Google GenAI Chat

The Google GenAI API allows developers to build generative AI applications using Google’s Gemini models through either the Gemini Developer API or Vertex AI. The Google GenAI API supports multimodal prompts as input and outputs text or code. A multimodal model is capable of processing information from multiple modalities, including images, videos, and text. For example, you can send the model a photo of a plate of cookies and ask it to give you a recipe for those cookies.

Gemini is a family of generative AI models developed by Google DeepMind that is designed for multimodal use cases. The Gemini API gives you access to Gemini 2.0 Flash, Gemini 2.0 Flash-Lite, and Gemini Pro models.

This implementation provides two authentication modes:

  • Gemini Developer API: Use an API key for quick prototyping and development

  • Vertex AI: Use Google Cloud credentials for production deployments with enterprise features

Gemini API Reference

Prerequisites

Choose one of the following authentication methods:

Option 1: Gemini Developer API (API Key)

  • Obtain an API key from the Google AI Studio

  • Set the API key as an environment variable or in your application properties

Option 2: Vertex AI (Google Cloud)

  • Install the gcloud CLI, appropriate for your OS.

  • Authenticate by running the following command. Replace PROJECT_ID with your Google Cloud project ID and ACCOUNT with your Google Cloud username.

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

Auto-configuration

There has been a significant change in the Spring AI auto-configuration, starter modules' artifact names. Please refer to the upgrade notes for more information.

Spring AI provides Spring Boot auto-configuration for the Google GenAI Chat Client. To enable it add the following dependency to your project’s Maven pom.xml or Gradle build.gradle build files:

  • Maven

  • Gradle

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-google-genai</artifactId>
</dependency>
dependencies {
    implementation 'org.springframework.ai:spring-ai-starter-model-google-genai'
}
Refer to the Dependency Management section to add the Spring AI BOM to your build file.

Chat Properties

Enabling and disabling of the chat auto-configurations are now configured via top level properties with the prefix spring.ai.model.chat.

To enable, spring.ai.model.chat=google-genai (It is enabled by default)

To disable, spring.ai.model.chat=none (or any value which doesn’t match google-genai)

This change is done to allow configuration of multiple models.

Connection Properties

The prefix spring.ai.google.genai is used as the property prefix that lets you connect to Google GenAI.

Property Description Default

spring.ai.model.chat

Enable Chat Model client

google-genai

spring.ai.google.genai.api-key

API key for Gemini Developer API. When provided, the client uses the Gemini Developer API instead of Vertex AI.

-

spring.ai.google.genai.project-id

Google Cloud Platform project ID (required for Vertex AI mode)

-

spring.ai.google.genai.location

Google Cloud region (required for Vertex AI mode)

-

spring.ai.google.genai.credentials-uri

URI to Google Cloud credentials. When provided it is used to create a GoogleCredentials instance for authentication.

-

Chat Model Properties

The prefix spring.ai.google.genai.chat is the property prefix that lets you configure the chat model implementation for Google GenAI Chat.

Property Description Default

spring.ai.google.genai.chat.options.model

Supported Google GenAI Chat models to use include gemini-2.0-flash, gemini-2.0-flash-lite, gemini-pro, and gemini-1.5-flash.

gemini-2.0-flash

spring.ai.google.genai.chat.options.response-mime-type

Output response mimetype of the generated candidate text.

text/plain: (default) Text output or application/json: JSON response.

spring.ai.google.genai.chat.options.google-search-retrieval

Use Google search Grounding feature

true or false, default false.

spring.ai.google.genai.chat.options.temperature

Controls the randomness of the output. Values can range over [0.0,1.0], inclusive. A value closer to 1.0 will produce responses that are more varied, while a value closer to 0.0 will typically result in less surprising responses from the generative.

0.7

spring.ai.google.genai.chat.options.top-k

The maximum number of tokens to consider when sampling. The generative uses combined Top-k and nucleus sampling. Top-k sampling considers the set of topK most probable tokens.

-

spring.ai.google.genai.chat.options.top-p

The maximum cumulative probability of tokens to consider when sampling. The generative uses combined Top-k and nucleus sampling. Nucleus sampling considers the smallest set of tokens whose probability sum is at least topP.

-

spring.ai.google.genai.chat.options.candidate-count

The number of generated response messages to return. This value must be between [1, 8], inclusive. Defaults to 1.

1

spring.ai.google.genai.chat.options.max-output-tokens

The maximum number of tokens to generate.

-

spring.ai.google.genai.chat.options.frequency-penalty

Frequency penalties for reducing repetition.

-

spring.ai.google.genai.chat.options.presence-penalty

Presence penalties for reducing repetition.

-

spring.ai.google.genai.chat.options.thinking-budget

Thinking budget for the thinking process.

-

spring.ai.google.genai.chat.options.tool-names

List of tools, identified by their names, to enable for function calling in a single prompt request. Tools with those names must exist in the ToolCallback registry.

-

spring.ai.google.genai.chat.options.tool-callbacks

Tool Callbacks to register with the ChatModel.

