跟踪 Spring AI
MLflow 跟踪为 Spring AI（用于构建 AI 应用程序的 Spring 框架）提供了自动跟踪功能。MLflow 通过 OpenTelemetry 集成支持对 Spring AI 的跟踪。
步骤 1：创建一个 Spring AI 项目
使用 Spring Initializr 创建一个新的 Spring AI 项目，或将 Spring AI 添加到现有项目中
bash
spring init --dependencies=web --build=maven --java-version=17 spring-ai-demo
cd spring-ai-demo
将 OpenTelemetry 依赖项添加到您的 pom.xml
xml
<properties>
<java.version>17</java.version>
<spring-ai.version>1.0.0-M3</spring-ai.version>
<opentelemetry.version>1.45.0</opentelemetry.version>
</properties>
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-bom</artifactId>
<version>${spring-ai.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
<dependencies>
<!-- Spring Boot Actuator (required for observability) -->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<!-- Spring AI OpenAI -->
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>
<!-- Micrometer to OpenTelemetry Bridge -->
<dependency>
<groupId>io.micrometer</groupId>
<artifactId>micrometer-tracing-bridge-otel</artifactId>
</dependency>
<!-- OpenTelemetry OTLP Exporter -->
<dependency>
<groupId>io.opentelemetry</groupId>
<artifactId>opentelemetry-exporter-otlp</artifactId>
<version>${opentelemetry.version}</version>
</dependency>
</dependencies>
<repositories>
<repository>
<id>spring-milestones</id>
<name>Spring Milestones</name>
<url>https://repo.spring.io/milestone</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
</repositories>
将以下内容添加到 src/main/resources/application.properties
properties
spring.application.name=spring-ai-demo
# OpenAI Configuration
spring.ai.openai.api-key=${OPENAI_API_KEY}
spring.ai.openai.chat.options.model=gpt-4o-mini
# Enable Spring AI Observations
spring.ai.chat.observations.include-prompt=true
spring.ai.chat.observations.include-completion=true
# Tracing Configuration
management.tracing.sampling.probability=1.0
步骤 2：启动 MLflow Tracking Server
启动 MLflow 跟踪服务器
bash
mlflow server --port 5000
默认情况下，MLflow 使用 SQLite 作为后端存储。要使用其他类型的 SQL 数据库，如 PostgreSQL、MySQL 和 MSSQL，请按照后端存储文档中所述更改存储 URI。基于文件的后端存储不支持 OpenTelemetry 摄取。
步骤 3：配置 OpenTelemetry
创建 src/main/java/com/example/spring_ai_demo/config/MlflowTracingConfig.java
java
package com.example.spring_ai_demo.config;
import io.opentelemetry.api.OpenTelemetry;
import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.common.Attributes;
import io.opentelemetry.exporter.otlp.http.trace.OtlpHttpSpanExporter;
import io.opentelemetry.sdk.OpenTelemetrySdk;
import io.opentelemetry.sdk.resources.Resource;
import io.opentelemetry.sdk.trace.SdkTracerProvider;
import io.opentelemetry.sdk.trace.export.SimpleSpanProcessor;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
/**
* OpenTelemetry configuration for MLflow tracing.
*
* Configure via environment variables:
* OTEL_EXPORTER_OTLP_ENDPOINT=https://:5000/v1/traces
* OTEL_EXPORTER_OTLP_HEADERS=x-mlflow-experiment-id=0
*/
@Configuration
public class MlflowTracingConfig {
private static final Logger log = LoggerFactory.getLogger(MlflowTracingConfig.class);
@Value("${OTEL_EXPORTER_OTLP_ENDPOINT:https://:5000/v1/traces}")
private String otlpEndpoint;
@Value("${OTEL_EXPORTER_OTLP_HEADERS:x-mlflow-experiment-id=0}")
private String otlpHeaders;
@Value("${spring.application.name:spring-ai-demo}")
private String serviceName;
@Bean
public OpenTelemetry openTelemetry() {
log.info("Configuring MLflow tracing: endpoint={}", otlpEndpoint);
var exporterBuilder = OtlpHttpSpanExporter.builder()
.setEndpoint(otlpEndpoint);
// Parse headers from OTEL_EXPORTER_OTLP_HEADERS format (key=value,key2=value2)
if (otlpHeaders != null && !otlpHeaders.isEmpty()) {
for (String header : otlpHeaders.split(",")) {
String[] parts = header.split("=", 2);
if (parts.length == 2) {
exporterBuilder.addHeader(parts[0].trim(), parts[1].trim());
}
}
}
OtlpHttpSpanExporter exporter = exporterBuilder.build();
SdkTracerProvider tracerProvider = SdkTracerProvider.builder()
.addSpanProcessor(SimpleSpanProcessor.create(exporter))
.setResource(Resource.create(Attributes.of(
AttributeKey.stringKey("service.name"), serviceName)))
.build();
Runtime.getRuntime().addShutdownHook(new Thread(tracerProvider::close));
return OpenTelemetrySdk.builder()
.setTracerProvider(tracerProvider)
.build();
}
}
步骤 4：创建聊天服务
创建 src/main/java/com/example/spring_ai_demo/service/ChatService.java
java
package com.example.spring_ai_demo.service;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.stereotype.Service;
@Service
public class ChatService {
private final ChatModel chatModel;
public ChatService(ChatModel chatModel) {
this.chatModel = chatModel;
}
public String chat(String userMessage) {
return ChatClient.create(chatModel)
.prompt()
.user(userMessage)
.call()
.content();
}
}
步骤 5：创建一个聊天控制器
创建 src/main/java/com/example/spring_ai_demo/controller/ChatController.java
java
package com.example.spring_ai_demo.controller;
import com.example.spring_ai_demo.service.ChatService;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.*;
import java.util.Map;
@RestController
@RequestMapping("/api/chat")
public class ChatController {
private final ChatService chatService;
public ChatController(ChatService chatService) {
this.chatService = chatService;
}
@PostMapping("/simple")
public ResponseEntity<Map<String, String>> simpleChat(@RequestBody Map<String, String> request) {
String prompt = request.get("prompt");
if (prompt == null || prompt.isBlank()) {
return ResponseEntity.badRequest()
.body(Map.of("error", "Prompt is required"));
}
String response = chatService.chat(prompt);
return ResponseEntity.ok(Map.of(
"prompt", prompt,
"response", response
));
}
}
步骤 6：运行应用程序
设置环境变量并运行您的 Spring Boot 应用程序
bash
export OPENAI_API_KEY=your-openai-api-key
export OTEL_EXPORTER_OTLP_ENDPOINT=https://:5000/v1/traces
export OTEL_EXPORTER_OTLP_HEADERS=x-mlflow-experiment-id=0
mvn spring-boot:run
进行聊天请求后，在 https://:5000 打开 MLflow UI，然后导航到实验以查看跟踪。