使用spring-ai-alibaba重构项目

2025-11-24 13:37:37 +08:00 · 2025-10-22 19:53:26 +08:00 · 2025-10-10 18:03:16 +08:00
51 changed files with 1763 additions and 1195 deletions
--- a/pom.xml
+++ b/pom.xml
@@ -5,7 +5,7 @@
 	<parent>
 		<groupId>org.springframework.boot</groupId>
 		<artifactId>spring-boot-starter-parent</artifactId>
-		<version>3.5.0</version>
+		<version>3.4.5</version>
 		<relativePath/> <!-- lookup parent from repository -->
 	</parent>
 	<groupId>com.qingqiu</groupId>
@@ -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/>
@@ -28,6 +29,9 @@
 	</scm>
 	<properties>
 		<java.version>17</java.version>
+		<spring-ai.version>1.0.0</spring-ai.version>
+		<spring-ai-alibaba.version>1.0.0.2</spring-ai-alibaba.version>
+		<spring-boot.version>3.4.5</spring-boot.version>
 	</properties>
 	<dependencies>
 		<!-- MyBatis-Plus -->
@@ -53,11 +57,58 @@
 			<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>-->
+<!--			<version>1.0.0-SNAPSHOT</version>-->
+<!--		</dependency>-->
+
 		<dependency>
-			<groupId>com.alibaba</groupId>
-			<artifactId>dashscope-sdk-java</artifactId>
-			<version>2.21.5</version>
+			<groupId>org.springframework.ai</groupId>
+			<artifactId>spring-ai-starter-model-openai</artifactId>
 		</dependency>
+
+		<dependency>
+			<groupId>com.alibaba.cloud.ai</groupId>
+			<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>
+		</dependency>
+
+		<dependency>
+			<groupId>com.alibaba.cloud.ai</groupId>
+			<artifactId>spring-ai-alibaba-starter-memory-jdbc</artifactId>
+		</dependency>
+
+
+		<dependency>
+			<groupId>com.alibaba.cloud.ai</groupId>
+			<artifactId>spring-ai-alibaba-starter-memory-redis</artifactId>
+		</dependency>
+		<dependency>
+			<groupId>redis.clients</groupId>
+			<artifactId>jedis</artifactId>
+			<version>5.2.0</version>
+		</dependency>
+
+
+		<!-- 集成redis依赖  -->
+		<dependency>
+			<groupId>org.springframework.boot</groupId>
+			<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>-->
+<!--			<version>2.21.5</version>-->
+<!--		</dependency>-->
 		<!-- https://mvnrepository.com/artifact/cn.hutool/hutool-all -->
 		<dependency>
 			<groupId>cn.hutool</groupId>
@@ -114,8 +165,28 @@
 		</dependency>
 	</dependencies>
 	<dependencyManagement>
-
 		<dependencies>
+			<dependency>
+				<groupId>com.alibaba.cloud.ai</groupId>
+				<artifactId>spring-ai-alibaba-bom</artifactId>
+				<version>${spring-ai-alibaba.version}</version>
+				<type>pom</type>
+				<scope>import</scope>
+			</dependency>
+			<dependency>
+				<groupId>org.springframework.boot</groupId>
+				<artifactId>spring-boot-dependencies</artifactId>
+				<version>${spring-boot.version}</version>
+				<type>pom</type>
+				<scope>import</scope>
+			</dependency>
+			<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>com.baomidou</groupId>
 				<artifactId>mybatis-plus-bom</artifactId>
@@ -123,24 +194,20 @@
 				<type>pom</type>
 				<scope>import</scope>
 			</dependency>
-			<dependency>
-				<groupId>dev.langchain4j</groupId>
-				<artifactId>langchain4j-bom</artifactId>
-				<version>1.0.0-beta3</version>
-				<type>pom</type>
-				<scope>import</scope>
-			</dependency>
 		</dependencies>
+
 	</dependencyManagement>

 	<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>
@@ -151,33 +218,6 @@
 			</plugin>
 		</plugins>
 	</build>
-	<repositories>
-		<repository>
-			<id>spring-snapshots</id>
-			<name>Spring Snapshots</name>
-			<url>https://repo.spring.io/snapshot</url>
-			<releases>
-				<enabled>false</enabled>
-			</releases>
-		</repository>
-		<repository>
-			<id>spring-milestones</id>
-			<name>Spring Milestones</name>
-			<url>https://repo.spring.io/milestone</url>
-			<snapshots>
-				<enabled>false</enabled>
-			</snapshots>
-		</repository>
-	</repositories>
-	<pluginRepositories>
-		<pluginRepository>
-			<id>spring-snapshots</id>
-			<name>Spring Snapshots</name>
-			<url>https://repo.spring.io/snapshot</url>
-			<releases>
-				<enabled>false</enabled>
-			</releases>
-		</pluginRepository>
-	</pluginRepositories>
+

 </project>
--- a/src/main/java/com/qingqiu/interview/ai/entity/Message.java
+++ b/src/main/java/com/qingqiu/interview/ai/entity/Message.java
@@ -1,22 +0,0 @@
-package com.qingqiu.interview.ai.entity;
-
-import lombok.AllArgsConstructor;
-import lombok.Data;
-import lombok.NoArgsConstructor;
-import lombok.experimental.Accessors;
-
-import java.io.Serial;
-import java.io.Serializable;
-
-@Data
-@Accessors(chain = true)
-@AllArgsConstructor
-@NoArgsConstructor
-public class Message implements Serializable {
-    @Serial
-    private static final long serialVersionUID = 1L;
-
-    private String role;
-
-    private String content;
-}
--- a/src/main/java/com/qingqiu/interview/ai/factory/AIClientFactory.java
+++ b/src/main/java/com/qingqiu/interview/ai/factory/AIClientFactory.java
@@ -1,11 +0,0 @@
-package com.qingqiu.interview.ai.factory;
-
-import com.qingqiu.interview.common.enums.LLMProvider;
-import com.qingqiu.interview.ai.service.AIClientService;
-
-public interface AIClientFactory {
