使用spring-ai-alibaba重构项目

pom.xml

@@ -13,6 +13,7 @@
 	<version>0.0.1-SNAPSHOT</version>
 	<name>AI-Interview</name>
 	<description>AI-Interview</description>
+	<!-- TODO: 考虑删除空的元数据元素 -->
 	<url/>
 	<licenses>
 		<license/>
@@ -56,6 +57,7 @@
 			<artifactId>spring-boot-starter-webflux</artifactId> <!-- 用于 WebClient -->
 		</dependency>
 
+		<!-- TODO: 考虑删除已注释的依赖 -->
 <!--		<dependency>-->
 <!--			<groupId>org.springframework.ai</groupId>-->
 <!--			<artifactId>spring-ai-openai-spring-boot-starter</artifactId>-->
@@ -72,6 +74,7 @@
 			<artifactId>spring-ai-alibaba-starter-dashscope</artifactId>
 		</dependency>
 
+		<!-- TODO: 检查内存相关依赖是否都需要 -->
 		<dependency>
 			<groupId>com.alibaba.cloud.ai</groupId>
 			<artifactId>spring-ai-alibaba-starter-memory</artifactId>
@@ -100,7 +103,7 @@
 			<artifactId>spring-boot-starter-data-redis</artifactId>
 		</dependency>
 
-
+		<!-- TODO: 考虑删除已注释的依赖 -->
 <!--		<dependency>-->
 <!--			<groupId>com.alibaba</groupId>-->
 <!--			<artifactId>dashscope-sdk-java</artifactId>-->
@@ -198,11 +201,13 @@
 	<build>
 		<finalName>ai-interview</finalName>
 		<plugins>
+			<!-- TODO: 检查Spring Boot插件版本是否与父POM一致 -->
 			<plugin>
 				<groupId>org.springframework.boot</groupId>
 				<artifactId>spring-boot-maven-plugin</artifactId>
 				<version>3.5.0</version>
 			</plugin>
+			<!-- TODO: 考虑在正式环境中启用测试 -->
 			<!-- maven 打包时跳过测试 -->
 			<plugin>
 				<groupId>org.apache.maven.plugins</groupId>

src/main/java/com/qingqiu/interview/annotation/AiChatLog.java

@@ -1,15 +0,0 @@
-package com.qingqiu.interview.annotation;
-
-import java.lang.annotation.*;
-
-/**
- * <h1></h1>
- *
- * @author qingqiu
- * @date 2025/9/18 12:58
- */
-@Documented
-@Target(ElementType.METHOD)
-@Retention(RetentionPolicy.RUNTIME)
-public @interface AiChatLog {
-}

src/main/java/com/qingqiu/interview/aspect/AiChatLogAspect.java

@@ -1,71 +0,0 @@
-package com.qingqiu.interview.aspect;
-
-import com.qingqiu.interview.dto.ChatDTO;
-import com.qingqiu.interview.entity.AiSessionLog;
-import com.qingqiu.interview.service.IAiSessionLogService;
-import com.qingqiu.interview.vo.ChatVO;
-import jakarta.annotation.Resource;
-import org.apache.commons.lang3.StringUtils;
-import org.aspectj.lang.ProceedingJoinPoint;
-import org.aspectj.lang.annotation.Around;
-import org.aspectj.lang.annotation.Aspect;
-import org.aspectj.lang.annotation.Pointcut;
-import org.springframework.stereotype.Component;
-import org.springframework.transaction.annotation.Transactional;
-
-/**
- * <h1>
- * ai聊天的切面
- * </h1>
- *
- * @author qingqiu
- * @date 2025/9/18 13:00
- */
-@Aspect
-@Component
-public class AiChatLogAspect {
-
-    @Resource
-    private IAiSessionLogService aiSessionLogService;
-
-    public AiChatLogAspect() {
-
-    }
-
-    @Pointcut("@annotation(com.qingqiu.interview.annotation.AiChatLog)")
-    public void logPointCut() {
-    }
-
-    @Around("logPointCut()")
-    @Transactional(rollbackFor = Exception.class)
-    public Object around(ProceedingJoinPoint point) throws Throwable {
-
-        Object[] args = point.getArgs();
-        ChatDTO arg = (ChatDTO) args[0];
-        if (StringUtils.isNoneBlank(arg.getSessionId())) {
-            AiSessionLog userSessionLog = new AiSessionLog();
-            userSessionLog
-                    .setRole(arg.getRole())
-                    .setDataType(arg.getDataType())
-                    .setContent(arg.getContent())
-                    .setToken(arg.getSessionId())
-            ;
-            aiSessionLogService.save(userSessionLog);
-        }
-
-
-        Object result = point.proceed();
-
-        ChatVO chatVO = (ChatVO) result;
-        if (StringUtils.isNotBlank(chatVO.getSessionId())) {
-            AiSessionLog aiSessionLog = new AiSessionLog();
-            aiSessionLog
-                    .setRole(chatVO.getRole())
-                    .setContent(chatVO.getContent())
-                    .setToken(chatVO.getSessionId())
-            ;
-            aiSessionLogService.save(aiSessionLog);
-        }
-        return result;
-    }
-}

src/main/java/com/qingqiu/interview/common/utils/SpringApplicationContextUtils.java

@@ -6,12 +6,12 @@ import org.springframework.context.ApplicationContextAware;
 import org.springframework.stereotype.Component;
 
 @Component
-public class SpringApplicationContextUtil implements ApplicationContextAware {
+public class SpringApplicationContextUtils implements ApplicationContextAware {
     private static ApplicationContext applicationContext;
 
     @Override
     public void setApplicationContext(ApplicationContext applicationContext) throws BeansException {
-        SpringApplicationContextUtil.applicationContext = applicationContext;
+        SpringApplicationContextUtils.applicationContext = applicationContext;
     }
 
     public static <T> T getBean(Class<T> beanClass) {

src/main/java/com/qingqiu/interview/common/utils/TreeUtils.java

@@ -7,7 +7,7 @@ import java.util.Map;
 import java.util.function.Function;
 import java.util.stream.Collectors;
 
