基岩 Anthropic 3

<dependency>
  <groupId>org.springframework.ai</groupId>
  <artifactId>spring-ai-bedrock-ai-spring-boot-starter</artifactId>
</dependency>

dependencies {
    implementation 'org.springframework.ai:spring-ai-bedrock-ai-spring-boot-starter'
}

ChatResponse response = chatModel.call(
    new Prompt(
        "Generate the names of 5 famous pirates.",
        Anthropic3ChatOptions.builder()
            .withTemperature(0.4)
        .build()
    ));

    byte[] imageData = new ClassPathResource("/test.png").getContentAsByteArray();

    var userMessage = new UserMessage("Explain what do you see o this picture?",
            List.of(new Media(MimeTypeUtils.IMAGE_PNG, this.imageData)));

    ChatResponse response = chatModel.call(new Prompt(List.of(this.userMessage)));

    assertThat(response.getResult().getOutput().getContent()).contains("bananas", "apple", "basket");

The image shows a close-up view of a wire fruit basket containing several pieces of fruit.
The basket appears to be made of thin metal wires formed into a round shape with an elevated handle.

Inside the basket, there are a few yellow bananas and a couple of red apples or possibly tomatoes.
The vibrant colors of the fruit contrast nicely against the metallic tones of the wire basket.

The shallow depth of field in the photograph puts the focus squarely on the fruit in the foreground, while the basket handle extending upwards is slightly blurred, creating a pleasing bokeh effect in the background.

The composition and lighting give the image a clean, minimalist aesthetic that highlights the natural beauty and freshness of the fruit displayed in this elegant wire basket.

spring.ai.bedrock.aws.region=eu-central-1
spring.ai.bedrock.aws.timeout=1000ms
spring.ai.bedrock.aws.access-key=${AWS_ACCESS_KEY_ID}
spring.ai.bedrock.aws.secret-key=${AWS_SECRET_ACCESS_KEY}

spring.ai.bedrock.anthropic3.chat.enabled=true
spring.ai.bedrock.anthropic3.chat.options.temperature=0.8
spring.ai.bedrock.anthropic3.chat.options.top-k=15

@RestController
public class ChatController {

    private final BedrockAnthropic3ChatModel chatModel;

    @Autowired
    public ChatController(BedrockAnthropic3ChatModel 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-bedrock</artifactId>
</dependency>

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

Anthropic3ChatBedrockApi anthropicApi =  new Anthropic3ChatBedrockApi(
    AnthropicChatBedrockApi.AnthropicModel.CLAUDE_V3_SONNET.id(),
    EnvironmentVariableCredentialsProvider.create(),
    Region.US_EAST_1.id(),
    new ObjectMapper(),
    Duration.ofMillis(1000L));

BedrockAnthropic3ChatModel chatModel = new BedrockAnthropic3ChatModel(this.anthropicApi,
    AnthropicChatOptions.builder()
        .withTemperature(0.6)
        .withTopK(10)
        .withTopP(0.8)
        .withMaxTokensToSample(100)
        .withAnthropicVersion(AnthropicChatBedrockApi.DEFAULT_ANTHROPIC_VERSION)
        .build());

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

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