-

spring.ai.google.genai.chat.options.internal-tool-execution-enabled

If true, the tool execution should be performed, otherwise the response from the model is returned back to the user. Default is null, but if it’s null, ToolCallingChatOptions.DEFAULT_TOOL_EXECUTION_ENABLED which is true will take into account

-

spring.ai.google.genai.chat.options.safety-settings

List of safety settings to control safety filters, as defined by Google GenAI Safety Settings. Each safety setting can have a method, threshold, and category.

-
All properties prefixed with spring.ai.google.genai.chat.options can be overridden at runtime by adding a request specific Runtime options to the Prompt call.

Runtime options

The GoogleGenAiChatOptions.java provides model configurations, such as the temperature, the topK, etc.

On start-up, the default options can be configured with the GoogleGenAiChatModel(client, options) constructor or the spring.ai.google.genai.chat.options.* properties.

At runtime, you can override the default options by adding new, request specific, options to the Prompt call. For example, to override the default temperature for a specific request:

ChatResponse response = chatModel.call(
    new Prompt(
        "Generate the names of 5 famous pirates.",
        GoogleGenAiChatOptions.builder()
            .temperature(0.4)
        .build()
    ));
In addition to the model specific GoogleGenAiChatOptions you can use a portable ChatOptions instance, created with the ChatOptions#builder().

Tool Calling

The Google GenAI model supports tool calling (function calling) capabilities, allowing models to use tools during conversations. Here’s an example of how to define and use @Tool-based tools:

public class WeatherService {

    @Tool(description = "Get the weather in location")
    public String weatherByLocation(@ToolParam(description= "City or state name") String location) {
        ...
    }
}

String response = ChatClient.create(this.chatModel)
        .prompt("What's the weather like in Boston?")
        .tools(new WeatherService())
        .call()
        .content();

You can use the java.util.function beans as tools as well:

@Bean
@Description("Get the weather in location. Return temperature in 36°F or 36°C format.")
public Function<Request, Response> weatherFunction() {
    return new MockWeatherService();
}

String response = ChatClient.create(this.chatModel)
        .prompt("What's the weather like in Boston?")
        .toolNames("weatherFunction")
        .inputType(Request.class)
        .call()
        .content();

Find more in Tools documentation.

Multimodal

Multimodality refers to a model’s ability to simultaneously understand and process information from various (input) sources, including text, pdf, images, audio, and other data formats.

Image, Audio, Video

Google’s Gemini AI models support this capability by comprehending and integrating text, code, audio, images, and video. For more details, refer to the blog post Introducing Gemini.

Spring AI’s Message interface supports multimodal AI models by introducing the Media type. This type contains data and information about media attachments in messages, using Spring’s org.springframework.util.MimeType and a java.lang.Object for the raw media data.

Below is a simple code example extracted from GoogleGenAiChatModelIT.java, demonstrating the combination of user text with an image.

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)));

PDF

Google GenAI provides support for PDF input types. Use the application/pdf media type to attach a PDF file to the message:

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)));

Sample Controller

Create a new Spring Boot project and add the spring-ai-starter-model-google-genai to your pom (or gradle) dependencies.

Add a application.properties file, under the src/main/resources directory, to enable and configure the Google GenAI chat model:

Using Gemini Developer API (API Key)

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

Using Vertex AI

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
Replace the project-id with your Google Cloud Project ID and location is Google Cloud Region like us-central1, europe-west1, etc…​

Each model has its own set of supported regions, you can find the list of supported regions in the model page.

This will create a GoogleGenAiChatModel implementation that you can inject into your class. Here is an example of a simple @Controller class that uses the chat model for text generations.

@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);
    }
}

Manual Configuration

The GoogleGenAiChatModel implements the ChatModel and uses the com.google.genai.Client to connect to the Google GenAI service.

Add the spring-ai-google-genai dependency to your project’s Maven pom.xml file:

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

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-google-genai'
}
Refer to the Dependency Management section to add the Spring AI BOM to your build file.

Next, create a GoogleGenAiChatModel and use it for text generations:

Using API Key

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."));

Using Vertex AI

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."));

The GoogleGenAiChatOptions provides the configuration information for the chat requests. The GoogleGenAiChatOptions.Builder is fluent options builder.

Migration from Vertex AI Gemini

If you’re currently using the Vertex AI Gemini implementation (spring-ai-vertex-ai-gemini), you can migrate to Google GenAI with minimal changes:

Key Differences

  1. SDK: Google GenAI uses the new com.google.genai.Client instead of com.google.cloud.vertexai.VertexAI

  2. Authentication: Supports both API key and Google Cloud credentials

  3. Package Names: Classes are in org.springframework.ai.google.genai instead of org.springframework.ai.vertexai.gemini

  4. Property Prefix: Uses spring.ai.google.genai instead of spring.ai.vertex.ai.gemini

When to Use Google GenAI vs Vertex AI Gemini

Use Google GenAI when: - You want quick prototyping with API keys - You need the latest Gemini features from the Developer API - You want flexibility to switch between API key and Vertex AI modes

Use Vertex AI Gemini when: - You have existing Vertex AI infrastructure - You need specific Vertex AI enterprise features - Your organization requires Google Cloud-only deployment

Low-level Java Client

The Google GenAI implementation is built on the new Google GenAI Java SDK, which provides a modern, streamlined API for accessing Gemini models.