-    AIClientService createAIClient();
-
-    // 支持的提供商
-    LLMProvider getSupportedProvider();
-}
--- a/src/main/java/com/qingqiu/interview/ai/factory/AIClientManager.java
+++ b/src/main/java/com/qingqiu/interview/ai/factory/AIClientManager.java
@@ -1,33 +0,0 @@
-package com.qingqiu.interview.ai.factory;
-
-import com.qingqiu.interview.common.enums.LLMProvider;
-import com.qingqiu.interview.ai.service.AIClientService;
-import org.springframework.stereotype.Service;
-
-import java.util.List;
-import java.util.Map;
-import java.util.function.Function;
-import java.util.stream.Collectors;
-
-@Service
-public class AIClientManager {
-
-    private final Map<LLMProvider, AIClientFactory> factories;
-
-    public AIClientManager(List<AIClientFactory> strategies) {
-        this.factories = strategies.stream()
-                .collect(Collectors.toMap(
-                        AIClientFactory::getSupportedProvider,
-                        Function.identity()
-                ));
-    }
-
-    public AIClientService getClient(LLMProvider provider) {
-//        String factoryName = aiType + "ClientFactory";
-        AIClientFactory factory = factories.get(provider);
-        if (factory == null) {
-            throw new IllegalArgumentException("不支持的AI type: " + provider);
-        }
-        return factory.createAIClient();
-    }
-}
--- a/src/main/java/com/qingqiu/interview/ai/factory/DeepSeekClientFactory.java
+++ b/src/main/java/com/qingqiu/interview/ai/factory/DeepSeekClientFactory.java
@@ -1,20 +0,0 @@
-package com.qingqiu.interview.ai.factory;
-
-import com.qingqiu.interview.common.enums.LLMProvider;
-import com.qingqiu.interview.ai.service.AIClientService;
-import com.qingqiu.interview.ai.service.impl.DeepSeekClientServiceImpl;
-import com.qingqiu.interview.common.utils.SpringApplicationContextUtil;
-import org.springframework.stereotype.Service;
-
-@Service
-public class DeepSeekClientFactory implements AIClientFactory{
-    @Override
-    public AIClientService createAIClient() {
-        return SpringApplicationContextUtil.getBean(DeepSeekClientServiceImpl.class);
-    }
-
-    @Override
-    public LLMProvider getSupportedProvider() {
-        return LLMProvider.DEEPSEEK;
-    }
-}
--- a/src/main/java/com/qingqiu/interview/ai/factory/QwenClientFactory.java
+++ b/src/main/java/com/qingqiu/interview/ai/factory/QwenClientFactory.java
@@ -1,20 +0,0 @@
-package com.qingqiu.interview.ai.factory;
-
-import com.qingqiu.interview.common.enums.LLMProvider;
-import com.qingqiu.interview.ai.service.AIClientService;
-import com.qingqiu.interview.ai.service.impl.QwenClientServiceImpl;
-import com.qingqiu.interview.common.utils.SpringApplicationContextUtil;
-import org.springframework.stereotype.Service;
-
-@Service
-public class QwenClientFactory implements AIClientFactory{
-    @Override
-    public AIClientService createAIClient() {
-        return SpringApplicationContextUtil.getBean(QwenClientServiceImpl.class);
-    }
-
-    @Override
-    public LLMProvider getSupportedProvider() {
-        return LLMProvider.QWEN;
-    }
-}
--- a/src/main/java/com/qingqiu/interview/ai/service/AIClientService.java
+++ b/src/main/java/com/qingqiu/interview/ai/service/AIClientService.java
@@ -1,13 +0,0 @@
-package com.qingqiu.interview.ai.service;
-
-import com.alibaba.dashscope.common.Message;
-
-import java.util.List;
-
-public abstract class AIClientService {
-    public abstract String chatCompletion(String prompt);
-
-    public String chatCompletion(List<Message> messages) {
-        return null;
-    }
-}
--- a/src/main/java/com/qingqiu/interview/ai/service/impl/DeepSeekClientServiceImpl.java
+++ b/src/main/java/com/qingqiu/interview/ai/service/impl/DeepSeekClientServiceImpl.java
@@ -1,65 +0,0 @@
-package com.qingqiu.interview.ai.service.impl;
-
-import com.alibaba.dashscope.common.Message;
-import com.alibaba.fastjson2.JSONArray;
-import com.alibaba.fastjson2.JSONObject;
-import com.qingqiu.interview.ai.service.AIClientService;
-import com.qingqiu.interview.common.service.HttpService;
-import lombok.RequiredArgsConstructor;
-import org.apache.commons.lang3.StringUtils;
-import org.springframework.beans.factory.annotation.Value;
-import org.springframework.stereotype.Service;
-
-import java.util.Collections;
-import java.util.List;
-import java.util.Map;
-
-import static com.qingqiu.interview.common.utils.AIUtils.createUserMessage;
-
-/**
- * deepseek 接入
- */
-@Service
-@RequiredArgsConstructor
-public class DeepSeekClientServiceImpl extends AIClientService {
-
-    private final HttpService httpService;
-
-    @Value("${deepseek.api-url}")
-    private String apiUrl;
-
-    @Value("${deepseek.api-key}")
-    private String apiKey;
-
-
-    @Override
-    public String chatCompletion(String prompt) {
-        return chatCompletion(Collections.singletonList(createUserMessage(prompt)));
-    }
-
-    @Override
-    public String chatCompletion(List<Message> messages) {
-        JSONObject jsonObject = new JSONObject();
