What is the Spring AI content moderation strategy and how do you implement it?

Spring AI does not ship a built-in content moderation system, but the framework provides the right extension points — primarily Advisors — to implement moderation as a pre- and post-processing step in the ChatClient pipeline. This keeps moderation logic reusable and decoupled from business code.

There are two moderation approaches:

1. Provider moderation API (e.g. OpenAI Moderation endpoint) — Call the moderation API before sending user input to the chat model. If flagged, throw an exception or return a safe fallback response without ever calling the LLM.

@Component
public class ModerationAdvisor implements RequestResponseAdvisor {

    private final OpenAiModerationModel moderationModel;

    @Override
    public AdvisedRequest adviseRequest(AdvisedRequest request, Map<String, Object> context) {
        String userInput = request.userText();
        ModerationResponse moderation = moderationModel.moderate(userInput);
        if (moderation.isFlagged()) {
            throw new ContentPolicyViolationException(
                "Input violated content policy: " + moderation.categories());
        }
        return request;
    }

    @Override
    public ChatClientResponse adviseResponse(ChatClientResponse response, Map<String, Object> context) {
        return response; // optionally scan the output too
    }
}

2. LLM-based self-moderation (SafeGuardAdvisor) — Spring AI's built-in SafeGuardAdvisor sends the user message to a second LLM prompt that evaluates safety, then blocks or passes the original request. This is more flexible (works with any provider) but adds an extra model call per request.

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