-public class TreeUtil {
+public class TreeUtils {
 
     /**
      * 通用树形结构构建方法

src/main/java/com/qingqiu/interview/common/utils/WebClientUtils.java

@@ -0,0 +1,46 @@
+package com.qingqiu.interview.common.utils;
+
+
+import io.netty.channel.ChannelOption;
+import org.springframework.http.client.ReactorClientHttpRequestFactory;
+import org.springframework.http.client.reactive.ReactorClientHttpConnector;
+import org.springframework.web.client.RestClient;
+import org.springframework.web.reactive.function.client.WebClient;
+import reactor.netty.http.client.HttpClient;
+
+import java.time.Duration;
+
+/**
+ * @program: ai-interview
+ * @description:
+ * @author: huangpeng
+ * @create: 2025-11-06 20:04
+ **/
+public class WebClientUtils {
+
+    /**
+     * 创建全局默认的WebClient Bean实例
+     * 配置了连接超时和响应超时时间
+     *
+     * @return WebClient实例
+     */
+    public static WebClient.Builder getWebClientBuilder() {
+        HttpClient httpClient = HttpClient.create()
+                .option(ChannelOption.CONNECT_TIMEOUT_MILLIS, 30000) // 连接超时
+                .responseTimeout(Duration.ofMillis(30000)); // 读取超时
+
+        return WebClient.builder()
+                .clientConnector(new ReactorClientHttpConnector(httpClient))
+                ;
+    }
+
+    public static RestClient.Builder getRestClientBuilder() {
+        return RestClient.builder()
+                .requestFactory(
+                        new ReactorClientHttpRequestFactory(
+                                HttpClient.create()
+                                        .responseTimeout(Duration.ofMinutes(30))
+                        )
+                );
+    }
+}

src/main/java/com/qingqiu/interview/config/DashScopeConfig.java

@@ -5,28 +5,19 @@ import com.alibaba.cloud.ai.dashscope.api.DashScopeResponseFormat;
 import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatModel;
 import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatOptions;
 import com.qingqiu.interview.common.constants.ChatConstant;
-import io.netty.channel.ChannelOption;
 import jakarta.annotation.Resource;
 import org.springframework.ai.chat.client.ChatClient;
 import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
 import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
 import org.springframework.ai.chat.memory.MessageWindowChatMemory;
 import org.springframework.ai.chat.model.ChatModel;
-import org.springframework.ai.openai.OpenAiChatModel;
-import org.springframework.ai.openai.OpenAiChatOptions;
-import org.springframework.ai.openai.api.OpenAiApi;
-import org.springframework.ai.openai.api.ResponseFormat;
 import org.springframework.beans.factory.annotation.Value;
 import org.springframework.context.annotation.Bean;
 import org.springframework.context.annotation.Configuration;
 import org.springframework.context.annotation.Primary;
-import org.springframework.http.client.ReactorClientHttpRequestFactory;
-import org.springframework.http.client.reactive.ReactorClientHttpConnector;
-import org.springframework.web.client.RestClient;
-import org.springframework.web.reactive.function.client.WebClient;
-import reactor.netty.http.client.HttpClient;
 
-import java.time.Duration;
+import static com.qingqiu.interview.common.utils.WebClientUtils.getRestClientBuilder;
+import static com.qingqiu.interview.common.utils.WebClientUtils.getWebClientBuilder;
 
 /**
  * DashScope相关配置类
@@ -45,21 +36,11 @@ public class DashScopeConfig {
     @Value("${spring.ai.dashscope.chat.options.model}")
     private String dashScopeChatModelName;
 
-    @Value("${spring.ai.openai.chat.options.model}")
-    private String openAiChatModelName;
 
-    @Value("${spring.ai.openai.base-url}")
-    private String openAiBaseUrl;
-
-    @Value("${spring.ai.openai.api-key}")
-    private String openAiApiKey;
 
     @Resource(name = ChatConstant.MYSQL_CHAT_MEMORY_BEAN_NAME)
     private MessageWindowChatMemory mysqlChatMemory;
 
-    @Resource(name = ChatConstant.REDIS_CHAT_MEMORY_BEAN_NAME)
-    private MessageWindowChatMemory redisChatMemory;
-
     /**
      * 创建DashScopeApi Bean实例
      * 配置了HTTP客户端连接参数，包括连接超时和响应超时时间
@@ -100,42 +81,6 @@ public class DashScopeConfig {
                 .build();
     }
 
-    /**
-     * 创建OpenAI聊天模型Bean实例（主模型）
-     * 配置了最大完成token数和响应格式为JSON Schema
-     *
-     * @return ChatModel实例
-     */
-    @Bean(name = ChatConstant.OPEN_AI_CHAT_MODEL_BEAN_NAME)
-    @Primary
-    public ChatModel openAiChatModel() {
-        return OpenAiChatModel.builder()
-                .openAiApi(
-                        OpenAiApi.builder()
-                                .apiKey(openAiApiKey)
-                                .baseUrl(openAiBaseUrl)
-                                .webClientBuilder(getWebClientBuilder())
-                                .restClientBuilder(getRestClientBuilder())
-                                .build()
-                )
-                .defaultOptions(
-                        OpenAiChatOptions.builder()
-                                .model(openAiChatModelName)
-                                .maxCompletionTokens(ChatConstant.MAX_COMPLETION_TOKENS)
-
-                                .responseFormat(
-                                        ResponseFormat.builder()
-                                                .type(ResponseFormat.Type.JSON_SCHEMA)
-                                                .jsonSchema(
-                                                        ResponseFormat.JsonSchema
-                                                                .builder()
-                                                                .build()
-                                                ).build()
-                                )
-                                .build()
-                )
-                .build();
-    }
 
     /**
      * 创建默认的聊天客户端Bean实例（使用DashScope模型）
@@ -171,47 +116,5 @@ public class DashScopeConfig {
                 .build();
     }
 
-    /**
-     * 创建基于MySQL存储聊天历史的OpenAI聊天客户端Bean实例
-     * 配置了消息内存管理和日志记录功能
-     *
-     * @return ChatClient实例
-     */
-    @Bean(name = ChatConstant.OPEN_AI_CHAT_CLIENT_BEAN_NAME)
-    public ChatClient openAiChatClient() {
-        return ChatClient
-                .builder(openAiChatModel())
-                .defaultAdvisors(
-                        MessageChatMemoryAdvisor.builder(mysqlChatMemory).build(),
-                        new SimpleLoggerAdvisor()
-                )
-                .build();
-    }
 