-        jsonObject.put("type", "json_object");
-        Map<String, Object> requestBody = Map.of(
-                "model", "deepseek-chat",
-                "messages", messages,
-                "max_tokens", 8192,
-                "response_format", Map.of("type", "json_object")
-        );
-        String res = httpService.postWithAuth(
-                apiUrl,
-                requestBody,
-                String.class,
-                "Bearer " + apiKey
-        ).block();
-        if (StringUtils.isNotBlank(res)) {
-            JSONObject jsonRes = JSONObject.parse(res);
-            JSONArray choices = jsonRes.getJSONArray("choices");
-            JSONObject resContent = choices.getJSONObject(0);
-            JSONObject message = resContent.getJSONObject("message");
-            return message.getString("content");
-        }
-        return null;
-    }
-}
--- a/src/main/java/com/qingqiu/interview/ai/service/impl/QwenClientServiceImpl.java
+++ b/src/main/java/com/qingqiu/interview/ai/service/impl/QwenClientServiceImpl.java
@@ -1,62 +0,0 @@
-package com.qingqiu.interview.ai.service.impl;
-
-import com.alibaba.dashscope.aigc.generation.Generation;
-import com.alibaba.dashscope.aigc.generation.GenerationParam;
-import com.alibaba.dashscope.aigc.generation.GenerationResult;
-import com.alibaba.dashscope.common.Message;
-import com.alibaba.dashscope.common.ResponseFormat;
-import com.alibaba.dashscope.exception.ApiException;
-import com.alibaba.dashscope.exception.InputRequiredException;
-import com.alibaba.dashscope.exception.NoApiKeyException;
-import com.qingqiu.interview.ai.service.AIClientService;
-import com.qingqiu.interview.common.res.ResultCode;
-import lombok.RequiredArgsConstructor;
-import lombok.extern.slf4j.Slf4j;
-import org.springframework.beans.factory.annotation.Value;
-import org.springframework.stereotype.Service;
-
-import java.util.Collections;
-import java.util.List;
-
-import static com.qingqiu.interview.common.constants.QwenModelConstant.QWEN_PLUS_LATEST;
-import static com.qingqiu.interview.common.utils.AIUtils.createUserMessage;
-
-@Slf4j
-@Service
-@RequiredArgsConstructor
-public class QwenClientServiceImpl extends AIClientService {
-
-    @Value("${dashscope.api-key}")
-    private String apiKey;
-
-    private final Generation generation;
-
-    @Override
-    public String chatCompletion(String prompt) {
-        return chatCompletion(Collections.singletonList(createUserMessage(prompt)));
-    }
-
-    @Override
-    public String chatCompletion(List<Message> messages) {
-
-        GenerationParam param = GenerationParam.builder()
-                .model(QWEN_PLUS_LATEST) // 可根据需要更换模型
-                .messages(messages)
-                .resultFormat(GenerationParam.ResultFormat.MESSAGE)
-                .responseFormat(ResponseFormat.builder().type(ResponseFormat.JSON_OBJECT).build())
-                .apiKey(apiKey)
-                .build();
-
-        GenerationResult result = null;
-        try {
-            result = generation.call(param);
-            return result.getOutput().getChoices().get(0).getMessage().getContent();
-        } catch (NoApiKeyException e) {
-            log.error("没有api key，请先确认配置！");
-            throw new com.qingqiu.interview.common.ex.ApiException(ResultCode.INTERNAL);
-        } catch (ApiException | InputRequiredException e) {
-            log.error("调用AI服务失败", e);
-            throw new com.qingqiu.interview.common.ex.ApiException(ResultCode.INTERNAL);
-        }
-    }
-}
--- a/src/main/java/com/qingqiu/interview/annotation/AiChatLog.java
+++ b/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 {
-}
--- a/src/main/java/com/qingqiu/interview/aspect/AiChatLogAspect.java
+++ b/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;
-    }
-}
--- a/src/main/java/com/qingqiu/interview/common/constants/ChatConstant.java
+++ b/src/main/java/com/qingqiu/interview/common/constants/ChatConstant.java
@@ -0,0 +1,44 @@
+package com.qingqiu.interview.common.constants;
+
+/**
+ * 聊天相关常量类
+ * 定义了聊天功能中使用的各种常量值
+ */
+public class ChatConstant {
+
+    /**
+     * MySQL聊天记录存储的Bean名称
+     */
+    public static final String MYSQL_CHAT_MEMORY_BEAN_NAME = "mysql-chat-memory";
+    
+    /**
+     * Redis聊天记录存储的Bean名称
+     */
+    public static final String REDIS_CHAT_MEMORY_BEAN_NAME = "redis-chat-memory";
+
+    /**
+     * 聊天记录最大保存消息数
+     */
+    public static final Integer MAX_MESSAGES = 100;
+
+    /**
+     * DashScope聊天模型的Bean名称
+     */
+    public static final String DASH_SCOPE_CHAT_MODEL_BEAN_NAME = "dash-scope-chat-model";
+    public static final String OPEN_AI_CHAT_MODEL_BEAN_NAME = "open-ai-chat-model";
+
+    /**
+     * DashScope聊天客户端的Bean名称
+     */
+    public static final String DASH_SCOPE_CHAT_CLIENT_BEAN_NAME = "dash-scope-chat-client";
+    
+    /**
+     * OpenAI聊天客户端的Bean名称
+     */
+    public static final String OPEN_AI_CHAT_CLIENT_BEAN_NAME = "open-ai-chat-client";
+    
+    /**
+     * 最大补全token数量
+     */
