Step 04 - System messages

In step 1, we saw two types of messages:

• User messages (User)
• AI responses (Assistant)

There are other types of messages, and this step is about System message. It’s an important type of message. It provides the scope of the conversation and provides instructions to the LLM.

System messages

A system message in a LLM is a directive that helps guide the model’s behavior and tone during an interaction. It typically sets the context, role, or boundaries for the model, defining how it should respond to the user.

System messages are crucial for shaping the model’s output, ensuring it aligns with specific requirements such as formality, topic focus, or specific task execution. Unlike user input, the system message remains hidden from the conversation but influences the overall experience.

To add a system message, we need to enhance our CustomerSupportAgent interface. Update the CustomerSupportAgent interface content to become:

CustomerSupportAgent.java

package dev.langchain4j.quarkus.workshop;

import dev.langchain4j.service.SystemMessage;
import io.quarkiverse.langchain4j.RegisterAiService;
import io.smallrye.mutiny.Multi;
import jakarta.enterprise.context.SessionScoped;

@SessionScoped
@RegisterAiService
public interface CustomerSupportAgent {

    @SystemMessage("""
            You are a customer support agent of a car rental company 'Miles of Smiles'.
            You are friendly, polite and concise.
            If the question is unrelated to car rental, you should politely redirect the customer to the right department.
            """)
    Multi<String> chat(String userMessage);
}

If you do not follow the workshop, the step-04 directory already contains the updated CustomerSupportAgent interface.

As you can see, we added the @SystemMessage annotation to the chat method. This is how we add a system message to the LLM. We define the context, tone, and scope of the conversation.

System message and memory

Remember the conversation memory we talked about in step 1? We are sending all the messages exchanged between the user and the AI to the LLM, so the LLM can provide a context-aware response.

At some point, we may have too many messages and we need to evict some of them. In general, we remove the oldest message. However, we always keep the system message. We only remove the user and AI messages.

So, the LLM still understands the context and does not change its behavior radically because of the memory eviction.

Playing with the system message

Now, let’s test the system message. Make sure the application is running and open the browser at http://localhost:8080.

Let’s ask the LLM to tell us a story

Tell me a story

The AI should respond with a message that it is out of context. You can relatively easily work around this by asking for a car rental story, but there are other solution to this problem.

What’s important is to have a system message defining the scope of the conversation and the role of the AI. This will never be lost, even if the conversation is very long.

Alright, let’s now go a bit further and implement a RAG pattern! That’s the topic of the next step!