-
-    /**
-     * 创建全局默认的WebClient Bean实例
-     * 配置了连接超时和响应超时时间
-     *
-     * @return WebClient实例
-     */
-    public WebClient.Builder getWebClientBuilder() {
-        HttpClient httpClient = HttpClient.create()
-                .option(ChannelOption.CONNECT_TIMEOUT_MILLIS, 5000) // 连接超时
-                .responseTimeout(Duration.ofMillis(10000)); // 读取超时
-
-        return WebClient.builder()
-                .clientConnector(new ReactorClientHttpConnector(httpClient))
-                ;
-    }
-
-    private RestClient.Builder getRestClientBuilder() {
-        return RestClient.builder()
-                .requestFactory(
-                        new ReactorClientHttpRequestFactory(
-                                HttpClient.create()
-                                        .responseTimeout(Duration.ofMinutes(5))
-                        )
-                );
-    }
 }

src/main/java/com/qingqiu/interview/config/OpenAiChatConfig.java

@@ -0,0 +1,100 @@
+package com.qingqiu.interview.config;
+
+
+import com.qingqiu.interview.common.constants.ChatConstant;
+import jakarta.annotation.Resource;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
+import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
+import org.springframework.ai.chat.memory.MessageWindowChatMemory;
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.openai.OpenAiChatModel;
+import org.springframework.ai.openai.OpenAiChatOptions;
+import org.springframework.ai.openai.api.OpenAiApi;
+import org.springframework.ai.openai.api.ResponseFormat;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+import org.springframework.context.annotation.Primary;
+
+import static com.qingqiu.interview.common.utils.WebClientUtils.getRestClientBuilder;
+import static com.qingqiu.interview.common.utils.WebClientUtils.getWebClientBuilder;
+
+/**
+ * @program: ai-interview
+ * @description: openai聊天配置
+ * @author: huangpeng
+ * @create: 2025-11-06 20:03
+ **/
+@Configuration
+public class OpenAiChatConfig {
+
+
+    @Value("${spring.ai.openai.chat.options.model}")
+    private String openAiChatModelName;
+
+    @Value("${spring.ai.openai.base-url}")
+    private String openAiBaseUrl;
+
+    @Value("${spring.ai.openai.api-key}")
+    private String openAiApiKey;
+
+    @Resource(name = ChatConstant.MYSQL_CHAT_MEMORY_BEAN_NAME)
+    private MessageWindowChatMemory mysqlChatMemory;
+
+
+    /**
+     * 创建OpenAI聊天模型Bean实例（主模型）
+     * 配置了最大完成token数和响应格式为JSON Schema
+     *
+     * @return ChatModel实例
+     */
+    @Bean(name = ChatConstant.OPEN_AI_CHAT_MODEL_BEAN_NAME)
+    @Primary
+    public ChatModel openAiChatModel() {
+        return OpenAiChatModel.builder()
+                .openAiApi(
+                        OpenAiApi.builder()
+                                .apiKey(openAiApiKey)
+                                .baseUrl(openAiBaseUrl)
+                                .webClientBuilder(getWebClientBuilder())
+                                .restClientBuilder(getRestClientBuilder())
+                                .build()
+                )
+                .defaultOptions(
+                        OpenAiChatOptions.builder()
+                                .model(openAiChatModelName)
+                                .maxCompletionTokens(ChatConstant.MAX_COMPLETION_TOKENS)
+
+                                .responseFormat(
+                                        ResponseFormat.builder()
+                                                .type(ResponseFormat.Type.JSON_SCHEMA)
+                                                .jsonSchema(
+                                                        ResponseFormat.JsonSchema
+                                                                .builder()
+                                                                .build()
+                                                ).build()
+                                )
+                                .build()
+                )
+                .build();
+    }
+
+
+    /**
+     * 创建基于MySQL存储聊天历史的OpenAI聊天客户端Bean实例
+     * 配置了消息内存管理和日志记录功能
+     *
+     * @return ChatClient实例
+     */
+    @Bean(name = ChatConstant.OPEN_AI_CHAT_CLIENT_BEAN_NAME)
+    public ChatClient openAiChatClient() {
+        return ChatClient
+                .builder(openAiChatModel())
+                .defaultAdvisors(
+                        MessageChatMemoryAdvisor.builder(mysqlChatMemory).build(),
+                        new SimpleLoggerAdvisor()
+                )
+                .build();
+    }
+}

src/main/java/com/qingqiu/interview/controller/InterviewController.java

@@ -13,6 +13,7 @@ import org.springframework.context.annotation.Lazy;
 import org.springframework.web.bind.annotation.*;
 import org.springframework.web.multipart.MultipartFile;
 
+import java.io.IOException;
 import java.util.List;
 
 /**
@@ -122,4 +123,11 @@ public class InterviewController {
     public R<InterviewReportResponse> getInterviewReportDetail(@PathVariable String sessionId) {
         return R.success(interviewService.getInterviewReport(sessionId));
     }
+
+    @PostMapping("/read-pdf")
+    public R<?> readPdf(@RequestParam MultipartFile file) throws IOException {
+        String readPdfFile = interviewService.readPdfFile(file);
+        log.info("resume content: {}", readPdfFile);
+        return R.success(readPdfFile);
+    }
 }

src/main/java/com/qingqiu/interview/entity/ai/QuestionClassificationAiRes.java

@@ -0,0 +1,22 @@
+package com.qingqiu.interview.entity.ai;
+
+
+import com.fasterxml.jackson.annotation.JsonProperty;
+
+import java.util.List;
+
+/**
+ * @program: ai-interview
+ * @description: 题型分类ai返回
+ * @author: huangpeng
+ * @create: 2025-11-06 20:08
+ **/
+public class QuestionClassificationAiRes {
+
+    public record Item(@JsonProperty("content") String content,
+                       @JsonProperty("category") String category,
+                       @JsonProperty("difficulty") String difficulty,
+                       @JsonProperty("tags") String tags) {}
+
+    public record Wrapper(@JsonProperty("questions")List<Item> questions) {}
+}

src/main/java/com/qingqiu/interview/entity/ai/QuestionDeduplicationAiRes.java

@@ -0,0 +1,18 @@
+package com.qingqiu.interview.entity.ai;
+
+
+import com.fasterxml.jackson.annotation.JsonProperty;
+import redis.clients.jedis.graph.Statistics;
+
+import java.util.List;
+
+/**
+ * @program: ai-interview
+ * @description: 题目去重AI返回结果
+ * @author: huangpeng
+ * @create: 2025-11-07 10:37
+ **/
+public record QuestionDeduplicationAiRes(
+        @JsonProperty("questionIds") String questionIds
+) {
+}

src/main/java/com/qingqiu/interview/service/DashboardService.java

@@ -1,59 +1,15 @@
 package com.qingqiu.interview.service;
 