+    public static final Integer MAX_COMPLETION_TOKENS = 8192;
+}
--- a/src/main/java/com/qingqiu/interview/common/utils/AIUtils.java
+++ b/src/main/java/com/qingqiu/interview/common/utils/AIUtils.java
@@ -1,41 +0,0 @@
-package com.qingqiu.interview.common.utils;
-
-import com.alibaba.dashscope.common.Message;
-import com.alibaba.dashscope.common.Role;
-import com.alibaba.dashscope.tokenizers.Tokenizer;
-import com.alibaba.dashscope.tokenizers.TokenizerFactory;
-
-import java.util.List;
-
-public class AIUtils {
-
-    public static Message createMessage(String role, String content) {
-        return Message.builder()
-                .role(role)
-                .content(content)
-                .build();
-    }
-
-    public static Message createUserMessage(String prompt) {
-        return createMessage(Role.USER.getValue(), prompt);
-    }
-
-    public static Message createAIMessage(String prompt) {
-        return createMessage(Role.ASSISTANT.getValue(), prompt);
-    }
-
-    public static Message createSystemMessage(String prompt) {
-        return createMessage(Role.SYSTEM.getValue(), prompt);
-    }
-
-    /**
-     * 获取prompt的token数
-     * @param prompt 输入
-     * @return  tokens
-     */
-    public static Integer getPromptTokens(String prompt) {
-        Tokenizer tokenizer = TokenizerFactory.qwen();
-        List<Integer> integers = tokenizer.encodeOrdinary(prompt);
-        return integers.size();
-    }
-}
--- a/src/main/java/com/qingqiu/interview/common/utils/PromptTemplateUtils.java
+++ b/src/main/java/com/qingqiu/interview/common/utils/PromptTemplateUtils.java
@@ -0,0 +1,430 @@
+package com.qingqiu.interview.common.utils;
+
+import org.springframework.ai.chat.messages.SystemMessage;
+import org.springframework.ai.chat.messages.UserMessage;
+import org.springframework.ai.chat.prompt.Prompt;
+import org.springframework.ai.chat.prompt.PromptTemplate;
+import org.springframework.ai.chat.prompt.SystemPromptTemplate;
+
+import java.util.List;
+import java.util.Map;
+import java.util.Objects;
+
+/**
+ * 提示模板工具类
+ */
+public class PromptTemplateUtils {
+
+    /**
+     * 获取提取技能的提示
+     *
+     * @param resumeContent 简历内容
+     * @return 提示
+     */
+    public static Prompt getExtractSkillsPrompt(String resumeContent) {
+        SystemMessage systemMessage = new SystemMessage("""
+                你是一位资深的技术面试官，以严格和深入著称。
+                你需要从提供的简历内容中，提取出所有与职位相关的技能。
+                请按照以下JSON格式返回：
+                {"skills": ["技能1", "技能2", "..."]}
+                """);
+        UserMessage userMessage = new UserMessage(resumeContent);
+        return new Prompt(List.of(userMessage, systemMessage));
+    }
+
+    /**
+     * 获取AI面试官的提示
+     *
+     * @param params 参数
+     * @return 提示
+     */
+    public static Prompt getAiInterviewerPrompt(Map<String, Object> params) {
+        SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate("""
+                你是一位经验丰富的软件开发技术面试官，具备以下特质：
+                
+                ## 专业能力
+                - 拥有10年以上软件开发和团队管理经验
+                - 熟悉各种主流技术栈和架构模式
+                - 擅长通过技术面试评估候选人的真实能力
+                - 能够设计多层次、递进式的面试问题
+                
+                ## 面试原则
+                1. **精准匹配**: 问题必须紧密结合岗位要求和候选人背景
+                2. **层次递进**: 从基础概念到深度应用，从理论到实践
+                3. **场景化考察**: 基于真实项目场景设计问题
+                4. **全面评估**: 涵盖技术深度、问题解决能力、系统思维
+                ## 本岗位招聘要求
+                [{jobRequirements}]
+                
+                ## 输出要求
+                - 严格按照指定的JSON格式输出
+                - 不得包含任何JSON格式之外的内容
+                - 不得添加代码块标记(```json)或其他解释性文字
+                - 确保生成的问题数量与要求完全一致
+                """);
+        PromptTemplate userPromptTemplate = new PromptTemplate("""
+                请根据岗位招聘要求设计 {count} 道技术面试题。
+                
+                ## 候选人信息
+                
+                ### 技术栈
+                {skills}
+                
+                ### 简历内容
+                {resume}
+                
+                ## 面试题设计要求
+                
+                ### 覆盖维度
+                1. **基础理论** (20%): 核心概念、原理机制
+                2. **项目实践** (40%): 具体项目中的技术难点和解决方案
+                3. **系统设计** (20%): 架构思维、技术选型、性能优化
+                4. **问题解决** (20%): 调试能力、故障排查、代码优化
+                
+                ### 难度分布
+                - 基础题 (30%): 验证核心技能掌握情况
+                - 进阶题 (50%): 考察深度理解和实际应用
+                - 高阶题 (20%): 评估架构能力和创新思维
+                
+                ### 问题类型
+                - **概念阐述**: "请解释..."、"什么是..."
+                - **场景分析**: "在你的XX项目中..."、"如果遇到XX问题..."
+                - **方案设计**: "如何设计..."、"请给出..."
+                - **比较选择**: "XX和YY的区别..."、"为什么选择..."
+                
+                ## 输出格式
+                必须严格按照以下JSON格式输出，不得有任何偏差：
+                
+                {jsonRes}
+                
+                请立即开始生成面试题：
+                """);
+        String s = """
+                {
+                  "questions": [
+                    {
+                      "id": "ai-gen-1",
+                      "content": "问题内容..."
