Metadata plays a crucial role in modern AI workflows. It enriches messages with context. It improves downstream processing. It helps your applications deliver more accurate results. Spring AI includes a Message Metadata feature that simplifies metadata handling. This tutorial explains how to use it in a Spring Boot application with Java. You will learn how to attach metadata to user, system, and default messages. You will also see how metadata validation works. Finally, you will discover how to access metadata with the ChatClient API.
Introduction to Message Metadata in Spring AI
Developers often need to send more than plain text to an AI model. They want to include attributes, tags, flags, or contextual indicators. These extra details help the model understand intent. Spring AI introduces metadata support to solve this need. The framework lets you attach metadata to each message. This metadata can control behavior, routing, and processing. It can also help log or trace message flow. The ChatClient exposes this feature through a clean Java API.
Metadata becomes powerful when you design structured AI interactions. You can create chains, workflows, or agents. Each step can include metadata that explains its purpose. The system then processes messages more consistently. This tutorial walks through every feature you need to master.
Because you will use Spring Boot, integrating metadata becomes simple. The framework manages configuration. It also provides the necessary components through dependency injection. As a result, you can focus on implementation rather than infrastructure.
Now let’s explore each metadata feature in detail.
Setting Up the Project
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-model-openai</artifactId>
<version>1.1.0</version>
</dependency>
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-bom</artifactId>
<version>1.1.0</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>1. Adding Metadata to User Messages
User messages benefit from metadata when conversations need context. You should identify the message origin. You may tag messages with preferences or settings. Metadata allows this without altering the text body. Spring AI’s message builder makes this process very simple.
Below is a short example:
Code Example: User Message Metadata
ChatClient.CallResponseSpec response = chatClient.prompt()
.user(u -> u.text("Generate a project plan.")
.metadata("requestId", "REQ-4412")
.metadata("department", "Engineering")
.metadata("priority", "high"))
.advisors(advisor)
.call();In this example, the metadata attributes do several things. They track the request. They identify the department. They indicate urgency. These fields remain visible to downstream operations. This structure supports auditing. It also supports routing logic because your application can read metadata before processing.
Metadata helps you classify messages. It also allows A/B testing. You can tag test groups. The ChatClient preserves all metadata through the request chain. This consistency avoids confusion during debugging. It also makes application logic predictable.
You can include multiple metadata keys. Each key should follow a clear naming pattern. Many teams choose a prefix. Some choose camelCase. Others prefer snake_case. Spring AI accepts any style. However, a consistent style improves traceability.
Every metadata value must be serializable. Strings work in every case. Numbers and Booleans also work. Objects may require conversion. Many developers convert complex objects to JSON. This technique keeps metadata portable.
User messages may contain sensitive content. When you attach metadata, ensure privacy rules apply. Both Spring Boot and Spring AI can integrate with secure storage. You can also sanitize metadata before logging.
Now let’s move to system messages.
2. Adding Metadata to System Messages
System messages act as instructions. They guide the AI model. They shape the conversation. They tell the model how to behave. Metadata makes these instructions more powerful. You can specify role details, routing logic, or environment indicators.
Spring AI lets you attach system metadata just as you attach user metadata.
Code Example: System Message Metadata
ChatClient.CallResponseSpec response = chatClient.prompt()
.system(s -> s.text("You are a strict code reviewer.")
.metadata("role", "reviewer")
.metadata("confidenceLevel", "high"))
.user("""
public double calculateArea(double length, double width) {
// 1. Method Body: Contains the logic to be executed.
double area = length * width;
// 2. Return Statement: Sends the result back to the caller.
return area;
}
""")
.advisors(advisor)
.call();In this example, metadata enhances the system prompt. It identifies the role. It signals confidence. These details may help downstream interpreters. They also help logging systems filter messages.
Metadata helps teams separate configuration from content. For instance, you can include the model version. You can also include environment flags such as dev, qa, or prod. The AI model might not use these fields directly. However, your application may check them before forwarding messages. That flexibility supports large-scale systems.
System metadata becomes extremely useful in multi-agent workflows. One agent may include hints. Another may include routing data. Metadata offers a standardized structure. Because Spring AI uses builders, you can easily reuse metadata patterns.
The ChatClient preserves system metadata in the final request. This preservation allows advanced pipelines. For instance, you might send metadata to an analytics service. You might calculate performance metrics. You might track prompt efficiency.
Now, let’s explore the default metadata feature.
3. Default Metadata Support
Sometimes you want metadata added automatically. Spring AI supports default metadata. You can configure global metadata that applies to every message. This feature removes boilerplate code. It also ensures consistency across your application.
Default metadata applies to user messages and system messages. It also applies to assistant messages if you choose. Developers often store application version details. They also store session identifiers. They may also include global tags that label the environment.
Code Example: Default Metadata Configuration
@Bean
public ChatClient moderatedChatClient(ChatClient.Builder builder) {
return builder
// 1. Set a Default System Instruction for moderation
// This instruction acts as a primary safety layer for the LLM.
.defaultSystem(s -> s.text("You are a strictly professional and ethical chatbot. " +
"You must refuse politely to discuss politics, sensitive topics, or any controversial content. " +
"Always prioritize safety and factual accuracy in technical responses."))
// 2. Set Default User Context/Metadata
// This can be used to pass non-prompt data like the user's ID or role
// to the underlying ChatModel for logging, tracing, or personalized model behavior.
