Mistral AI チャット

ToolCallback weatherCallback = FunctionToolCallback.builder("getCurrentWeather", new WeatherService())
    .description("Get the weather in location")
    .inputType(WeatherService.Request.class)
    .build();

// Synchronous
String response = ChatClient.create(chatModel)
    .prompt()
    .user("What's the weather in Paris, Tokyo, and New York?")
    .tools(weatherCallback)
    .call()
    .content();

// Streaming
Flux<String> stream = ChatClient.create(chatModel)
    .prompt()
    .user("What's the weather in Paris, Tokyo, and New York?")
    .tools(weatherCallback)
    .stream()
    .content();

ToolCallingManager toolCallingManager = ToolCallingManager.builder().build();

ChatOptions options = ToolCallingChatOptions.builder()
    .toolCallbacks(ToolCallbacks.from(new WeatherService()))
    .build();

Prompt prompt = new Prompt("What's the weather in Paris, Tokyo, and New York?", options);
ChatResponse response = chatModel.call(prompt);

while (response.hasToolCalls()) {
    ToolExecutionResult result = toolCallingManager.executeToolCalls(prompt, response);
    prompt = new Prompt(result.conversationHistory(), options);
    response = chatModel.call(prompt);
}

record ActorsFilms(String actor, List<String> movies) {}

ActorsFilms actorsFilms = ChatClient.create(chatModel).prompt()
    .advisors(AdvisorParams.ENABLE_NATIVE_STRUCTURED_OUTPUT)
    .user("Generate the filmography of 5 movies for Tom Hanks.")
    .call()
    .entity(ActorsFilms.class);

record MovieRecommendation(String title, String director, int year, String plotSummary) {}

var options = MistralAiChatOptions.builder()
    .model(MistralAiApi.ChatModel.MISTRAL_SMALL.getValue())
    .responseFormat(ResponseFormat.jsonSchema(MovieRecommendation.class))
    .build();

ChatResponse response = chatModel.call(
    new Prompt("Recommend a classic science fiction movie.", options));

// JSON Mode - any valid JSON
var jsonMode = MistralAiChatOptions.builder()
    .responseFormat(ResponseFormat.jsonObject())
    .build();

// Structured Output - specific schema enforced
var structuredOutput = MistralAiChatOptions.builder()
    .responseFormat(ResponseFormat.jsonSchema(MyClass.class))
    .build();

var options = MistralAiChatOptions.builder()
    .model(MistralAiApi.ChatModel.MISTRAL_SMALL.getValue()) // Or any adjustable reasoning model
    .reasoningEffort(MistralAiApi.ChatCompletionRequest.ReasoningEffort.HIGH)
    .build();

ChatResponse response = chatModel.call(
    new Prompt("Solve this complex math problem...", options));

ChatResponse response = chatModel.call(new Prompt("Solve this complex math problem..."));

AssistantMessage message = response.getResult().getOutput();

// Access the thinking content from metadata
String thinking = (String) message.getMetadata().get(MistralAiChatModel.THINKING_CONTENT_METADATA);
if (thinking != null && !thinking.isEmpty()) {
    System.out.println("Model's thinking process:");
    System.out.println(thinking);
}

var imageResource = new ClassPathResource("/multimodal.test.png");

var userMessage = new UserMessage("Explain what do you see on this picture?",
        new Media(MimeTypeUtils.IMAGE_PNG, this.imageResource));

ChatResponse response = chatModel.call(new Prompt(this.userMessage,
        ChatOptions.builder().model(MistralAiApi.ChatModel.MISTRAL_LARGE.getValue()).build()));

var userMessage = new UserMessage("Explain what do you see on this picture?",
        new Media(MimeTypeUtils.IMAGE_PNG,
                URI.create("https://docs.spring.io/spring-ai/reference/_images/multimodal.test.png")));

ChatResponse response = chatModel.call(new Prompt(this.userMessage,
        ChatOptions.builder().model(MistralAiApi.ChatModel.MISTRAL_LARGE.getValue()).build()));

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.

MistralAiApi mistralAiApi = new MistralAiApi(System.getenv("MISTRAL_AI_API_KEY"));

ChatCompletionMessage chatCompletionMessage =
    new ChatCompletionMessage("Hello world", Role.USER);

// Sync request
ResponseEntity<ChatCompletion> response = this.mistralAiApi.chatCompletionEntity(
    new ChatCompletionRequest(List.of(this.chatCompletionMessage), MistralAiApi.ChatModel.MISTRAL_LARGE.getValue(), 0.8, false));

// Streaming request
Flux<ChatCompletionChunk> streamResponse = this.mistralAiApi.chatCompletionStream(
        new ChatCompletionRequest(List.of(this.chatCompletionMessage), MistralAiApi.ChatModel.MISTRAL_LARGE.getValue(), 0.8, true));

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

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

MistralOcrApi mistralAiApi = new MistralOcrApi(System.getenv("MISTRAL_AI_API_KEY"));

String documentUrl = "https://arxiv.org/pdf/2201.04234";
MistralOcrApi.OCRRequest request = new MistralOcrApi.OCRRequest(
        MistralOcrApi.OCRModel.MISTRAL_OCR_LATEST.getValue(), "test_id",
        new MistralOcrApi.OCRRequest.DocumentURLChunk(documentUrl), List.of(0, 1, 2), true, 5, 50);

ResponseEntity<MistralOcrApi.OCRResponse> response = mistralAiApi.ocr(request);