+                    }
+                  ]
+                }
+                """;
+        params.put("jsonRes", s);
+        return new Prompt(List.of(userPromptTemplate.createMessage(params), systemPromptTemplate.createMessage(params)));
+    }
+
+    public static Prompt getLocalInterviewPrompt(Map<String, Object> params) {
+        SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate("""
+                你是一位资深的技术面试专家。
+                ## 你的专业背景
+                - 10年+ 软件开发和技术管理经验
+                - 熟悉前端、后端、全栈、嵌入式、移动端、DevOps、大数据、AI等各技术领域
+                - 精通各种技术栈的深度和广度评估
+                - 擅长根据岗位特点和候选人背景进行精准匹配
+                ## 通用岗位分类及评估重点
+                ### 后端开发岗位
+                - 重点：框架熟练度、数据库设计、API设计、性能优化、并发处理
+                - 核心技能：编程语言基础、框架应用、系统设计、问题排查
+                ### 前端开发岗位
+                - 重点：UI/UX实现、性能优化、跨浏览器兼容、现代框架使用
+                - 核心技能：HTML/CSS/JS、框架应用、工程化、用户体验
+                ### 全栈开发岗位
+                - 重点：前后端技术栈、系统架构、DevOps流程
+                - 核心技能：多技术栈掌握、系统整合、项目管理
+                ### 架构师岗位
+                - 重点：系统设计、技术选型、性能调优、团队技术规划
+                - 核心技能：架构设计、技术决策、团队领导、业务理解
+                ## 筛选策略框架
+                ### 匹配维度权重
+                1. **技术栈匹配度** (35%): 候选人技能与岗位要求的重合度
+                2. **项目经验相关性** (30%): 过往项目与岗位场景的匹配度
+                3. **技能深度评估** (20%): 根据经验年限判断技能掌握深度
+                4. **发展潜力考量** (15%): 学习能力和技术视野的考察
+                ## 本岗位招聘要求
+                [{jobRequirements}]
+                
+                ## 输出规范
+                - 必须输出标准JSON格式：{jsonRes}
+                - 不得包含任何解释、注释或代码块标记
+                - 确保所选题目ID真实存在于题库中
+                - 保证题目数量与要求完全一致
+                """);
+
+        PromptTemplate userPromptTemplate = new PromptTemplate("""
+                请根据岗位招聘要求，从题库中筛选筛选 {count} 道技术面试题。
+                ## 候选人档案
+                ### 技术栈
+                {skills}
+                ### 简历内容
+                {resume}
+                ## 筛选要求
+                请根据以上信息，结合你的专业框架，从题库中筛选出最能评估该候选人是否适合此岗位的面试题。
+                ### 重点考虑因素
+                1. 岗位核心技术要求与候选人技能的匹配度
+                2. 候选人项目经验与岗位应用场景的关联性
+                3. 题目难度与候选人经验水平的适配性
+                4. 题目类型的多样性（理论+实践+设计）
+                """
+        );
+        params.put("jsonRes",
+                """
+                {
+                  "questions": [
+                    {
+                      "id": "1",
+                      "content": "问题内容..."
+                    },
+                    {
+                      "id": "3",
+                      "content": "问题内容..."
+                    }
+                    ...
+                  ]
+               }
+               """
+        );
+        return new Prompt(List.of(
+                userPromptTemplate.createMessage(params),
+                systemPromptTemplate.createMessage(params)
+        ));
+
+    }
+
+
+    public static Prompt getEvaluatePrompt(Map<String, Object> params) {
+//        SystemMessage systemMessage = new SystemMessage("""
+//                你是一位经验丰富的高级技术面试官，以公正、严谨、深入的评估风格著称。
+//                ## 你的专业特质
+//                - 拥有15年+ 技术开发和面试经验
+//                - 善于通过追问挖掘候选人的真实技术水平
+//                - 能够准确识别标准答案、实际经验和深度理解的区别
+//                - 注重考察解决问题的思路和实际应用能力
+//
+//                """);
+
+        PromptTemplate promptTemplate = new PromptTemplate("""
+                请对候选人的回答进行专业评估。
+                ## 评估维度及权重
+                ### 技术准确性 (30%)
+                - 概念理解的正确性
+                - 技术细节的准确程度
+                - 是否存在明显错误或误解
+                ### 深度与广度 (25%)
+                - 知识的深入程度
+                - 相关技术的关联理解
+                - 是否能举一反三
+                ### 实践经验 (25%)
+                - 是否有真实项目经验支撑
+                - 能否结合具体场景说明
+                - 对技术选型和权衡的理解
+                ### 表达能力 (20%)
+                - 逻辑清晰度
+                - 表达的完整性
+                - 专业术语使用的准确性
+                ## 评分标准
+                ### 优秀 (85-100分)
+                - 回答准确、深入、有见解
+                - 能结合实际项目经验
+                - 表达清晰、逻辑严密
+                - 展现出深度思考和实践能力
+                ### 良好 (70-84分)
+                - 回答基本正确，有一定深度
+                - 有实际应用经验
+                - 表达较为清晰
+                - 个别地方可能需要补充
+                ### 及格 (60-69分)
+                - 回答基本正确但较浅显
+                - 缺乏深入理解或实践经验
+                - 表达尚可但不够完整
+                - 需要进一步考察
+                ### 不及格 (0-59分)
+                - 回答错误或严重不完整
+                - 基础概念理解有误
+                - 表达混乱或逻辑不清
+                - 明显缺乏相关经验
+                ## 追问策略
+                ### 何时追问
+                1. **概念模糊**: 候选人给出的概念定义不够准确或完整
+                2. **缺少细节**: 回答过于宽泛，缺乏具体的技术细节
+                3. **经验质疑**: 怀疑候选人是否有真实的项目经验
+                4. **深度探索**: 基础回答正确，想考察更深层次的理解
+                ### 追问类型
+                1. **澄清追问**: "你刚才提到XX，能具体解释一下吗？"
+                2. **场景追问**: "在实际项目中，你是如何处理XX问题的？"
+                3. **对比追问**: "XX和YY有什么区别？你会如何选择？"
+                4. **深度追问**: "如果遇到XX情况，你会如何优化？"
+                ### 追问限制
+                - 单个问题最多追问3次
+                - 追问应该递进深入，不重复
+                - 追问后必须给出综合评估
+                - 避免过度纠缠细节，影响整体面试节奏
+                ## 当前问题