.defaultUser(u -> u.text("User Role: Standard Customer")
.metadata("userId", "UID-4567-CUST")
.metadata("accessLevel", "basic"))
// 3. Configure a conservative temperature for high-stakes, professional responses
.defaultOptions(ChatOptions.builder().temperature(0.2).build())
// 4. Build the client
.build();
}After you define this bean, every message receives the default fields. You can still add message-specific metadata. Spring AI merges default metadata with custom metadata. Custom metadata always takes priority if key names conflict.
Default metadata helps in distributed systems. When you process many requests per second, consistency matters. Default metadata creates a trail across all operations. Every request carries the same base fields. This structure makes debugging easier. It also helps analytics tools identify patterns.
However, do not overload default metadata. Too many fields create noise. Metadata should support clarity. Choose fields with purpose. Use global metadata only when every message truly requires it.
Now let’s examine metadata validation.
4. Metadata Validation
Webhook processors, middleware components, or downstream systems may expect specific metadata. Missing fields can break workflows. Wrong types can produce errors. Spring AI includes metadata validation to avoid these problems.
Validation ensures metadata keys follow naming rules. It also confirms compatibility. Many developers define metadata contracts. These contracts describe expected keys. They also describe expected types. Spring AI can validate metadata before sending a request.
Metadata validation triggers errors early. You avoid sending invalid messages. This confirmation helps maintain stability. It also ensures downstream components operate predictably.
Code Example: Metadata Validation
// This will throw an IllegalArgumentException
chatClient.prompt()
.user(u -> u.text("Hello World!")
.metadata(null, "someValue")) // Invalid: null key
.call()
.content();
// This will also throw an IllegalArgumentException
chatClient.prompt()
.user(u -> u.text("Hello World!")
.metadata("someKey", null)) // Invalid: null value
.call()
.content();Here, validation is enabled. The system checks metadata. It rejects unsupported types. It detects missing keys. Validation ensures metadata integrity before the message reaches the model.
You may define custom logic that extends validation. Many teams add custom validators. These validators check content. They also check the structure. You can register your validator through Spring configuration.
Although metadata validation protects your system, you must still consider performance. Excessive validation slows requests. Therefore, enable detailed validation during development. Use lighter validation in production.
Now, let’s learn how to access metadata after sending messages.
5. Accessing Metadata
After messages flow through your system, you may need metadata for logs or analytics. The ChatClient allows metadata extraction from both requests and responses. The API exposes structured objects. These objects include message content and metadata.
Code Example: Reading Metadata
response.chatResponse().getMetadata()
This code retrieves metadata from the response. You can process the values. You can store them. You can forward them to monitoring tools. Response metadata may also include model details. You may use them to measure performance. You can track token usage. You can track completion time. Every detail helps refine your system.
Metadata access supports advanced features. You can build chains that depend on metadata. You can route messages based on keys. You can implement throttling strategies. Metadata gives you the context needed for intelligent decision-making.
Spring AI exposes metadata in a simple map structure. You can convert it to JSON if needed. You can store it in a database. You can print it in logs. The framework keeps metadata lightweight and flexible.
6. Create MessageMetadataController for Testing Metadata Access
A dedicated controller allows you to test how metadata flows through your Spring Boot application. It also helps you verify that the ChatClient correctly receives and returns metadata. This controller exposes an HTTP endpoint. When you call it, the controller sends a message with metadata, receives the AI response, and returns all metadata for inspection.
Code Example: MessageMetadataController
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.ChatModelCallAdvisor;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;
import java.util.HashMap;
import java.util.Map;
@RestController
@RequestMapping("/api/metadata")
public class MessageMetadataController {
private final ChatClient chatClient;
private final ChatModel chatModel;
public MessageMetadataController(ChatClient.Builder chatClientBuilder, ChatModel chatMode) {
this.chatClient = chatClientBuilder.build();
this.chatModel = chatMode;
}
@GetMapping("/test")
public Map<String, Object> testMetadata() {
var advisor = ChatModelCallAdvisor.builder()
.chatModel(chatModel)
.build();
ChatClient.CallResponseSpec response = chatClient.prompt()
.user(u -> u.text("Generate a project plan.")
.metadata("requestId", "REQ-4412")
.metadata("department", "Engineering")
.metadata("priority", "high"))
.advisors(advisor)
.call();
Map<String, Object> result = new HashMap<>();
result.put("assistantMessage", response.chatResponse().getResults());
result.put("responseMetadata", response.chatResponse().getMetadata());
return result;
}
}This controller method performs three actions.
First, it sends a user message with custom metadata.
Second, it retrieves metadata from the AI response.
Third, it returns both the assistant message and all metadata in a JSON structure.
You can test it easily by calling:
GET /api/metadata/test
This endpoint helps verify how metadata behaves during the request–response cycle. It also confirms whether your application correctly passes and extracts metadata, making it a valuable tool during development and debugging.
Finally
Metadata expands the capabilities of AI-driven applications. Developers gain control. They gain structure. They gain clarity. This tutorial demonstrated several features. You learned how to add metadata to user messages. You explored system message metadata. You saw how to define default metadata. You discovered validation techniques. You also learned how to access metadata after a request completes.
Spring Boot integrates cleanly with Spring AI. Because of this integration, metadata handling becomes natural. Java developers gain a reliable, elegant API. They can build complex workflows without friction.
When you master metadata, you unlock new design patterns. You can build agent systems. You can orchestrate workflows. You can connect AI models with business logic. Metadata provides the glue between these layers.
As you build real applications, continue refining your metadata strategy. Focus on clarity. Keep metadata compact. Validate fields carefully. Use naming conventions that age well. These principles help maintain long-term stability.
This article was originally published on Medium.
