Build Intelligent Apps with Java (or Java Spring Boot) and DeepSeek R1

Introduction

In the era of AI-driven applications, integrating deep learning models into enterprise applications has become essential. DeepSeek R1, a powerful open-source LLM (Large Language Model), provides capabilities to enhance Java-based applications, including those built with Spring Boot. This article explores how to integrate DeepSeek R1 with Java Spring Boot to build an intelligent chatbot.

What is DeepSeek R1?

DeepSeek R1 is an advanced AI model designed for natural language processing tasks such as text generation, classification, and Q&A. It provides REST APIs, making it easy to integrate into various applications.

Prerequisites

To follow along, ensure you have:

• Java 17+
• Spring Boot 3+
• Maven
• OpenAI-compatible API access to DeepSeek R1

Setting Up a Spring Boot Project

Create a new Spring Boot project using Spring Initializr with dependencies:

• Spring Web (for REST API)
• Spring Boot Starter WebFlux (for asynchronous calls)
• Lombok (for reducing boilerplate code)

Refer to the provided GitHub repository for a sample project setup: https://github.com/Jayaprakashsuseelam/AIProjects/tree/main/deepseek.

Project Structure

Let's define a simple AI-powered chatbot in Java Spring Boot.

Step 1: Add Dependencies (pom.xml)

File: pom.xml

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-webflux</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.projectlombok</groupId>
        <artifactId>lombok</artifactId>
        <scope>provided</scope>
    </dependency>
</dependencies>        

Step 2: Create Configuration for DeepSeek API

File: src/main/java/com/openai/deepseek/config/DeepSeekConfig.java

package com.openai.deepseek.config;

import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Configuration;
import org.springframework.web.reactive.function.client.WebClient;
import org.springframework.http.HttpHeaders;

@Configuration
public class DeepSeekConfig {
    @Value("${deepseek.api.key}")
    private String apiKey;

    public WebClient getWebClient() {
        return WebClient.builder()
                .baseUrl("https://api.deepseek.com/v1")
                .defaultHeader(HttpHeaders.AUTHORIZATION, "Bearer " + apiKey)
                .build();
    }
}        

Step 3: Create a Service to Call DeepSeek R1 API

File: src/main/java/com/openai/deepseek/service/AIChatService.java

package com.openai.deepseek.service;

import lombok.RequiredArgsConstructor;
import org.springframework.stereotype.Service;
import org.springframework.web.reactive.function.client.WebClient;
import reactor.core.publisher.Mono;
import java.util.Map;

@Service
@RequiredArgsConstructor
public class AIChatService {
    private final WebClient webClient;
    
    public Mono<String> getChatResponse(String message) {
        return webClient.post()
                .uri("/chat/completions")
                .bodyValue(Map.of("prompt", message))
                .retrieve()
                .bodyToMono(String.class);
    }
}        

Step 4: Create a REST Controller

File: src/main/java/com/openai/deepseek/controller/ChatController.java

package com.openai.deepseek.controller;

import com.openai.deepseek.service.AIChatService;
import lombok.RequiredArgsConstructor;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.*;
import reactor.core.publisher.Mono;
import java.util.Map;

@RestController
@RequestMapping("/api/chat")
@RequiredArgsConstructor
public class ChatController {
    private final AIChatService aiChatService;

    @PostMapping
    public Mono<ResponseEntity<String>> chat(@RequestBody Map<String, String> request) {
        return aiChatService.getChatResponse(request.get("message"))
                .map(ResponseEntity::ok);
    }
}        

Running the Application

Set the API key in application.properties: File: src/main/resources/application.properties

deepseek.api.key=your_api_key_here        

Start the Spring Boot application:

mvn spring-boot:run        

Test the chatbot by sending a POST request to https://localhost:8080/api/chat with a JSON body:

{
    "message": "Hello, what is AI?"
}        

Sample Response from DeepSeek R1

{
    "response": "AI stands for Artificial Intelligence, which enables machines to learn from experience and perform tasks that typically require human intelligence."
}        

Enhancing the Application: Caching & Storing AI Responses

To optimize the chatbot, we can enhance the existing implementation by saving AI responses in a database (like PostgreSQL/MySQL) or a caching layer (like Redis) to avoid redundant DeepSeek R1 API calls. This helps in:

? Reducing API costs & response time

? Improving efficiency by reusing stored responses

? Handling frequently asked queries faster

Conclusion

This guide demonstrated how to integrate DeepSeek R1 into a Java Spring Boot application to build an intelligent chatbot. The same approach can be extended for text summarization, sentiment analysis, and more AI-powered use cases. With minimal effort, developers can leverage DeepSeek R1’s powerful capabilities to enhance their Java applications.

Next Steps

• Explore DeepSeek R1’s full API capabilities.
• Implement authentication and caching mechanisms.
• Extend the chatbot to support multiple user conversations.

Reference :

Deepseek Documentation

Spring Boot WebFlux

#DeepSeekR1

#JavaDevelopment

#SpringBoot

#AIIntegration

#MachineLearning

#ArtificialIntelligence

#LLMIntegration

#JavaSpringBoot

#ChatbotDevelopment

#WebFlux

#RESTAPI

#AIForDevelopers

#IntelligentApps

#NaturalLanguageProcessing

#TechInnovation