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Prerequisites

  • Java 21 or higher
  • (Optional) Phoenix API key if using Phoenix Cloud
  • (Optional) Docker or Podman if using the Arconia Phoenix Dev Service

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Add Dependencies

Add the dependencies to your build.gradle:
dependencies {
    implementation 'io.arconia:arconia-openinference-ai-semantic-conventions'
    implementation 'io.arconia:arconia-opentelemetry-spring-boot-starter'

    implementation 'org.springframework.boot:spring-boot-starter-web'
    implementation 'org.springframework.ai:spring-ai-starter-model-mistral-ai'

    testAndDevelopmentOnly 'io.arconia:arconia-dev-services-phoenix'
}

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Setup Phoenix

If you included the Arconia Phoenix Dev Service dependency as instructed in the previous step, your Spring Boot application will automatically provision a Phoenix service at startup time and connect to it. No extra code or configuration needed.The application logs will show you the URL where you can access the Phoenix AI observability platform in your development environment.
...Phoenix UI: http://localhost:<port>
By default, traces are exported via OTLP using the HTTP/Protobuf format.For more info on using Phoenix with Arconia, see Phoenix Dev Service.
If you are using Phoenix Cloud or a self-hosted Phoenix, adjust the endpoint in the code as needed via the arconia.otel.exporter.otlp.endpoint=${PHOENIX_COLLECTOR_ENDPOINT} and arconia.otel.exporter.otlp.headers=Authorization=Bearer ${PHOENIX_API_KEY} properties. Alternatively, you can use the canonical OpenTelemetry Environment Variables: OTEL_EXPORTER_OTLP_ENDPOINT and OTEL_EXPORTER_OTLP_HEADERS.

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Run Spring AI with Arconia

By instrumenting your application with Arconia, spans are automatically created whenever your AI models via Spring AI are invoked and sent to the Phoenix server for collection. Arconia plugs into Spring Boot and Spring AI without any code or configuration changes. 
package io.arconia.demo;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;

@SpringBootApplication
public class ArconiaTracingApplication {
    public static void main(String[] args) {
        SpringApplication.run(ArconiaTracingApplication.class, args);
    }
}

@RestController
class ChatController {

    private static final Logger logger = LoggerFactory.getLogger(ChatController.class);
    private final ChatClient chatClient;

    ChatController(ChatClient.Builder chatClientBuilder) {
        this.chatClient = chatClientBuilder.clone().build();
    }

    @GetMapping("/chat")
    String chat(String question) {
        logger.info("Received question: {}", question);
        return chatClient
                .prompt(question)
                .call()
                .content();
    }
}
Full example: https://github.com/arconia-io/arconia-examples/tree/main/arconia-openinference

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Observe

Once configured, your OpenInference traces will be automatically sent to Phoenix where you can:
  • Monitor Performance: Track latency, throughput, and error rates
  • Analyze Usage: View token usage, model performance, and cost metrics
  • Debug Issues: Trace request flows and identify bottlenecks
  • Evaluate Quality: Run evaluations on your LLM outputs

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Resources

Full Example

Arconia OpenInference Semantic Conventions (docs)