, E> callback, 
                                                    Throwable throwable) {
            AiException aiException = exceptionClassifier.classifyException(
                getProviderFromContext(context), (Exception) throwable);
                
            // 根据异常类型调整重试策略
            if (aiException instanceof RateLimitException) {
                adjustBackoffForRateLimit(context, (RateLimitException) aiException);
            }
            
            log.warn("AI服务调用失败，正在进行第{}次重试", context.getRetryCount(), throwable);
        }
    }
}

// 注解式重试
@Service
public class ResilientAiService {
    
    @Retryable(value = {ModelServiceException.class, TimeoutException.class},
              maxAttempts = 3,
              backoff = @Backoff(delay = 1000, multiplier = 2))
    @Recover
    public String fallbackAiCall(String prompt, Exception e) {
        log.warn("重试失败，启用降级策略", e);
        return getCachedResponse(prompt).orElse("服务暂时不可用，请稍后重试");
    }
    
    public String callWithRetry(String prompt) {
        return aiServiceRetryTemplate.execute(context -> {
            // 根据重试次数选择备用模型
            String model = selectModelBasedOnRetryCount(context.getRetryCount());
            return chatClient.call(prompt, model);
        });
    }
}

3.2 熔断器与降级策略

复制代码 
@Component
public class CircuitBreakerManager {
    
    private final Map<String, CircuitBreaker> breakers = new ConcurrentHashMap<>();
    
    @PostConstruct
    public void initCircuitBreakers() {
        // 为每个模型服务创建独立的熔断器
        List<String> providers = Arrays.asList("aliyun", "byte-dance", "deepseek", "zhipu");
        providers.forEach(provider -> {
            CircuitBreakerConfig config = CircuitBreakerConfig.custom()
                .failureRateThreshold(50) // 失败率阈值50%
                .slidingWindowSize(10)   // 最近10次调用
                .minimumNumberOfCalls(5) // 最少5次调用才开始计算
                .waitDurationInOpenState(Duration.ofSeconds(30))
                .build();
                
            breakers.put(provider, CircuitBreaker.of(provider + "-cb", config));
        });
    }
    
    public String executeWithCircuitBreaker(String provider, Supplier<String> operation) {
        CircuitBreaker breaker = breakers.get(provider);
        
        return breaker.executeSupplier(() -> {
            try {
                return operation.get();
            } catch (Exception e) {
                // 记录失败指标
                metrics.recordFailure(provider, e);
                throw e;
            }
        });
    }
    
    // 熔断器状态监控
    @Scheduled(fixedRate = 10000)
    public void monitorCircuitBreakers() {
        breakers.forEach((provider, breaker) -> {
            CircuitBreaker.State state = breaker.getState();
            Metrics.gauge("circuit_breaker_state", 
                Tags.of("provider", provider), 
                state.ordinal());
                
            if (state == CircuitBreaker.State.OPEN) {
                alertService.sendAlert(provider + "服务熔断开启");
            }
        });
    }
}

四、优雅降级与备用方案

4.1 多级降级策略

复制代码 
@Service
public class GracefulDegradationService {
    
    // 降级策略枚举
    public enum DegradationLevel {
        NONE,           // 无降级
        MODEL_FALLBACK, // 模型降级（付费->免费）
        CACHE_ONLY,     // 仅缓存
        STATIC_RESPONSE // 静态响应
    }
    
    public String executeWithFallback(String prompt, DegradationLevel maxLevel) {
        for (DegradationLevel level : getDegradationSequence(maxLevel)) {
            try {
                switch (level) {
                    case NONE:
                        return callPrimaryModel(prompt);
                    case MODEL_FALLBACK:
                        return callFallbackModel(prompt);
                    case CACHE_ONLY:
                        return getCachedResponse(prompt)
                            .orElseThrow(() -> new FallbackException("缓存未命中"));
                    case STATIC_RESPONSE:
                        return getStaticResponse(prompt);
                }
            } catch (Exception e) {
                log.warn("降级级别 {} 执行失败", level, e);
                continue; // 尝试下一级降级
            }
        }
        return "系统繁忙，请稍后重试";
    }
    
    private List<DegradationLevel> getDegradationSequence(DegradationLevel maxLevel) {
        List<DegradationLevel> sequence = new ArrayList<>();
        for (DegradationLevel level : DegradationLevel.values()) {
            if (level.ordinal() <= maxLevel.ordinal()) {
                sequence.add(level);
            }
        }
        return sequence;
    }
    
    private String callFallbackModel(String prompt) {
        // 降级模型调用顺序
        List<String> fallbackSequence = Arrays.asList(
            "deepseek-chat",  // 成本最低
            "qwen-turbo",     // 速度最快  
            "ernie-speed"     // 最稳定
        );
        
        for (String model : fallbackSequence) {
            try {
                return chatClient.call(prompt, model);
            } catch (Exception e) {
                log.warn("降级模型 {} 调用失败", model, e);
            }
        }
        throw new FallbackException("所有降级模型均失败");
    }
}