-import com.qingqiu.interview.mapper.InterviewSessionMapper;
+
-import com.qingqiu.interview.mapper.QuestionMapper;
 import com.qingqiu.interview.dto.DashboardStatsResponse;
-import lombok.RequiredArgsConstructor;
-import org.springframework.stereotype.Service;
 
-import java.time.LocalDate;
+/**
-import java.time.format.DateTimeFormatter;
+ * @program: ai-interview
-import java.util.List;
+ * @description: 工作台接口
-import java.util.Map;
+ * @author: huangpeng
-import java.util.stream.Collectors;
+ * @create: 2025-11-07 14:54
-import java.util.stream.IntStream;
+ **/
+public interface DashboardService {
 
-@Service
+    DashboardStatsResponse getDashboardStats();
-@RequiredArgsConstructor
-public class DashboardService {
-
-    private final QuestionMapper questionMapper;
-    private final InterviewSessionMapper sessionMapper;
-
-    public DashboardStatsResponse getDashboardStats() {
-        DashboardStatsResponse stats = new DashboardStatsResponse();
-
-        // 1. 获取核心KPI
-        stats.setTotalQuestions(questionMapper.selectCount(null));
-        stats.setTotalInterviews(sessionMapper.selectCount(null));
-
-        // 2. 获取题库分类统计
-        stats.setQuestionCategoryStats(questionMapper.countByCategory());
-
-        // 3. 获取最近7天的面试统计，并补全没有数据的日期
-        List<DashboardStatsResponse.DailyStat> recentStats = sessionMapper.countRecentInterviews(7);
-        stats.setRecentInterviewStats(fillMissingDates(recentStats, 7));
-
-        return stats;
-    }
-
-    /**
-     * 填充最近几天内没有面试数据的日期，补0
-     */
-    private List<DashboardStatsResponse.DailyStat> fillMissingDates(List<DashboardStatsResponse.DailyStat> existingStats, int days) {
-        Map<String, Long> statsMap = existingStats.stream()
-                .collect(Collectors.toMap(DashboardStatsResponse.DailyStat::getDate, DashboardStatsResponse.DailyStat::getCount));
-
-        DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd");
-
-        return IntStream.range(0, days)
-                .mapToObj(i -> LocalDate.now().minusDays(i))
-                .map(date -> {
-                    String dateString = date.format(formatter);
-                    long count = statsMap.getOrDefault(dateString, 0L);
-                    return new DashboardStatsResponse.DailyStat(dateString, count);
-                })
-                .sorted((d1, d2) -> d1.getDate().compareTo(d2.getDate())) // 按日期升序排序
-                .collect(Collectors.toList());
-    }
 }

src/main/java/com/qingqiu/interview/service/InterviewService.java

@@ -54,8 +54,17 @@ public interface InterviewService extends IService<InterviewSession> {
 
     /**
      * 获取面试报告
+     *
      * @param sessionId
      * @return
      */
     InterviewReportResponse getInterviewReport(String sessionId);
+
+    /**
+     * 读取pdf文件数据
+     *
+     * @param file 文件
+     * @return 文件内容
+     */
+    String readPdfFile(MultipartFile file) throws IOException;
 }

src/main/java/com/qingqiu/interview/service/QuestionClassificationService.java

@@ -1,147 +1,18 @@
 package com.qingqiu.interview.service;
 
-import com.fasterxml.jackson.core.JsonProcessingException;
-import com.fasterxml.jackson.databind.JsonNode;
-import com.fasterxml.jackson.databind.ObjectMapper;
-import com.qingqiu.interview.common.constants.ChatConstant;
-import com.qingqiu.interview.common.utils.UUIDUtils;
-import com.qingqiu.interview.entity.Question;
-import jakarta.annotation.Resource;
-import lombok.RequiredArgsConstructor;
-import lombok.extern.slf4j.Slf4j;
-import org.springframework.ai.chat.client.ChatClient;
-import org.springframework.ai.chat.memory.ChatMemory;
-import org.springframework.stereotype.Service;
 
-import java.util.ArrayList;
+import com.qingqiu.interview.entity.Question;
+import com.qingqiu.interview.entity.ai.QuestionClassificationAiRes;
+
 import java.util.List;
 
-@Slf4j
+/**
-@Service
+ * @program: ai-interview
-@RequiredArgsConstructor
+ * @description: 题型分类
-public class QuestionClassificationService {
+ * @author: huangpeng
+ * @create: 2025-11-06 19:59
+ **/
+public interface QuestionClassificationService {
 
