AI is no longer just a front-end feature; it is rapidly becoming a first-class person of back-end engineering. Whether you are creating intelligent APIs, automations, or context-based microservices, the ability to plug Large Language Models (LLMs) into Spring Boot can become a superpower. But thanks to Spring AI, it's not as difficult as it seems. In this tutorial, we'll show you how to integrate Google Gemini (one of the most developed multimodal models on the market today) into your Spring Boot application If you are a Java engineer, this tutorial will provide everything you need to get started in no time. Why Spring AI + Gemini Makes Sense Spring AI eliminates the need for any significant AI plumbing, leaving you with only: An HTTP client for your requests
The various endpoints of the Gemini models.
How to authenticate
How to marshal JSON responses back into usable response objects
How to manage exceptions. An HTTP client for your requests The various endpoints of the Gemini models. How to authenticate How to marshal JSON responses back into usable response objects How to manage exceptions. Instead, you get nice and clean Java abstractions that really are: ChatResponseModel res = chatClient.prompt("Explain Spring AI and its "). call(); 
//chaClient is the clinet provided by SpringAI to intrearct with ChatResponseModel res = chatClient.prompt("Explain Spring AI and its "). call(); 
//chaClient is the clinet provided by SpringAI to intrearct with Gemini offers: Multimodal understanding (text + images + audio)
Scaling reasoning capability
Low-latency inference response
Enterprise-ready APIs Multimodal understanding (text + images + audio) Scaling reasoning capability Low-latency inference response Enterprise-ready APIs GeMini+ Spring AI turns your simple Java into an AI-powered backend engine. AI-powered backend engine Now we look into the required coding changes for a simple Java AI project. <dependency>    
 <groupId>org.springframework.ai</groupId>     
<artifactId>spring-ai-gemini-spring-boot-starter</artifactId> 
</dependency>
# Spring config need
spring.ai.gemini.api-key= its YOUR_API_KEY
spring.ai.gemini.model=gemini-pro { what ever AI model you want to integrate you can add here }
#gemini-pro-vision if you need use vision <dependency>    
 <groupId>org.springframework.ai</groupId>     
<artifactId>spring-ai-gemini-spring-boot-starter</artifactId> 
</dependency>
# Spring config need
spring.ai.gemini.api-key= its YOUR_API_KEY
spring.ai.gemini.model=gemini-pro { what ever AI model you want to integrate you can add here }
#gemini-pro-vision if you need use vision Now it's is the time to build a simple chat API @RestController
@RequestMapping("/ai")
public class GeminiRestController {

    private final ChatClient chatClient;

    public GeminiRestController(ChatClient chatClient) {
        this.chatClient = chatClient;
    }

    @GetMapping("/explain")
    public String explain(@RequestParam String topic) {
        ChatResponse response = chatClient.prompt("Explain " + topic + " in simple terms.").call();
        return response.getResult().getOutputText();
    }
} @RestController
@RequestMapping("/ai")
public class GeminiRestController {

    private final ChatClient chatClient;

    public GeminiRestController(ChatClient chatClient) {
        this.chatClient = chatClient;
    }

    @GetMapping("/explain")
    public String explain(@RequestParam String topic) {
        ChatResponse response = chatClient.prompt("Explain " + topic + " in simple terms.").call();
        return response.getResult().getOutputText();
    }
} Do you need Multimodal (image)? Query Support @PostMapping(value = "/vision", consumes = MediaType.MULTIPART_FORM_DATA_VALUE)
public String vision(@RequestPart MultipartFile file) throws IOException {

    Prompt prompt = Prompt.builder()
            .text("Describe requiured  image in detail.")
            .media(file.getBytes(), file.getContentType())
            .build();

    ChatResponse response = chatClient.prompt(prompt).call();

    return response.getResult().getOutputText();
} @PostMapping(value = "/vision", consumes = MediaType.MULTIPART_FORM_DATA_VALUE)
public String vision(@RequestPart MultipartFile file) throws IOException {

