feat(ai): 集成 Spring AI 并配置 DeepSeek 模型支持

- 在 pom.xml 中添加 spring-ai 版本管理及依赖
- 在 application-dev.yml 中配置 DeepSeek AI 相关参数
- 新增 AI 模型测试用例，支持同步、流式及自定义模型选项调用
- 引入 spring-ai-ollama 和 spring-ai-starter-model-deepseek依赖以支持多种模型接入

pom.xml

@@ -35,6 +35,7 @@
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
         <maven.compiler.source>17</maven.compiler.source>
         <maven.compiler.target>17</maven.compiler.target>
+        <spring-ai.version>1.0.0</spring-ai.version>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
     </properties>
 
@@ -53,6 +54,13 @@
 
     <dependencyManagement>
         <dependencies>
+            <dependency>
+                <groupId>org.springframework.ai</groupId>
+                <artifactId>spring-ai-bom</artifactId>
+                <version>${spring-ai.version}</version>
+                <type>pom</type>
+                <scope>import</scope>
+            </dependency>
             <dependency>
                 <groupId>org.mybatis.spring.boot</groupId>
                 <artifactId>mybatis-spring-boot-starter</artifactId>

visual-novel-server-app/pom.xml

@@ -12,6 +12,15 @@
     <packaging>jar</packaging>
 
     <dependencies>
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-ollama</artifactId>
+        </dependency>
+
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-starter-model-deepseek</artifactId>
+        </dependency>
         <dependency>
             <groupId>org.springframework.boot</groupId>
             <artifactId>spring-boot-starter-web</artifactId>

visual-novel-server-app/src/main/resources/application-dev.yml

@@ -30,6 +30,15 @@ spring:
     connection-test-query: SELECT 1
   type: com.zaxxer.hikari.HikariDataSource
 
+  ai:
+    deepseek:
+      api-key: your-api-key
+      base-url: https://api.deepseek.com # DeepSeek 的请求 URL, 可不填，默认值为 api.deepseek.com
+      chat:
+        options:
+          model: deepseek-reasoner # 使用深度思考模型
+          temperature: 0.8 # 温度值
+
 # MyBatis 配置【如需使用记得打开】
 #mybatis:
 #  mapper-locations: classpath:/mybatis/mapper/*.xml
@@ -39,4 +48,5 @@ spring:
 logging:
   level:
     root: info
-  config: classpath:logback-spring.xml
+  config: classpath:logback-spring.xml
+

visual-novel-server-app/src/test/java/com/touka/test/ApiTest.java

@@ -1,19 +1,81 @@
 package com.touka.test;
 
+import jakarta.annotation.Resource;
 import lombok.extern.slf4j.Slf4j;
 import org.junit.Test;
 import org.junit.runner.RunWith;
+import org.springframework.ai.chat.model.ChatResponse;
+
+import org.springframework.ai.chat.prompt.Prompt;
+import org.springframework.ai.chat.prompt.PromptTemplate;
+import org.springframework.ai.deepseek.DeepSeekChatModel;
+
+import org.springframework.ai.ollama.api.OllamaOptions;
+
 import org.springframework.boot.test.context.SpringBootTest;
 import org.springframework.test.context.junit4.SpringRunner;
 
+import reactor.core.publisher.Flux;
+
 @Slf4j
 @RunWith(SpringRunner.class)
 @SpringBootTest
 public class ApiTest {
 
+    @Resource
+    private DeepSeekChatModel chatModel;
+
+    /**
+     * 测试同步生成响应
+     */
     @Test
-    public void test() {
-        log.info("测试完成");
+    public void testGenerate() {
+        String message = "Tell me a joke";
+
+        // 方式1: 直接传入字符串
+        String response1 = chatModel.call(message);
+        System.out.println("Response1: " + response1);
+
+        // 方式2: 使用 Prompt
+        ChatResponse response2 = chatModel.call(new Prompt(message));
+        System.out.println("Response2: " + response2.getResult().getOutput().getText());
+    }
+
+    /**
+     * 测试流式生成响应
+     */
+    @Test
+    public void testGenerateStream() {
+        String message = "Tell me a joke";
+
+        // 使用 PromptTemplate 构建提示词
+        Prompt prompt = new PromptTemplate(message).create();
+
+        // 流式输出
+        Flux<ChatResponse> responseFlux = chatModel.stream(prompt);
+
+        // 订阅并打印流式响应
+        responseFlux.doOnNext(response -> {
+            System.out.println("Stream Response: " + response.getResult().getOutput().getText());
+        }).blockLast(); // 在测试中阻塞等待完成
+    }
+
+    /**
+     * 测试使用特定模型选项
+     */
+    @Test
+    public void testGenerateWithModelOptions() {
+        String message = "1+1=?";
+        String model = "deepseek-r1:1.5b";
+
+        ChatResponse response = chatModel.call(new Prompt(
+                message,
+                OllamaOptions.builder()
+                        .model(model)
+                        .build()
+        ));
+
+        System.out.println("Response with model options: " + response.getResult().getOutput().getText());
     }
 
 }