-
+    List<QuestionClassificationAiRes.Item> classifyQuestions(String rawContent);
-    private final ObjectMapper objectMapper = new ObjectMapper();
-
-    private final ChatClient chatClient;
-
-    /**
-     * 使用AI对题目进行分类
-     */
-    public List<Question> classifyQuestions(String rawContent) {
-        log.info("开始使用AI分类题目，内容长度: {}", rawContent.length());
-        
-        String prompt = buildClassificationPrompt(rawContent);
-        String aiResponse = chatClient.prompt()
-                .user(prompt)
-                .call()
-                .content();
-
-        log.info("AI分类响应: {}", aiResponse);
-
-        assert aiResponse != null;
-        return parseAiResponse(aiResponse);
-    }
-
-    private String buildClassificationPrompt(String content) {
-        return """
-            请分析以下面试题内容，将其分类并提取信息。请严格按照以下JSON格式返回结果：
-            
-            {
-              "questions": [
-                {
-                  "content": "题目内容",
-                  "category": "分类（如：Java基础、Spring框架、数据库、算法、系统设计等）",
-                  "difficulty": "难度（Easy、Medium、Hard）",
-                  "tags": "相关标签，用逗号分隔"
-                }
-              ]
-            }
-            
-            分类规则：
-            1. category应该是具体的技术领域，如：Java基础、Spring框架、MySQL、Redis、算法与数据结构、系统设计、网络协议等
-            2. difficulty根据题目复杂度判断：Easy（基础概念）、Medium（实际应用）、Hard（深入原理或复杂场景）
-            3. tags包含更细粒度的标签，如：多线程、JVM、事务、索引等
-            4. 如果内容包含多个独立的题目，请分别提取
-            5. 只返回JSON，不要其他解释文字
-            
-            待分析内容：
-            """ + content;
-    }
-
-    private List<Question> parseAiResponse(String aiResponse) {
-        List<Question> questions = new ArrayList<>();
-        
-        try {
-            // 清理响应，移除可能的markdown标记
-            String cleanResponse = aiResponse.trim();
-            if (cleanResponse.startsWith("```json")) {
-                cleanResponse = cleanResponse.substring(7);
-            }
-            if (cleanResponse.endsWith("```")) {
-                cleanResponse = cleanResponse.substring(0, cleanResponse.length() - 3);
-            }
-            cleanResponse = cleanResponse.trim();
-            
-            JsonNode rootNode = objectMapper.readTree(cleanResponse);
-            JsonNode questionsNode = rootNode.get("questions");
-            
-            if (questionsNode != null && questionsNode.isArray()) {
-                for (JsonNode questionNode : questionsNode) {
-                    Question question = new Question()
-                            .setContent(getTextValue(questionNode, "content"))
-                            .setCategoryName(getTextValue(questionNode, "category"))
-                            .setDifficulty(getTextValue(questionNode, "difficulty"))
-                            .setTags(getTextValue(questionNode, "tags"));
-                    
-                    if (isValidQuestion(question)) {
-                        questions.add(question);
-                    }
-                }
-            }
-            
-            log.info("成功解析出 {} 个题目", questions.size());
-            
-        } catch (JsonProcessingException e) {
-            log.error("解析AI响应失败: {}", e.getMessage());
-            log.error("原始响应: {}", aiResponse);
-            
-            // 降级处理：如果AI返回格式不正确，尝试简单分割
-            questions.addAll(fallbackParsing(aiResponse));
-        }
-        
-        return questions;
-    }
-
-    private String getTextValue(JsonNode node, String fieldName) {
-        JsonNode fieldNode = node.get(fieldName);
-        return fieldNode != null ? fieldNode.asText("") : "";
-    }
-
-    private boolean isValidQuestion(Question question) {
-        return question.getContent() != null && !question.getContent().trim().isEmpty()
-                && question.getCategoryName() != null && !question.getCategoryName().trim().isEmpty();
-    }
-
-    private List<Question> fallbackParsing(String content) {
-        log.warn("使用降级解析策略");
-        List<Question> questions = new ArrayList<>();
-        
-        // 简单的降级策略：按行分割，每行作为一个题目
-        String[] lines = content.split("\n");
-        for (String line : lines) {
-            line = line.trim();
-            if (!line.isEmpty() && line.length() > 10) { // 过滤太短的内容
-                Question question = new Question()
-                        .setContent(line)
-                        .setCategoryName("未分类")
-                        .setDifficulty("Medium")
-                        .setTags("待分类");
-                questions.add(question);
-            }
-        }
-        
-        return questions;
-    }
 }

src/main/java/com/qingqiu/interview/service/impl/DashboardServiceImpl.java

@@ -0,0 +1,61 @@
+package com.qingqiu.interview.service.impl;
+
+import com.qingqiu.interview.mapper.InterviewSessionMapper;
+import com.qingqiu.interview.mapper.QuestionMapper;
+import com.qingqiu.interview.dto.DashboardStatsResponse;
+import com.qingqiu.interview.service.DashboardService;
+import lombok.RequiredArgsConstructor;
+import org.springframework.stereotype.Service;
+
+import java.time.LocalDate;
+import java.time.format.DateTimeFormatter;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+
+@Service
+@RequiredArgsConstructor
+public class DashboardServiceImpl implements DashboardService {
+
+    private final QuestionMapper questionMapper;
+    private final InterviewSessionMapper sessionMapper;
+
+    @Override
+    public DashboardStatsResponse getDashboardStats() {
+        DashboardStatsResponse stats = new DashboardStatsResponse();
+
+        // 1. 获取核心KPI
+        stats.setTotalQuestions(questionMapper.selectCount(null));
+        stats.setTotalInterviews(sessionMapper.selectCount(null));
+
+        // 2. 获取题库分类统计
+        stats.setQuestionCategoryStats(questionMapper.countByCategory());
+
+        // 3. 获取最近7天的面试统计，并补全没有数据的日期
+        List<DashboardStatsResponse.DailyStat> recentStats = sessionMapper.countRecentInterviews(7);
+        stats.setRecentInterviewStats(fillMissingDates(recentStats, 7));
+
+        return stats;
+    }
+
+    /**
+     * 填充最近几天内没有面试数据的日期，补0
+     */
+    private List<DashboardStatsResponse.DailyStat> fillMissingDates(List<DashboardStatsResponse.DailyStat> existingStats, int days) {
+        Map<String, Long> statsMap = existingStats.stream()
+                .collect(Collectors.toMap(DashboardStatsResponse.DailyStat::getDate, DashboardStatsResponse.DailyStat::getCount));
+
+        DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd");
+
+        return IntStream.range(0, days)
+                .mapToObj(i -> LocalDate.now().minusDays(i))
+                .map(date -> {
+                    String dateString = date.format(formatter);
+                    long count = statsMap.getOrDefault(dateString, 0L);
+                    return new DashboardStatsResponse.DailyStat(dateString, count);
+                })
+                .sorted((d1, d2) -> d1.getDate().compareTo(d2.getDate())) // 按日期升序排序
+                .collect(Collectors.toList());
+    }
+}

src/main/java/com/qingqiu/interview/service/impl/InterviewAiServiceImpl.java

@@ -60,6 +60,7 @@ public class InterviewAiServiceImpl implements InterviewAiService {
                                                               String jobRequirements,
                                                               String resumeContent,
                                                               int count) {
+        // TODO: 考虑删除这些注释掉的旧代码实现
 //        String skillsStr = String.join(", ", skills);
 //        String prompt = String.format(
 //                "你是一位专业的软件开发岗位技术面试官。" +
@@ -83,6 +84,7 @@ public class InterviewAiServiceImpl implements InterviewAiService {
                 .advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, sessionId))
                 .call()
                 .entity(QuestionAiRes.Wrapper.class);
+        // TODO: 考虑删除这些注释掉的旧代码实现
 //        String content = openAiChatClient
 //                .prompt(aiInterviewerPrompt)
 //                .advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, sessionId))
@@ -120,6 +122,7 @@ public class InterviewAiServiceImpl implements InterviewAiService {
                 .entity(QuestionAiRes.Wrapper.class);
         assert entity != null;
         return entity.questions();
+        // TODO: 考虑删除这些注释掉的旧代码实现
 //        String skillsStr = String.join(", ", skills);
 //        // 2. 构建发送给AI的提示
 //        String prompt = String.format("""
@@ -152,6 +155,7 @@ public class InterviewAiServiceImpl implements InterviewAiService {
 
