comet-ml--opik/apps/opik-documentation/documentation/fern/docs-v2/integrations/spring-ai.mdx

---
description: Start here to integrate Opik into your Spring AI-based genai application
  for end-to-end LLM observability, unit testing, and optimization.
headline: Spring AI | Opik Documentation
og:description: Build intelligent applications with Spring AI, leveraging AI models
  and features seamlessly integrated into the Spring ecosystem.
og:site_name: Opik Documentation
og:title: Simplify AI Integration in Spring - Opik
title: Observability for Spring AI (Java) with Opik
---

Spring AI is a framework designed to simplify the integration of AI and machine learning capabilities into Spring applications. It provides a familiar Spring-based programming model for working with AI models, vector stores, and AI-powered features, making it easier to build intelligent applications within the Spring ecosystem.

Spring AI's primary advantage is its seamless integration with the Spring framework, allowing developers to leverage Spring's dependency injection, configuration management, and testing capabilities while building AI-powered applications.

## Getting started

To use the Spring AI integration with Opik, you will need to have Spring AI and the required OpenTelemetry packages installed.
The easiest way to start is to use the [OPIK SpringAI starter](https://github.com/comet-ml/opik-springai-demo) project.

### Prerequisites

Before running the demo application, ensure you have the following installed:

- **Java 21** or higher
- **Maven 3.6+** for dependency management and building
- **OpenAI API Key** (only for Opik Cloud) - Sign up at [OpenAI Platform](https://platform.openai.com/)
- **OPIK API Key** - Sign up at [Comet OPIK](https://www.comet.com/opik)

### Installation

#### 1. Clone the Repository
```bash
git clone git@github.com:comet-ml/opik-springai-demo.git
cd opik-springai-demo
```

#### 2. Verify Java Installation
```bash
java --version
```
Ensure you have Java 21 or higher installed.

#### 3. Verify Maven Installation
```bash
mvn --version
```

#### 4. Install Dependencies
```bash
mvn clean install
```

## Environment configuration

The application requires the following environment variables to be set:

#### Required Variables

- **OPENAI_API_KEY**: Your OpenAI API key
- **OTEL_EXPORTER_OTLP_ENDPOINT**: OPIK OpenTelemetry endpoint
- **OTEL_EXPORTER_OTLP_HEADERS**: Authorization headers for OPIK

Configure your environment variables based on your Opik deployment:

<Tabs>
    <Tab value="Opik Cloud" title="Opik Cloud">
        If you are using Opik Cloud, you will need to set the following
        environment variables:
            
        ```bash wordWrap
        export OTEL_EXPORTER_OTLP_ENDPOINT=https://www.comet.com/opik/api/v1/private/otel
        export OTEL_EXPORTER_OTLP_HEADERS='Authorization=<your-api-key>,Comet-Workspace=default'
        ```

        <Tip>
            To log the traces to a specific project, you can add the
            `projectName` parameter to the `OTEL_EXPORTER_OTLP_HEADERS`
            environment variable:

            ```bash wordWrap
            export OTEL_EXPORTER_OTLP_HEADERS='Authorization=<your-api-key>,Comet-Workspace=default,projectName=<your-project-name>'
            ```

            You can also update the `Comet-Workspace` parameter to a different
            value if you would like to log the data to a different workspace.
        </Tip>
    </Tab>
    <Tab value="Enterprise deployment" title="Enterprise deployment">
        If you are using an Enterprise deployment of Opik, you will need to set the following
        environment variables:

        ```bash wordWrap
        export OTEL_EXPORTER_OTLP_ENDPOINT=https://<comet-deployment-url>/opik/api/v1/private/otel
        export OTEL_EXPORTER_OTLP_HEADERS='Authorization=<your-api-key>,Comet-Workspace=default'
        ```

        <Tip>
            To log the traces to a specific project, you can add the
            `projectName` parameter to the `OTEL_EXPORTER_OTLP_HEADERS`
            environment variable:

            ```bash wordWrap
            export OTEL_EXPORTER_OTLP_HEADERS='Authorization=<your-api-key>,Comet-Workspace=default,projectName=<your-project-name>'
            ```

            You can also update the `Comet-Workspace` parameter to a different
            value if you would like to log the data to a different workspace.
        </Tip>
    </Tab>
    <Tab value="Self-hosted instance" title="Self-hosted instance">

    If you are self-hosting Opik, you will need to set the following environment
    variables:

    ```bash
    export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:5173/api/v1/private/otel
    ```

    <Tip>
        To log the traces to a specific project, you can add the `projectName`
        parameter to the `OTEL_EXPORTER_OTLP_HEADERS` environment variable:

        ```bash
        export OTEL_EXPORTER_OTLP_HEADERS='projectName=<your-project-name>'
        ```