    Prompt prompt = Prompt.builder()
            .text("Describe requiured  image in detail.")
            .media(file.getBytes(), file.getContentType())
            .build();

    ChatResponse response = chatClient.prompt(prompt).call();

    return response.getResult().getOutputText();
} What You Can Create With This What You Can Create With This AI-enhanced microservices
Domain-specific chatbots
Documentation-generating APIs
Anomaly detection with context
Multimodal interpretation in healthcare/automotive
Zero-Trust AI TrustOps automation AI-enhanced microservices Domain-specific chatbots Documentation-generating APIs Anomaly detection with context Multimodal interpretation in healthcare/automotive Zero-Trust AI TrustOps automation What you need to think about for production-ready What you need to think about for production-ready Prompt sanitization
Rate limits
Log masking
Retries and circuit breakers
Caching of common or repeated prompts Prompt sanitization Rate limits Log masking Retries and circuit breakers Caching of common or repeated prompts Adding Security to your App when integrating an AI tool into a Java application Adding Security to your App when integrating an AI tool into a Java application Sanitizing your prompt: public String sanitize(String input) {
    return input
            .replaceAll("(?i)delete|drop|shutdown|ignore previous|system:", "")
            .trim();
}

// now  use this public String sanitize(String input) {
    return input
            .replaceAll("(?i)delete|drop|shutdown|ignore previous|system:", "")
            .trim();
}

// now  use this String cleaned = sanitizeInput(userText);
chatClient.prompt(cleaned).call(); String cleaned = sanitizeInput(userText);
chatClient.prompt(cleaned).call(); Only users who are authenticated should be allowed to call/ai the endpoint. We will use Role-Based Access Control (RBAC) for this /ai @PreAuthorize("hasRole('AI_USER')")
@GetMapping("/explain")
public String explain(@RequestParam String topic) {
    
}


@EnableWebSecurity
public class SecurityConfig {

    @Bean
    public SecurityFilterChain securityFilterChain(HttpSecurity http) throws Exception {
        http
                .csrf().disable()
                .authorizeHttpRequests(auth -> auth
                        .requestMatchers("/ai/**").authenticated()
                        .anyRequest().permitAll()
                )
                .oauth2ResourceServer(OAuth2ResourceServerConfigurer::jwt);

        return http.build();
    }
} @PreAuthorize("hasRole('AI_USER')")
@GetMapping("/explain")
public String explain(@RequestParam String topic) {
    
}


@EnableWebSecurity
public class SecurityConfig {

    @Bean
    public SecurityFilterChain securityFilterChain(HttpSecurity http) throws Exception {
        http
                .csrf().disable()
                .authorizeHttpRequests(auth -> auth
                        .requestMatchers("/ai/**").authenticated()
                        .anyRequest().permitAll()
                )
                .oauth2ResourceServer(OAuth2ResourceServerConfigurer::jwt);

        return http.build();
    }
} Protect sensitive data logging. Masking: You never want to log raw prompts or responses. sensi Slf4j
@Service
public class SecureAIDataService {

    public String safeLog(String text) {
        return text.replaceAll("[0-9]{12,}", "****MASKED****");
    }

    public String callAi(String input) {

        log.info("AI Prompt: {}", safeLog(input));

        ChatResponse response = chatClient.prompt(input).call();

        String output = safeLog(response.getResult().getOutputText());

        log.info("AI Output: {}", output);

        return output;
    }
} Slf4j
@Service
public class SecureAIDataService {

    public String safeLog(String text) {
        return text.replaceAll("[0-9]{12,}", "****MASKED****");
    }

    public String callAi(String input) {

        log.info("AI Prompt: {}", safeLog(input));

        ChatResponse response = chatClient.prompt(input).call();

        String output = safeLog(response.getResult().getOutputText());

        log.info("AI Output: {}", output);

        return output;
    }
}

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

Spring AI Capabilities: Effortlessly Integrate Google Gemini with Your Spring Boot Application

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