     @Override
     public EvaluateAiRes evaluateAnswer(String sessionId, String question, String userAnswer, List<InterviewQuestionProgress> context) {
+        // TODO: 考虑删除这些注释掉的旧代码实现
         // 构建上下文历史
 //        String history = context.stream()
 //                .map(p -> String.format("Q: %s\nA: %s", p.getQuestionContent(), p.getUserAnswer()))
@@ -190,6 +194,7 @@ public class InterviewAiServiceImpl implements InterviewAiService {
 
     @Override
     public InterviewReportAiRes generateFinalReport(InterviewSession session, List<InterviewQuestionProgress> progressList) {
+        // TODO: 考虑删除这些注释掉的旧代码实现
 //        String transcript = progressList.stream()
 //                .map(p -> String.format("问题: %s\n回答: %s\nAI评分: %.1f\nAI反馈: %s\n",
 //                        p.getQuestionContent(), p.getUserAnswer(), p.getScore(), p.getFeedback()))

src/main/java/com/qingqiu/interview/service/impl/InterviewChatServiceImpl.java

@@ -28,11 +28,13 @@ import java.io.IOException;
 public class InterviewChatServiceImpl implements InterviewChatService {
 
     private final DocumentParserManager documentParserManager;
+    
     @Override
     public void startInterview(MultipartFile resume, InterviewStartRequest request) throws IOException {
       log.info("开始新面试会话，当前模式: {}, 候选人: {}, 默认AI模型: {}", request.getModel(), request.getCandidateName(), AIStrategyConstant.QWEN);
         // 1. 解析简历
         String resumeContent = parseResume(resume);
+        // TODO: 检查这个空if语句是否需要实现逻辑或删除
         // 判断是否使用本地题库
         if (request.getModel().equals("local")) {
             

src/main/java/com/qingqiu/interview/service/impl/InterviewServiceImpl.java

@@ -2,7 +2,6 @@ package com.qingqiu.interview.service.impl;
 
 import cn.hutool.core.collection.CollectionUtil;
 import cn.hutool.core.io.file.FileNameUtil;
-import com.alibaba.fastjson2.JSONArray;
 import com.alibaba.fastjson2.JSONObject;
 import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
 import com.baomidou.mybatisplus.extension.service.impl.ServiceImpl;
@@ -70,7 +69,7 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
     public InterviewSession startInterview(MultipartFile file, InterviewStartRequest dto) throws IOException {
         // 1. 创建并保存会话主记录
         String sessionId = UUID.randomUUID().toString().replace("-", "");
-        String resumeContent = parseResume(file);
+        String resumeContent = readPdfFile(file);
         InterviewSession session = new InterviewSession();
 
         session.setSessionId(sessionId);
@@ -400,7 +399,8 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
     }
 
 
-    private String parseResume(MultipartFile resume) throws IOException {
+    @Override
+    public String readPdfFile(MultipartFile resume) throws IOException {
         // 获取文件扩展名
         String extName = FileNameUtil.extName(resume.getOriginalFilename());
         // 1. 获取简历解析器

src/main/java/com/qingqiu/interview/service/impl/QuestionClassificationServiceImpl.java

@@ -0,0 +1,77 @@
+package com.qingqiu.interview.service.impl;
+
+import com.fasterxml.jackson.core.JsonProcessingException;
+import com.fasterxml.jackson.databind.JsonNode;
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.qingqiu.interview.entity.Question;
+import com.qingqiu.interview.entity.ai.QuestionClassificationAiRes;
+import com.qingqiu.interview.service.QuestionClassificationService;
+import lombok.RequiredArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.prompt.Prompt;
+import org.springframework.ai.chat.prompt.PromptTemplate;
+import org.springframework.ai.template.st.StTemplateRenderer;
+import org.springframework.stereotype.Service;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+
+@Slf4j
+@Service
+@RequiredArgsConstructor
+public class QuestionClassificationServiceImpl implements QuestionClassificationService {
+
+    private final ChatClient chatClient;
+
+    /**
+     * 使用AI对题目进行分类
+     */
+    @Override
+    public List<QuestionClassificationAiRes.Item> classifyQuestions(String rawContent) {
+        log.info("开始使用AI分类题目，内容长度: {}", rawContent.length());
+
+
+        QuestionClassificationAiRes.Wrapper entity = chatClient.prompt()
+                .user(buildClassificationPrompt(rawContent))
+                .call()
+                .entity(QuestionClassificationAiRes.Wrapper.class);
+        assert entity != null;
+        return entity.questions();
+    }
+
+    private String buildClassificationPrompt(String content) {
+
+        PromptTemplate prompt = PromptTemplate.builder()
+                .renderer(StTemplateRenderer.builder().startDelimiterToken('<').endDelimiterToken('>').build())
+                .template(
+                        """
+                                请分析以下面试题内容，将其分类并提取信息。请严格按照以下JSON格式返回结果：
+                                
+                                {
+                                  "questions": [
+                                    {
+                                      "content": "题目内容",
+                                      "category": "分类（如：Java基础、Spring框架、数据库、算法、系统设计等）",
+                                      "difficulty": "难度（Easy、Medium、Hard）",
+                                      "tags": "相关标签，用逗号分隔"
+                                    }
+                                  ]
+                                }
+                                
+                                分类规则：
+                                1. category应该是具体的技术领域，如：Java基础、Spring框架、MySQL、Redis、算法与数据结构、系统设计、网络协议等
+                                2. difficulty根据题目复杂度判断：Easy（基础概念）、Medium（实际应用）、Hard（深入原理或复杂场景）
+                                3. tags包含更细粒度的标签，如：多线程、JVM、事务、索引等
+                                4. 如果内容包含多个独立的题目，请分别提取
+                                5. 只返回JSON，不要其他解释文字
+                                
+                                待分析内容：
+                                <content>
+                                """
+                )
+                .build();
+        return prompt.render(Map.of("content", content));
+    }
+}

src/main/java/com/qingqiu/interview/service/impl/QuestionServiceImpl.java

@@ -1,28 +1,36 @@
 package com.qingqiu.interview.service.impl;
 