+                {question}
+                
+                ## 候选人回答
+                {candidateAnswer}
+                
+                ## 评估任务
+                
+                ### 请分析以下方面
+                1. **技术准确性**: 回答是否正确，有无技术错误
+                2. **完整性**: 是否涵盖了问题的关键要点
+                3. **深度**: 是否展现了深入的理解和思考
+                4. **实践性**: 是否结合了实际项目经验
+                5. **表达质量**: 逻辑是否清晰，表达是否准确
+                
+                ### 追问决策逻辑
+                - 如果回答存在明显不足，制定一个精准的追问来深入考察
+                - 如果已经追问过2次，本次必须结束追问（continueAsking: false）
+                - 追问应该针对性强，避免过于宽泛
+                - 优先考察核心技术能力，避免偏离主题
+                
+                ### AI标准答案要求
+                请提供一个简洁、准确的技术答案作为参考，突出关键要点。
+                
+                ## 输出格式要求
+                严格按照以下JSON格式输出，确保字段完整且格式正确：
+                
+                {jsonRes}
+                
+                开始评估：
+                """);
+        params.put(
+                "jsonRes",
+                """
+                         {
+                           "feedback": "对回答的具体评价，指出优点和不足",
+                           "suggestions": "具体的改进建议和学习方向",
+                           "aiAnswer": "简洁准确的标准答案要点",
+                           "score": 75.5,
+                           "continueAsking": true,
+                           "followUpQuestion": "具体的追问问题（如果不追问则为空字符串）"
+                         }
+                        """
+        );
+        return new Prompt(List.of(
+                promptTemplate.createMessage(params)
+        ));
+    }
+
+    /**
+     * 获取最终报告
+     *
+     * @param params 参数
+     * @return 提示
+     */
+    public static Prompt getFinalReportPrompt(Map<String, Object> params) {
+//        SystemMessage systemMessage = new SystemMessage("""
+//                你是一位资深的技术面试官和HR专家，具备丰富的候选人评估经验。你的职责是：
+//
+//                ## 核心职能：
+//                - 基于面试数据进行客观、全面的技术能力评估
+//                - 提供标准化的面试结果分析和建议
+//                - 支持招聘决策制定
+//
+//                ## 评估原则：
+//                1. **客观性**：基于实际表现数据，避免主观推测
+//                2. **全面性**：覆盖技术能力、沟通表达、问题解决等维度
+//                3. **标准化**：使用统一的评分体系和输出格式
+//                4. **实用性**：提供可执行的改进建议和明确的录用建议
+//
+//                ## 输出要求：
+//                严格按照JSON格式输出，不得包含任何markdown标记或额外解释：
+//
+//                {
+//                  "overallScore": <1-100整数>,
+//                  "overallFeedback": "<综合评价，客观描述候选人整体表现，150-200字>",
+//                  "technicalAssessment": {
+//                    "Java基础": "掌握良好，对集合框架理解深入。",
+//                    "Spring框架": "熟悉基本使用，但对底层原理理解不足。",
+//                    "数据库": "能够编写常规SQL，但在索引优化方面知识欠缺。",
+//                    "<技术领域1>": "<该领域的具体评估>",
+//                    ...
+//                  },
+//                  "strengthsAndWeaknesses": {
+//                    "strengths": ["<具体优势1>", "<具体优势2>", "<具体优势3>"],
+//                    "weaknesses": ["<具体不足1>", "<具体不足2>", "<具体不足3>"]
+//                  },
+//                  "suggestions": [
+//                    "<具体可执行的改进建议1>",
+//                    "<具体可执行的改进建议2>",
+//                    "<具体可执行的改进建议3>",
+//                    "<具体可执行的改进建议4>",
+//                    "<具体可执行的改进建议5>"
+//                  ],
+//                  "hiringRecommendation": "<强烈推荐|推荐|待考虑|不推荐>",
+//                  "hiringReason": "<录用建议的具体理由，50-80字>"
+//                }
+//
+//                ## 评分标准：
+//                - 90-100分：技术优秀，表达清晰，思维敏捷，超出岗位要求
+//                - 80-89分：技术良好，基本满足岗位要求，有培养潜力
+//                - 70-79分：技术一般，需要指导和培养
+//                - 60-69分：技术较弱，存在明显知识盲区
+//                - 60分以下：技术不足，不符合岗位要求
+//                """);
+
+        PromptTemplate promptTemplate = new PromptTemplate("""
+                请根据以下候选人信息和面试记录，生成标准化的面试评估报告：
+                ## 核心职能：
+                - 基于面试数据进行客观、全面的技术能力评估
+                - 提供标准化的面试结果分析和建议
+                - 支持招聘决策制定
+                
+                ## 评估原则：
+                1. **客观性**：基于实际表现数据，避免主观推测
+                2. **全面性**：覆盖技术能力、沟通表达、问题解决等维度
+                3. **标准化**：使用统一的评分体系和输出格式
+                4. **实用性**：提供可执行的改进建议和明确的录用建议
+                
+                ## 输出要求：
+                严格按照JSON格式输出，不得包含任何markdown标记或额外解释：
+                
+                {jsonRes}
+                
+                ## 评分标准：
+                - 90-100分：技术优秀，表达清晰，思维敏捷，超出岗位要求
+                - 80-89分：技术良好，基本满足岗位要求，有培养潜力
+                - 70-79分：技术一般，需要指导和培养
+                - 60-69分：技术较弱，存在明显知识盲区
+                - 60分以下：技术不足，不符合岗位要求
+                
+                ## 候选人简历信息：
+                {resume}
+                
+                ## 完整面试记录：
+                {history}
+                
+                请开始评估并输出JSON格式的报告。
+                """);
+        params.put("jsonRes", """
+                {
+                  "overallScore": <1-100整数>,
+                  "overallFeedback": "<综合评价，客观描述候选人整体表现，150-200字>",
+                  "technicalAssessment": {
+                    "Java基础": "掌握良好，对集合框架理解深入。",
+                    "Spring框架": "熟悉基本使用，但对底层原理理解不足。",
+                    "数据库": "能够编写常规SQL，但在索引优化方面知识欠缺。",
+                    "<技术领域1>": "<该领域的具体评估>",
+                    ...
