Azure OpenAI

@Configuration
public class AzureOpenAiConfig {

	@Bean
	public AzureOpenAIClientBuilderCustomizer responseTimeoutCustomizer() {
		return openAiClientBuilder -> {
			HttpClientOptions clientOptions = new HttpClientOptions()
					.setResponseTimeout(Duration.ofMinutes(5));
			openAiClientBuilder.httpClient(HttpClient.createDefault(clientOptions));
		};
	}

}

ChatResponse response = chatModel.call(
    new Prompt(
        "Generate the names of 5 famous pirates.",
        AzureOpenAiChatOptions.builder()
            .deploymentName("gpt-4o")
            .temperature(0.4)
        .build()
    ));

URL url = new URL("https://docs.spring.io/spring-ai/reference/_images/multimodal.test.png");
String response = ChatClient.create(chatModel).prompt()
        .options(AzureOpenAiChatOptions.builder().deploymentName("gpt-4o").build())
        .user(u -> u.text("Explain what do you see on this picture?").media(MimeTypeUtils.IMAGE_PNG, this.url))
        .call()
        .content();

This is an image of a fruit bowl with a simple design. The bowl is made of metal with curved wire edges that
create an open structure, allowing the fruit to be visible from all angles. Inside the bowl, there are two
yellow bananas resting on top of what appears to be a red apple. The bananas are slightly overripe, as
indicated by the brown spots on their peels. The bowl has a metal ring at the top, likely to serve as a handle
for carrying. The bowl is placed on a flat surface with a neutral-colored background that provides a clear
view of the fruit inside.

Resource resource = new ClassPathResource("multimodality/multimodal.test.png");

String response = ChatClient.create(chatModel).prompt()
    .options(AzureOpenAiChatOptions.builder()
    .deploymentName("gpt-4o").build())
    .user(u -> u.text("Explain what do you see on this picture?")
    .media(MimeTypeUtils.IMAGE_PNG, this.resource))
    .call()
    .content();

spring.ai.azure.openai.api-key=YOUR_API_KEY
spring.ai.azure.openai.endpoint=YOUR_ENDPOINT
spring.ai.azure.openai.chat.options.deployment-name=gpt-4o
spring.ai.azure.openai.chat.options.temperature=0.7

@RestController
public class ChatController {

    private final AzureOpenAiChatModel chatModel;

    @Autowired
    public ChatController(AzureOpenAiChatModel chatModel) {
        this.chatModel = chatModel;
    }

    @GetMapping("/ai/generate")
    public Map generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        return Map.of("generation", this.chatModel.call(message));
    }

    @GetMapping("/ai/generateStream")
	public Flux<ChatResponse> generateStream(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        Prompt prompt = new Prompt(new UserMessage(message));
        return this.chatModel.stream(prompt);
    }
}

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-azure-openai</artifactId>
</dependency>

dependencies {
    implementation 'org.springframework.ai:spring-ai-azure-openai'
}

var openAIClientBuilder = new OpenAIClientBuilder()
  .credential(new AzureKeyCredential(System.getenv("AZURE_OPENAI_API_KEY")))
  .endpoint(System.getenv("AZURE_OPENAI_ENDPOINT"));

var openAIChatOptions = AzureOpenAiChatOptions.builder()
  .deploymentName("gpt-4o")
  .temperature(0.4)
  .maxTokens(200)
  .build();

var chatModel = AzureOpenAiChatModel.builder()
				.openAIClientBuilder(openAIClientBuilder)
				.defaultOptions(openAIChatOptions)
				.build();

ChatResponse response = chatModel.call(
  new Prompt("Generate the names of 5 famous pirates."));

// Or with streaming responses
Flux<ChatResponse> streamingResponses = chatModel.stream(
  new Prompt("Generate the names of 5 famous pirates."));