+import cn.hutool.core.bean.BeanUtil;
 import cn.hutool.core.collection.CollectionUtil;
+import com.alibaba.cloud.ai.dashscope.api.DashScopeApi;
 import com.alibaba.fastjson2.JSONArray;
 import com.alibaba.fastjson2.JSONObject;
 import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
 import com.baomidou.mybatisplus.extension.plugins.pagination.Page;
 import com.baomidou.mybatisplus.extension.service.impl.ServiceImpl;
-import com.qingqiu.interview.common.enums.LLMProvider;
 import com.qingqiu.interview.common.constants.CommonConstant;
-import com.qingqiu.interview.common.utils.TreeUtil;
+import com.qingqiu.interview.common.enums.LLMProvider;
+import com.qingqiu.interview.common.utils.TreeUtils;
 import com.qingqiu.interview.dto.QuestionOptionsDTO;
 import com.qingqiu.interview.dto.QuestionPageParams;
 import com.qingqiu.interview.entity.Question;
 import com.qingqiu.interview.entity.QuestionCategory;
+import com.qingqiu.interview.entity.ai.QuestionClassificationAiRes;
+import com.qingqiu.interview.entity.ai.QuestionDeduplicationAiRes;
 import com.qingqiu.interview.mapper.QuestionMapper;
 import com.qingqiu.interview.service.IQuestionCategoryService;
-import com.qingqiu.interview.service.QuestionClassificationService;
 import com.qingqiu.interview.service.QuestionService;
 import com.qingqiu.interview.service.parser.DocumentParser;
 import com.qingqiu.interview.vo.QuestionAndCategoryTreeListVO;
+import jakarta.annotation.Resource;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.lang3.StringUtils;
 import org.jetbrains.annotations.NotNull;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.prompt.PromptTemplate;
+import org.springframework.ai.template.TemplateRenderer;
+import org.springframework.ai.template.st.StTemplateRenderer;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.context.annotation.Lazy;
 import org.springframework.stereotype.Service;
@@ -38,10 +46,12 @@ import java.util.stream.Collectors;
 @RequiredArgsConstructor(onConstructor_ = {@Autowired, @Lazy})
 public class QuestionServiceImpl extends ServiceImpl<QuestionMapper, Question> implements QuestionService {
     private final QuestionMapper questionMapper;
-    private final QuestionClassificationService classificationService;
+    private final QuestionClassificationServiceImpl classificationService;
     private final List<DocumentParser> documentParserList; // This will be injected by Spring
     private final IQuestionCategoryService questionCategoryService;
 
+    private final ChatClient chatClient;
+
     /**
      * 分页查询题库
      */
@@ -96,19 +106,19 @@ public class QuestionServiceImpl extends ServiceImpl<QuestionMapper, Question> i
                 .orElseThrow(() -> new IllegalArgumentException("不支持的文件类型: " + fileExtension));
 
         String content = parser.parse(file.getInputStream());
-        List<Question> questionsFromAi = classificationService.classifyQuestions(content);
+        List<QuestionClassificationAiRes.Item> items = classificationService.classifyQuestions(content);
 
         int newQuestionsCount = 0;
-        for (Question question : questionsFromAi) {
+        for (QuestionClassificationAiRes.Item item : items) {
             try {
-                validateQuestion(question.getContent(), null);
+                validateQuestion(item.content(), null);
-                questionMapper.insert(question);
+                questionMapper.insert(BeanUtil.toBean(item, Question.class));
                 newQuestionsCount++;
             } catch (IllegalArgumentException e) {
-                log.warn("跳过重复题目: {}", question.getContent());
+                log.warn("跳过重复题目: {}", item.content());
             }
         }
-        log.info("成功导入 {} 个新题目，跳过 {} 个重复题目。", newQuestionsCount, questionsFromAi.size() - newQuestionsCount);
+        log.info("成功导入 {} 个新题目，跳过 {} 个重复题目。", newQuestionsCount, items.size() - newQuestionsCount);
     }
 
     /**
@@ -117,30 +127,32 @@ public class QuestionServiceImpl extends ServiceImpl<QuestionMapper, Question> i
     @Override
     @Transactional(rollbackFor = Exception.class)
     public void useAiCheckQuestionData() {
-//        // 查询数据库
+        // 查询数据库
-//        List<Question> questions = questionMapper.selectList(
+        List<Question> questions = questionMapper.selectList(
-//                new LambdaQueryWrapper<Question>()
+                new LambdaQueryWrapper<Question>()
-//                        .orderByDesc(Question::getCreatedTime)
+                        .orderByDesc(Question::getCreatedTime)
-//        );
+        );
-//        // 组装prompt
+        // 组装prompt
-//        if (CollectionUtil.isEmpty(questions)) {
+        if (CollectionUtil.isEmpty(questions)) {
-//            return;
+            return;
-//        }
+        }
-//        String prompt = getPrompt(questions);
+        String prompt = getPrompt(questions);
-//        log.info("发送内容: {}", prompt);
+
-//        // 验证token上下文长度
+
-//        Integer promptTokens = llmService.getPromptTokens(prompt);
+        QuestionDeduplicationAiRes entity = chatClient.prompt().user(prompt).call().entity(QuestionDeduplicationAiRes.class);
-//        log.info("当前prompt长度: {}", promptTokens);
+        assert entity != null;
-//        String chat = llmService.chat(prompt, LLMProvider.DEEPSEEK);
+        // 调用AI
-//        // 调用AI
+        log.info("AI返回内容: {}", JSONObject.toJSONString(entity));
-//        log.info("AI返回内容: {}", chat);
+        String s = entity.questionIds();
+        List<String> list = Arrays.asList(s.split(","));
+        // TODO: 检查这些注释代码是否可以删除
 //        JSONObject parse = JSONObject.parse(chat);
 //        JSONArray questionsIds = parse.getJSONArray("questions");
 //        List<Long> list = questionsIds.toList(Long.class);
-//        questionMapper.delete(
+        questionMapper.delete(
-//                new LambdaQueryWrapper<Question>()
+                new LambdaQueryWrapper<Question>()
-//                        .notIn(Question::getId, list)
+                        .notIn(Question::getId, list)
-//        );
+        );
     }
 