+                  },
+                  "strengthsAndWeaknesses": {
+                    "strengths": ["<具体优势1>", "<具体优势2>", "<具体优势3>"],
+                    "weaknesses": ["<具体不足1>", "<具体不足2>", "<具体不足3>"]
+                  },
+                  "suggestions": [
+                    "<具体可执行的改进建议1>",
+                    "<具体可执行的改进建议2>",
+                    "<具体可执行的改进建议3>",
+                    "<具体可执行的改进建议4>",
+                    "<具体可执行的改进建议5>"
+                  ],
+                  "hiringRecommendation": "<强烈推荐|推荐|待考虑|不推荐>",
+                  "hiringReason": "<录用建议的具体理由，50-80字>"
+                }
+                """);
+        return new Prompt(List.of(
+                promptTemplate.createMessage(params)
+        ));
+    }
+}
--- a/src/main/java/com/qingqiu/interview/common/utils/SpringApplicationContextUtils.java
+++ b/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) {
--- a/src/main/java/com/qingqiu/interview/common/utils/TreeUtils.java
+++ b/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
--- a/src/main/java/com/qingqiu/interview/common/utils/UUIDUtils.java
+++ b/src/main/java/com/qingqiu/interview/common/utils/UUIDUtils.java
@@ -0,0 +1,8 @@
+package com.qingqiu.interview.common.utils;
+
+public class UUIDUtils {
+
+    public static String getUUID() {
+        return java.util.UUID.randomUUID().toString().replace("-", "");
+    }
+}
--- a/src/main/java/com/qingqiu/interview/common/utils/WebClientUtils.java
+++ b/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))
+                        )
+                );
+    }
+}
--- a/src/main/java/com/qingqiu/interview/config/ChatMemoryConfig.java
+++ b/src/main/java/com/qingqiu/interview/config/ChatMemoryConfig.java
@@ -0,0 +1,73 @@
+package com.qingqiu.interview.config;
+
+import com.alibaba.cloud.ai.memory.jdbc.MysqlChatMemoryRepository;
+import com.alibaba.cloud.ai.memory.redis.RedisChatMemoryRepository;
+import com.qingqiu.interview.common.constants.ChatConstant;
+import jakarta.annotation.Resource;
+import org.springframework.ai.chat.memory.MessageWindowChatMemory;
+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.jdbc.core.JdbcTemplate;
+
+import javax.sql.DataSource;
+
+import static com.qingqiu.interview.common.constants.ChatConstant.MAX_MESSAGES;
+
+/**
+ * 聊天记忆相关配置
+ */
+@Configuration
+public class ChatMemoryConfig {
+
+    @Value("${spring.ai.memory.redis.host}")
+    private String redisHost;
+    @Value("${spring.ai.memory.redis.port}")
+    private int redisPort;
+    @Value("${spring.ai.memory.redis.password}")
+    private String redisPassword;
+    @Value("${spring.ai.memory.redis.timeout}")
+    private int redisTimeout;
+
+    @Resource
+    private DataSource dataSource;
+
+
+    @Bean
+    public MysqlChatMemoryRepository mysqlChatMemoryRepository() {
+        JdbcTemplate jdbcTemplate = new JdbcTemplate(dataSource);
+        return MysqlChatMemoryRepository.mysqlBuilder()
+                .jdbcTemplate(jdbcTemplate)
+                .build();
+    }
+
+    @Primary
+    @Bean(name = ChatConstant.MYSQL_CHAT_MEMORY_BEAN_NAME)
+    public MessageWindowChatMemory mysqlChatMemory(MysqlChatMemoryRepository mysqlChatMemoryRepository) {
+        return MessageWindowChatMemory.builder()
+                .chatMemoryRepository(mysqlChatMemoryRepository)
+                .maxMessages(MAX_MESSAGES)
+                .build();
+    }
+
+    @Bean
+    public RedisChatMemoryRepository redisChatMemoryRepository() {
+        return RedisChatMemoryRepository.builder()
+                .host(redisHost)
+                .port(redisPort)
+                // 若没有设置密码则注释该项
+				.password(redisPassword)
+                .timeout(redisTimeout)
+                .build();
+    }
+
+    @Bean(name = ChatConstant.REDIS_CHAT_MEMORY_BEAN_NAME)
+    public MessageWindowChatMemory redisChatMemory(RedisChatMemoryRepository redisChatMemoryRepository) {
+        return MessageWindowChatMemory.builder()
+                .chatMemoryRepository(redisChatMemoryRepository)
+                .maxMessages(MAX_MESSAGES)
+                .build();
+    }
+
+}
--- a/src/main/java/com/qingqiu/interview/config/CorsConfig.java
+++ b/src/main/java/com/qingqiu/interview/config/CorsConfig.java
@@ -0,0 +1,22 @@
+package com.qingqiu.interview.config;
+
+import org.springframework.context.annotation.Configuration;
+import org.springframework.http.HttpHeaders;
+import org.springframework.web.servlet.config.annotation.CorsRegistry;
+import org.springframework.web.servlet.config.annotation.WebMvcConfigurer;
+
+@Configuration
+public class CorsConfig implements WebMvcConfigurer {
+    @Override
+    public void addCorsMappings(CorsRegistry registry) {
+        registry.addMapping("/**").