4.2 响应缓存与兜底数据

复制代码 
@Component
@CacheConfig(cacheNames = "ai-responses")
public class ResponseCacheService {
    
    @Cacheable(key = "T(com.example.HashUtil).sha256(#prompt)", 
               unless = "#result == null")
    public Optional<String> getCachedResponse(String prompt) {
        // Redis或本地缓存查询
        return cacheStore.get(generateCacheKey(prompt));
    }
    
    @CachePut(key = "T(com.example.HashUtil).sha256(#prompt)")
    public void cacheResponse(String prompt, String response, Duration ttl) {
        cacheStore.put(generateCacheKey(prompt), response, ttl);
    }
    
    // 热门问题预缓存
    @Scheduled(fixedRate = 300000) // 5分钟更新一次
    public void warmUpCache() {
        List<String> hotQuestions = hotQuestionService.getHotQuestions(50);
        hotQuestions.parallelStream().forEach(question -> {
            if (!cacheStore.exists(generateCacheKey(question))) {
                try {
                    String response = callPrimaryModel(question);
                    cacheResponse(question, response, Duration.ofHours(1));
                } catch (Exception e) {
                    log.debug("预热缓存失败: {}", question, e);
                }
            }
        });
    }
}

五、监控、告警与自愈

5.1 异常监控体系

复制代码 
@Component
public class ExceptionMonitoring {
    
    @EventListener
    public void handleAiException(AiException event) {
        // 记录详细异常指标
        Counter.builder("ai.exceptions")
            .tag("provider", event.getProvider())
            .tag("error_code", event.getErrorCode())
            .tag("severity", event.getSeverity().name())
            .register(meterRegistry)
            .increment();
        
        // 异常模式检测
        detectExceptionPatterns(event);
        
        // 触发相应告警
        if (requiresImmediateAlert(event)) {
            alertService.sendImmediateAlert(event);
        }
    }
    
    private void detectExceptionPatterns(AiException exception) {
        // 异常频率检测
        if (isExceptionFrequencyHigh(exception)) {
            adjustCircuitBreaker(exception.getProvider());
        }
        
        // 关联异常检测
        if (isCorrelatedFailure(exception)) {
            escalateToEngineering(exception);
        }
    }
    
    @Scheduled(fixedRate = 60000) // 每分钟检查一次
    public void healthCheck() {
        providers.forEach(provider -> {
            try {
                Health health = performHealthCheck(provider);
                updateProviderHealthStatus(provider, health);
                
                if (health.getStatus() == Status.DOWN) {
                    triggerRecoveryProcedure(provider);
                }
            } catch (Exception e) {
                log.error("健康检查失败: {}", provider, e);
            }
        });
    }
}

5.2 智能告警与自愈

复制代码 
# alert-rules.yml
ai:
  alerts:
    - name: "high-error-rate"
      condition: "rate(ai_exceptions_total[5m]) > 10"
      severity: "warning"
      actions: ["scale-out", "switch-provider"]
      
    - name: "circuit-breaker-open" 
      condition: "circuit_breaker_state == 2" # OPEN state
      severity: "critical"
      actions: ["alert-engineering", "enable-fallback"]
      
    - name: "response-time-degradation"
      condition: "ai_response_time_seconds{quantile='0.95'} > 10"
      severity: "warning"
      actions: ["adjust-timeout", "enable-cache"]

六、测试策略与质量保障

6.1 异常测试覆盖

复制代码 
@SpringBootTest
class ExceptionHandlingTest {
    
    @Test
    void testRateLimitHandling() {
        // 模拟频率限制异常
        when(chatClient.call(anyString()))
            .thenThrow(new RateLimitException("aliyun", 60));
        
        String result = resilientAiService.callWithRetry("test prompt");
        
        assertThat(result).isEqualTo("服务繁忙，请稍后重试");
        verify(chatClient, times(3)).call(anyString()); // 验证重试次数
    }
    
    @Test
    void testCircuitBreakerTransition() {
        // 连续失败触发熔断
        for (int i = 0; i < 10; i++) {
            try {
                resilientAiService.callWithCircuitBreaker("aliyun", 
                    () -> { throw new RuntimeException("模拟失败"); });
            } catch (Exception ignored) {}
        }
        
        CircuitBreaker breaker = circuitBreakerManager.getBreaker("aliyun");
        assertThat(breaker.getState()).isEqualTo(CircuitBreaker.State.OPEN);
    }
}

总结

Spring AI应用中的异常处理与容错机制，主要包括：

  1. 分层异常体系：建立针对AI应用的特有异常分类和处理策略

  2. 智能重试机制：基于异常类型的自适应重试与退避策略

  3. 熔断降级方案：多级熔断器保护与优雅降级流程

  4. 全面监控告警：实时异常检测与自愈能力

  5. 质量保障体系：完整的异常场景测试覆盖

通过实施本文的异常处理方案，可以显著提升AI应用的稳定性和用户体验，确保在部分服务不可用时的业务连续性。

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