 
@@ -155,25 +167,71 @@ public class QuestionServiceImpl extends ServiceImpl<QuestionMapper, Question> i
         }
         JSONObject jsonObject = new JSONObject();
         jsonObject.put("data", jsonArray);
-        return String.format("""
-                你是一个数据清洗与去重专家。你的任务是从以下文本列表中，识别出语义相同或高度相似的条目，并为每一组相似条目筛选出一个最规范、最简洁的代表性版本。
 
-                【去重规则】
+        return PromptTemplate.builder()
-                1.  **语义核心优先**：忽略无关的修饰词、标点符号、后缀（如“:2”）、大小写和空格。
+                .renderer(
-                2.  **合并同类项**：将表达同一主题或问题的文本归为一组。
+                        StTemplateRenderer.builder()
-                3.  **选择标准**：从每一组中，选出那个最完整、最简洁、且没有多余符号（如序号、特殊后缀）的版本作为代表。如果两个版本质量相当，优先选择更短的那个。
+                                .startDelimiterToken('<')
-                4.  **保留原意**：确保选出的代表版本没有改变原文本的核心含义。
+                                .endDelimiterToken('>')
-                请按照下述格式返回，已被剔除掉的数据无需返回
+                                .build()
-                {
+                )
-                  "questions": [1, 2, 3, .....]
+                .template("""
-                }
+                        请对以下题库JSON数据进行智能去重处理。
-                分类规则：
+                        
-                1. 只返回JSON，不要其他解释文字
+                        ## 任务说明
-                2. 请严格按照API接口形式返回，不要返回任何额外的文字内容，包括'```json```'!!!!
+                        识别并移除语义相似或表达意思基本相同的重复题目，只保留每个独特题目的一个版本。
-                3. 请严格按照网络接口的形式返回JSON数据！！！
+                        
-                【请处理以下数据列表】:
+                        ## 语义相似度判断标准
-                %s
+                        1. 核心意思相同：即使表述不同，但考察的知识点和答案逻辑一致
-                """, jsonObject.toJSONString());
+                        2. 同义替换：使用同义词、近义词但意思不变的题目
+                        3. 句式变换：主动被动语态转换、疑问词替换等句式变化
+                        4. 冗余表述：增加了无关修饰词但核心内容相同的题目
+                        
+                        ## 处理规则
+                        - 对语义相似的题目组，只保留其中一条数据
+                        - 保留原则：选择表述最清晰、最完整的那条
+                        - 如果难以判断，保留ID较小或创建时间较早的那条
+                        
+                        ## 输出要求
+                        1. 只返回JSON，不要其他解释文字
+                        2. 请严格按照API接口形式返回，不要返回任何额外的文字内容，包括'```json```'!!!!
+                        3. 请严格按照网络接口的形式返回JSON数据！！！
+                        {
+                          "questionIds": "1, 2, 3" # 请返回保留数据的id
+                        }
+                        
+                        ## 特殊说明
+                        - 注意区分真正重复和只是题型相似的题目
+                        - 对于选择题，要同时考虑题干和选项的语义相似度
+                        - 保留题目版本的完整性
+                        
+                        请处理以下JSON数据：
+                        <data>
+                        """)
+                .build()
+                .render(Map.of("data", jsonObject.toJSONString()))
+        ;
+
+        // TODO: 检查这些注释代码是否可以删除 - 这是旧的prompt模板实现
+//        return String.format("""
+//                你是一个数据清洗与去重专家。你的任务是从以下文本列表中，识别出语义相同或高度相似的条目，并为每一组相似条目筛选出一个最规范、最简洁的代表性版本。
+//
+//                【去重规则】
+//                1.  **语义核心优先**：忽略无关的修饰词、标点符号、后缀（如“:2”）、大小写和空格。
+//                2.  **合并同类项**：将表达同一主题或问题的文本归为一组。
+//                3.  **选择标准**：从每一组中，选出那个最完整、最简洁、且没有多余符号（如序号、特殊后缀）的版本作为代表。如果两个版本质量相当，优先选择更短的那个。
+//                4.  **保留原意**：确保选出的代表版本没有改变原文本的核心含义。
+//                请返回数据的id，已被剔除掉的数据无需返回，格式如下：
+//                {
+//                  "questions": [1, 2, 3, .....]
+//                }
+//                分类规则：
+//                1. 只返回JSON，不要其他解释文字
+//                2. 请严格按照API接口形式返回，不要返回任何额外的文字内容，包括'```json```'!!!!
+//                3. 请严格按照网络接口的形式返回JSON数据！！！
+//                【请处理以下数据列表】:
+//                %s
+//                """, jsonObject.toJSONString());
     }
 
     /**
@@ -209,7 +267,7 @@ public class QuestionServiceImpl extends ServiceImpl<QuestionMapper, Question> i
         if (CollectionUtil.isNotEmpty(dto.getCategoryIds())) {
             questionCategories = questionCategoryService.batchFindByAncestorIdsUnion(dto.getCategoryIds());
             if (CollectionUtil.isNotEmpty(questionCategories)) {
-                treeList = TreeUtil.buildTree(
+                treeList = TreeUtils.buildTree(
                         questionCategories,
                         QuestionCategory::getId,
                         QuestionCategory::getParentId,

src/main/resources/application.yml

@@ -37,6 +37,7 @@ spring:
           min-idle: 0
           max-wait: -1ms
 
+# TODO: 考虑删除已注释的配置
 #  ai:
 #    openai:
 #      api-key: sk-faaa2a1b485442ccbf115ff1271a3480
@@ -44,6 +45,14 @@ spring:
 #      chat:
 #        options:
 #          model: deepseek-chat
+logging:
+  level:
+    org:
+      springframework:
+        ai:
+          chat:
+            client:
+              advisor: debug
 mybatis-plus:
   configuration:
     map-underscore-to-camel-case: true