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Spring AI Observations:讓AI應用監(jiān)控變得簡單高效

人工智能
在AI應用爆發(fā)式增長的今天,Spring AI 1.0版本帶來了革命性的可觀測性功能。本文將深入探討如何利用Spring AI + Micrometer 構建企業(yè)級AI應用監(jiān)控體系,實現(xiàn)成本控制、性能優(yōu)化和全鏈路追蹤。

在AI應用爆發(fā)式增長的今天,Spring AI 1.0版本帶來了革命性的可觀測性功能。本文將深入探討如何利用Spring AI + Micrometer 構建企業(yè)級AI應用監(jiān)控體系,實現(xiàn)成本控制、性能優(yōu)化和全鏈路追蹤。

為什么Spring AI應用急需可觀測性?

AI服務成本失控的痛點

在企業(yè)級AI應用中,使用DeepSeek、OpenAI、Google Gemini或Azure OpenAI等服務時,成本控制是一個嚴峻挑戰(zhàn):

? Token消耗不透明:無法精確了解每次AI調用的成本

? 費用增長失控:大規(guī)模應用中,AI服務費用可能呈指數(shù)級增長

? 性能瓶頸難定位:AI調用鏈路復雜,問題排查困難

? 資源使用不合理:缺乏數(shù)據(jù)支撐的優(yōu)化決策

Spring AI可觀測性的價值

Spring AI的可觀測性功能為這些痛點提供了完美解決方案:

? ? 精準Token監(jiān)控:實時追蹤輸入/輸出Token消耗,精確到每次調用

? ? 智能成本控制:基于使用統(tǒng)計制定成本優(yōu)化策略

? ? 深度性能分析:識別AI調用瓶頸,優(yōu)化響應時間

? ? 完整鏈路追蹤:端到端記錄請求在Spring AI應用中的完整流轉

實戰(zhàn)演練:構建可觀測的Spring AI翻譯應用

第一步:Spring AI項目初始化

在start.spring.io[1]創(chuàng)建Spring Boot項目,集成Spring AI核心依賴:

Maven依賴配置(Spring AI BOM管理):

<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-bom</artifactId>
            <version>1.0.0</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

<dependencies>
    <!-- Spring AI DeepSeek 集成 -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-model-deepseek</artifactId>
    </dependency>
    
    <!-- Spring Boot Web -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    
    <!-- Spring Boot Actuator 監(jiān)控 -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-actuator</artifactId>
    </dependency>
</dependencies>

第二步:Spring AI客戶端配置

主應用類配置:

@SpringBootApplication
publicclassSpringAiTranslationApplication {
    
    publicstaticvoidmain(String[] args) {
        SpringApplication.run(SpringAiTranslationApplication.class, args);
    }
    
    @Bean
    public ChatClient chatClient(ChatClient.Builder builder) {
        return builder.build();
    }
}

Spring AI配置文件:

# Spring AI 可觀測性配置
management:
endpoints:
    web:
      exposure:
        include:"*"
endpoint:
    health:
      show-details:always
metrics:
    export:
      prometheus:
        enabled:true

spring:
threads:
    virtual:
      enabled:true
ai:
    deepseek:
      api-key:${DEEPSEEK_API_KEY}
      chat:
        options:
          model:deepseek-chat
          temperature: 0.8

環(huán)境變量設置:

export DEEPSEEK_API_KEY=your-deepseek-api-key

第三步:構建Spring AI翻譯服務

智能翻譯控制器:

@RestController
@RequestMapping("/api/v1")
@RequiredArgsConstructor
@Slf4j
publicclassSpringAiTranslationController {

    privatefinal ChatModel chatModel;

    @PostMapping("/translate")
    public TranslationResponse translate(@RequestBody TranslationRequest request) {
        log.info("Spring AI翻譯請求: {} -> {}", request.getSourceLanguage(), request.getTargetLanguage());
        
        Stringprompt= String.format(
                "作為專業(yè)翻譯助手,請將以下%s文本翻譯成%s,保持原文的語氣和風格:\n%s",
                request.getSourceLanguage(),
                request.getTargetLanguage(),
                request.getText()
        );

        StringtranslatedText= chatModel.call(prompt);
        
        return TranslationResponse.builder()
                .originalText(request.getText())
                .translatedText(translatedText)
                .sourceLanguage(request.getSourceLanguage())
                .targetLanguage(request.getTargetLanguage())
                .timestamp(System.currentTimeMillis())
                .build();
    }
}

