谷歌GenAI聊天

gcloud config set project <PROJECT_ID> &&
gcloud auth application-default login <ACCOUNT>

ChatResponse response = chatModel.call(
    new Prompt(
        "Solve this complex math problem step by step.",
        GoogleGenAiChatOptions.builder()
            .model("gemini-2.5-pro")
            .thinkingBudget(8192)
            .build()
    ));

// For Gemini 3 Pro: use thinkingLevel + includeThoughts
ChatResponse response = chatModel.call(
    new Prompt(
        "Analyze this complex scenario.",
        GoogleGenAiChatOptions.builder()
            .model("gemini-3-pro-preview")
            .thinkingLevel(GoogleGenAiThinkingLevel.HIGH)
            .includeThoughts(true)
            .build()
    ));

// For Gemini 2.5: use thinkingBudget + includeThoughts
ChatResponse response = chatModel.call(
    new Prompt(
        "Analyze this complex scenario.",
        GoogleGenAiChatOptions.builder()
            .model("gemini-2.5-pro")
            .thinkingBudget(8192)
            .includeThoughts(true)
            .build()
    ));

spring.ai.google.genai.chat.options.model=gemini-3-pro-preview
spring.ai.google.genai.chat.options.include-thoughts=true

ChatResponse response = chatModel.call(
    new Prompt(
        "Your question here",
        GoogleGenAiChatOptions.builder()
            .model("gemini-3-pro-preview")
            .includeThoughts(true)
            .toolCallbacks(callbacks)
            .build()
    ));

@Bean
@Description("Get the weather in a location")
public Function<WeatherRequest, WeatherResponse> weatherFunction() {
    return new WeatherService();
}

// Enable includeThoughts for Gemini 3 Pro with function calling
String response = ChatClient.create(this.chatModel)
        .prompt("What's the weather like in Boston?")
        .options(GoogleGenAiChatOptions.builder()
            .model("gemini-3-pro-preview")
            .includeThoughts(true)
            .build())
        .toolNames("weatherFunction")
        .call()
        .content();

byte[] data = new ClassPathResource("/vertex-test.png").getContentAsByteArray();

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

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

var pdfData = new ClassPathResource("/spring-ai-reference-overview.pdf");

var userMessage = UserMessage.builder()
			.text("You are a very professional document summarization specialist. Please summarize the given document.")
			.media(List.of(new Media(new MimeType("application", "pdf"), pdfData)))
			.build();

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

# Automatically cache prompts larger than 100,000 tokens
spring.ai.google.genai.chat.options.auto-cache-threshold=100000
# Set auto-cache TTL to 1 hour
spring.ai.google.genai.chat.options.auto-cache-ttl=PT1H

ChatResponse response = chatModel.call(
    new Prompt(
        largePrompt,
        GoogleGenAiChatOptions.builder()
            .autoCacheThreshold(100000)
            .autoCacheTtl(Duration.ofHours(1))
            .build()
    ));

GoogleGenAiCachedContent content = cachedContentService.get(cacheName);

// Check if cache is expired
boolean expired = content.isExpired();

// Get remaining TTL
Duration remaining = content.getRemainingTtl();

// Get usage metadata
CachedContentUsageMetadata metadata = content.getUsageMetadata();
if (metadata != null) {
    System.out.println("Total tokens: " + metadata.totalTokenCount().orElse(0));
}

# Enable cached content service (enabled by default)
spring.ai.google.genai.chat.enable-cached-content=true

# Use a specific cached content
spring.ai.google.genai.chat.options.use-cached-content=true
spring.ai.google.genai.chat.options.cached-content-name=cachedContent/my-cache-123

# Auto-caching configuration
spring.ai.google.genai.chat.options.auto-cache-threshold=50000
spring.ai.google.genai.chat.options.auto-cache-ttl=PT30M

spring.ai.google.genai.api-key=YOUR_API_KEY
spring.ai.google.genai.chat.options.model=gemini-2.0-flash
spring.ai.google.genai.chat.options.temperature=0.5

spring.ai.google.genai.project-id=PROJECT_ID
spring.ai.google.genai.location=LOCATION
spring.ai.google.genai.chat.options.model=gemini-2.0-flash
spring.ai.google.genai.chat.options.temperature=0.5

@RestController
public class ChatController {

    private final GoogleGenAiChatModel chatModel;

    @Autowired
    public ChatController(GoogleGenAiChatModel 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);
    }
}

Client genAiClient = Client.builder()
    .apiKey(System.getenv("GOOGLE_API_KEY"))
    .build();

var chatModel = new GoogleGenAiChatModel(genAiClient,
    GoogleGenAiChatOptions.builder()
        .model(ChatModel.GEMINI_2_0_FLASH)
        .temperature(0.4)
    .build());

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

Client genAiClient = Client.builder()
    .project(System.getenv("GOOGLE_CLOUD_PROJECT"))
    .location(System.getenv("GOOGLE_CLOUD_LOCATION"))
    .vertexAI(true)
    .build();

var chatModel = new GoogleGenAiChatModel(genAiClient,
    GoogleGenAiChatOptions.builder()
        .model(ChatModel.GEMINI_2_0_FLASH)
        .temperature(0.4)
    .build());

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