How do you use Spring AI with Spring WebFlux for a reactive AI endpoint?

Spring AI integrates naturally with Spring WebFlux's reactive pipeline. Because LLM streaming returns a Flux<String> or Flux<ChatResponse>, you can return it directly from a WebFlux controller with zero blocking, delivering tokens to the browser as Server-Sent Events (SSE) as fast as the model produces them.

@RestController
@RequestMapping("/ai")
public class AiStreamController {

    private final ChatClient chatClient;

    public AiStreamController(ChatClient.Builder builder) {
        this.chatClient = builder.build();
    }

    @GetMapping(value = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<String> stream(@RequestParam String message) {
        return chatClient.prompt()
            .user(message)
            .stream()
            .content();
    }

    // For full metadata (finish reason, token counts per chunk)
    @GetMapping(value = "/stream/full", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<ChatResponse> streamFull(@RequestParam String message) {
        return chatClient.prompt()
            .user(message)
            .stream()
            .chatResponse();
    }
}

From the browser or curl, the client reads the event stream as tokens arrive. Backpressure is handled by Project Reactor — if the client cannot consume fast enough, the Flux signals backpressure upstream. For SSE with Spring MVC (not WebFlux), SseEmitter combined with Flux.subscribe() and a manual emitter thread achieves the same result, though WebFlux is cleaner.

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