@Data
@Builder
classTranslationRequest {
    private String text;
    private String sourceLanguage;
    private String targetLanguage;
}

@Data
@Builder
classTranslationResponse {
    private String originalText;
    private String translatedText;
    private String sourceLanguage;
    private String targetLanguage;
    private Long timestamp;
}

第四步:Spring AI翻譯API測試

curl -X POST http://localhost:8080/api/v1/translate \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Spring AI makes AI integration incredibly simple and powerful",
    "sourceLanguage": "英語",
    "targetLanguage": "中文"
}'

# 響應示例
{
"originalText": "Spring AI makes AI integration incredibly simple and powerful",
"translatedText": "Spring AI讓AI集成變得極其簡單而強大",
"sourceLanguage": "英語",
"targetLanguage": "中文",
"timestamp": 1704067200000
}

Spring AI監(jiān)控指標深度解析

核心指標1:Spring AI操作性能監(jiān)控

指標端點:/actuator/metrics/spring.ai.chat.client.operation

{
  "name":"spring.ai.chat.client.operation",
"description":"Spring AI ChatClient操作性能指標",
"baseUnit":"seconds",
"measurements":[
    {
      "statistic":"COUNT",
      "value":15
    },
    {
      "statistic":"TOTAL_TIME",
      "value":8.456780293
    },
    {
      "statistic":"MAX",
      "value":2.123904083
    }
],
"availableTags":[
    {
      "tag":"gen_ai.operation.name",
      "values":["framework"]
    },
    {
      "tag":"spring.ai.kind",
      "values":["chat_client"]
    }
]
}

業(yè)務價值:

? 監(jiān)控Spring AI翻譯服務調用頻次

? 分析Spring AI響應時間分布

? 識別Spring AI性能瓶頸

核心指標2:Spring AI Token使用量精準追蹤

指標端點:/actuator/metrics/gen_ai.client.token.usage

{
  "name":"gen_ai.client.token.usage",
"description":"Spring AI Token使用量統(tǒng)計",
"measurements":[
    {
      "statistic":"COUNT",
      "value":1250
    }
],
"availableTags":[
    {
      "tag":"gen_ai.response.model",
      "values":["deepseek-chat"]
    },
    {
      "tag":"gen_ai.request.model",
      "values":["deepseek-chat"]
    },
    {
      "tag":"gen_ai.token.type",
      "values":[
        "output",
        "input",
        "total"
      ]
    }
]
}

成本控制價值:

? 精確計算Spring AI服務成本

? 優(yōu)化Prompt設計降低Token消耗

? 制定基于使用量的預算策略

Spring AI調用鏈路追蹤實戰(zhàn)

第一步:集成Zipkin追蹤

添加Spring AI追蹤依賴:

<dependency>
  <groupId>io.micrometer</groupId>
  <artifactId>micrometer-tracing-bridge-brave</artifactId>
</dependency>
<dependency>
  <groupId>io.zipkin.reporter2</groupId>
  <artifactId>zipkin-reporter-brave</artifactId>
</dependency>

第二步:啟動Zipkin服務

docker run -d \
  --name zipkin-spring-ai \
  -p 9411:9411 \
  -e STORAGE_TYPE=mem \
  openzipkin/zipkin:latest

第三步:Spring AI追蹤配置

management:
  zipkin:
    tracing:
      endpoint: http://localhost:9411/api/v2/spans
  tracing:
    sampling:
      probability: 1.0

Spring AI鏈路追蹤效果展示

Zipkin界面展示Spring AI調用鏈路:

Spring AI調用鏈路總覽Spring AI調用鏈路總覽

Spring AI詳細調用時序:

Spring AI調用時序分析Spring AI調用時序分析

通過Zipkin可以清晰看到:

? Spring AI ChatClient的調用耗時

? DeepSeek API的響應時間

? 完整的Spring AI請求鏈路

Spring AI Observations源碼架構解析

Spring AI可觀測性核心流程:

Spring AI Observations架構圖Spring AI Observations架構圖

Spring AI的可觀測性基于以下核心組件:

1. ChatClientObservationConvention:定義Spring AI觀測約定

2. ChatClientObservationContext:Spring AI觀測上下文

3. MicrometerObservationRegistry:指標注冊中心

4. TracingObservationHandler:鏈路追蹤處理器

引用鏈接

[1] start.spring.io: https://start.spring.io

責任編輯:武曉燕 來源: JAVA架構日記
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