    </Tip>
    </Tab>

</Tabs>

## Using Opik with Spring AI

Set up OpenTelemetry instrumentation for Spring AI in your `application.yaml`:

```yaml
spring:
  application:
    name: spring-ai-opik-demo
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
      chat:
        options:
          model: gpt-4o
          temperature: 0.7
server:
  port: 8085

# Enable OpenTelemetry tracing
management:
  tracing:
    sampling:
      probability: 1.0 # Sample all traces
  opentelemetry:
    tracing:
      export:
        otlp:
          endpoint: ${OTEL_EXPORTER_OTLP_ENDPOINT}
          headers: ${OTEL_EXPORTER_OTLP_HEADERS}

# Disable metrics and logs exporters via OpenTelemetry to avoid putting an extra load on the OpenTelemetry collector
otel:
  metrics:
    exporter: none
  logs:
    exporter: none
```

Your Spring AI code will now automatically send traces to Opik:

```java
import io.micrometer.tracing.Span;
import io.micrometer.tracing.Tracer;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.lang.NonNull;
import org.springframework.stereotype.Service;
import org.springframework.util.CollectionUtils;

import java.util.List;
import java.util.Map;
import java.util.Objects;

/**
 * Service class responsible for handling chat-related operations.
 * Provides functionality to interact with an underlying LLM chat client.
 */
@Service
public class ChatService {

    private static final String TAGS_KEY = "opik.tags";
    private static final String METADATA_PREFIX = "opik.metadata.";

    private final Tracer tracer;
    private final ChatClient chatClient;

    public ChatService(ChatClient.Builder chatClientBuilder, Tracer tracer) {
        this.chatClient = chatClientBuilder.build();
        this.tracer = tracer;
    }

    public String askQuestion(@NonNull String question) {
        return chatClient
                .prompt(new Prompt(question))
                .call()
                .content();
    }

    public String askQuestion(@NonNull String question, List<String> tags, Map<String, String> metadata) {

        Span span = tracer.currentSpan();
        if (Objects.nonNull(span)) {
            setTags(span, tags);
            setMetadata(span, metadata);
        }

        return chatClient
                .prompt(new Prompt(question))
                .call()
                .content();
    }

    private void setTags(@NonNull Span span, List<String> tags) {
        if (!CollectionUtils.isEmpty(tags)) {
            span.tagOfStrings(TAGS_KEY, tags);
        }
    }

    private void setMetadata(@NonNull Span span, Map<String, String> metadata) {
        if ( !CollectionUtils.isEmpty(metadata) ) {
            // populate metadata
            metadata.forEach((String k, String v) ->
                    span.tag(METADATA_PREFIX + k, v));
        }
    }
}
```

## Running the Demo Application

After cloning the [OPIK SpringAI starter](https://github.com/comet-ml/opik-springai-demo) repository,
you can run the demo application using one of the following methods:

### Method 1: Using Maven Spring Boot Plugin
```bash
export OTEL_EXPORTER_OTLP_HEADERS='Comet-Workspace=default,projectName=otel-springai-test' \
export OPENAI_API_KEY=sk-proj-your-api-key \
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:5173/api/v1/private/otel
mvn spring-boot:run
```

### Method 2: Using JAR File
```bash
mvn clean package

export OTEL_EXPORTER_OTLP_HEADERS='Comet-Workspace=default,projectName=otel-springai-test' \
export OPENAI_API_KEY=sk-proj-your-api-key \
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:5173/api/v1/private/otel
java -jar target/spring-ai-demo-opik-0.0.1-SNAPSHOT.jar
```

### Method 3: Development Mode with Auto-reload
```bash
export OTEL_EXPORTER_OTLP_HEADERS='Comet-Workspace=default,projectName=otel-springai-test' \
export OPENAI_API_KEY=sk-proj-your-api-key \
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:5173/api/v1/private/otel
mvn spring-boot:run -Dspring-boot.run.jvmArguments="-Dspring.devtools.restart.enabled=true"
```

The application will start on `http://localhost:8085`

## Testing the Demo Application using REST API

After that you can send a request to the application endpoints to interact with the chatbot:

```bash
curl --get --data-urlencode "question=How to integrate Spring AI with OpenAI for building chatbots?" http://localhost:8085/api/chat/ask-me
```

Or POST request to the `/api/chat/ask-enhanced` endpoint with TAGS and METADATA in the body:

```bash
curl -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "question": "What are the benefits of using Spring AI?",
    "tags": ["spring", "ai", "tutorial"],
    "metadata": {
      "userId": "user123",
      "sessionId": "session456",
      "category": "educational"
    }
  }' \
  http://localhost:8085/api/chat/ask-enhanced
```

After running the demo application, you can view the traces in Opik by navigating to the **Traces** tab in the **Projects** page.

![Opik Traces Page](/img/tracing/springai_opentelemetry_integration.png)

## Further improvements

If you have any questions or suggestions for improving the Spring AI integration, please [open an issue](https://github.com/comet-ml/opik/issues/new/choose) on our GitHub repository.