+                allowedOriginPatterns("*") //允许跨域的域名，可以用*表示允许任何域名使用
+//                allowedOrigins("*").    //在Springboot2.4对应Spring5.3后在设置allowCredentials(true)的基础上不能直接使用通配符设置allowedOrigins，而是需要指定特定的URL。如果需要设置通配符，需要通过allowedOriginPatterns指定
+                .allowedMethods("GET", "POST", "DELETE", "PUT") //允许任何方法（post、get等）
+                .allowedHeaders("*") //允许任何请求头
+                .allowCredentials(true) //带上cookie信息
+                .exposedHeaders(HttpHeaders.SET_COOKIE).maxAge(3600L); //maxAge(3600)表明在3600秒内，不需要再发送预检验请求，可以缓存该结果
+    }
+
+
+}
--- a/src/main/java/com/qingqiu/interview/config/DashScopeConfig.java
+++ b/src/main/java/com/qingqiu/interview/config/DashScopeConfig.java
@@ -1,14 +1,120 @@
 package com.qingqiu.interview.config;

-import com.alibaba.dashscope.aigc.generation.Generation;
+import com.alibaba.cloud.ai.dashscope.api.DashScopeApi;
+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 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.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;
+
+/**
+ * DashScope相关配置类
+ * 主要配置阿里云百炼平台(DashScope)和OpenAI的大模型服务
+ * 包括API客户端、聊天模型和聊天客户端的配置
+ *
+ * @author qingqiu
+ */
@Configuration
 public class DashScopeConfig {

+    // 从配置文件中读取DashScope API密钥
+    @Value("${spring.ai.dashscope.api-key}")
+    private String dashScopeApiKey;
+
+    @Value("${spring.ai.dashscope.chat.options.model}")
+    private String dashScopeChatModelName;
+
+
+
+    @Resource(name = ChatConstant.MYSQL_CHAT_MEMORY_BEAN_NAME)
+    private MessageWindowChatMemory mysqlChatMemory;
+
+    /**
+     * 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
+     *
+     * @return DashScopeApi实例
+     */
    @Bean
-    public Generation generation() {
-        return new Generation();
+    public DashScopeApi dashScopeApi() {
+        return DashScopeApi.builder()
+                .apiKey(dashScopeApiKey)
+                .restClientBuilder(getRestClientBuilder())
+                .webClientBuilder(getWebClientBuilder())
+                .build();
    }
-}
+
+
+    /**
+     * 创建DashScope聊天模型Bean实例
+     * 配置了最大token数、使用的模型以及返回格式为JSON对象
+     *
+     * @return ChatModel实例
+     */
+    @Bean(name = ChatConstant.DASH_SCOPE_CHAT_MODEL_BEAN_NAME)
+    public ChatModel dashScopeChatModel() {
+        return DashScopeChatModel.builder()
+                .dashScopeApi(dashScopeApi())
+                .defaultOptions(
+                        DashScopeChatOptions.builder()
+                                .withMaxToken(ChatConstant.MAX_COMPLETION_TOKENS)
+                                .withModel(dashScopeChatModelName)
+                                .withResponseFormat(
+                                        DashScopeResponseFormat.builder()
+                                                .type(DashScopeResponseFormat.Type.JSON_OBJECT)
+                                                .build()
+                                )
+                                .build()
+                )
+                .build();
+    }
+
+
+    /**
+     * 创建默认的聊天客户端Bean实例（使用DashScope模型）
+     * 添加了日志记录功能
+     *
+     * @return ChatClient实例
+     */
+    @Bean
+    @Primary
+    public ChatClient chatClient() {
+        return ChatClient
+                .builder(dashScopeChatModel())
+                .defaultAdvisors(
+                        new SimpleLoggerAdvisor()
+                )
+                .build();
+    }
+
+    /**
+     * 创建基于MySQL存储聊天历史的DashScope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.DASH_SCOPE_CHAT_CLIENT_BEAN_NAME)
+    public ChatClient dashScopeChatClient() {
+        return ChatClient
+                .builder(dashScopeChatModel())
+                .defaultAdvisors(
+                        MessageChatMemoryAdvisor.builder(mysqlChatMemory).build(),
+                        new SimpleLoggerAdvisor()
+                )
+                .build();
+    }
+
+
+}
--- a/src/main/java/com/qingqiu/interview/config/Fastjson2RedisSerializer.java
+++ b/src/main/java/com/qingqiu/interview/config/Fastjson2RedisSerializer.java
@@ -0,0 +1,57 @@
+package com.qingqiu.interview.config;
+
+import com.alibaba.fastjson2.JSON;
+import com.alibaba.fastjson2.JSONReader;
+import com.alibaba.fastjson2.JSONWriter;
+import org.springframework.data.redis.serializer.RedisSerializer;
+import org.springframework.data.redis.serializer.SerializationException;
+
+import java.nio.charset.Charset;
+import java.nio.charset.StandardCharsets;
+
+public class Fastjson2RedisSerializer<T> implements RedisSerializer<T> {
+    // 默认编码
+    public static final Charset DEFAULT_CHARSET = StandardCharsets.UTF_8;
+
+    // 泛型类型，用于反序列化
+    private final Class<T> clazz;
+
+    public Fastjson2RedisSerializer(Class<T> clazz) {
+        super();
+        this.clazz = clazz;
+    }
+
+    /**
+     * 序列化：将对象转换为字节数组
+     */
+    @Override
+    public byte[] serialize(T t) throws SerializationException {
+        if (t == null) {
+            return new byte[0];
+        }
+        try {
+            // 使用 Fastjson2 序列化对象，并写入字节数组
+            // 配置写入特性：WriteClassName 确保反序列化时能识别类型，如果不需要，可以移除。
+            return JSON.toJSONBytes(t, JSONWriter.Feature.WriteClassName);
+        } catch (Exception ex) {
+            throw new SerializationException("Could not serialize object with Fastjson2", ex);
+        }
+    }
+
+    /**
+     * 反序列化：将字节数组转换为对象
+     */
+    @Override
+    public T deserialize(byte[] bytes) throws SerializationException {
+        if (bytes == null || bytes.length <= 0) {
+            return null;
+        }
+        try {
+            // 使用 Fastjson2 反序列化字节数组
+            // 配置读取特性：SupportAutoType，确保可以正确读取带有类名信息的JSON
+            return JSON.parseObject(bytes, clazz, JSONReader.Feature.SupportAutoType);
+        } catch (Exception ex) {
+            throw new SerializationException("Could not deserialize object with Fastjson2", ex);
+        }
+    }
+}
--- a/src/main/java/com/qingqiu/interview/config/OpenAiChatConfig.java
+++ b/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();
+    }
+}
--- a/src/main/java/com/qingqiu/interview/config/RedisConfig.java
+++ b/src/main/java/com/qingqiu/interview/config/RedisConfig.java
@@ -0,0 +1,32 @@
+package com.qingqiu.interview.config;
+
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+import org.springframework.data.redis.connection.RedisConnectionFactory;
+import org.springframework.data.redis.core.RedisTemplate;
+import org.springframework.data.redis.serializer.StringRedisSerializer;
+
+@Configuration
+public class RedisConfig {
+
+    @Bean
+    public RedisTemplate<String, Object> redisTemplate(RedisConnectionFactory redisConnectionFactory) {
+        RedisTemplate<String, Object> template = new RedisTemplate<>();
+        template.setConnectionFactory(redisConnectionFactory);
+
+        // 使用StringRedisSerializer来序列化和反序列化redis的key值
+        StringRedisSerializer stringRedisSerializer = new StringRedisSerializer();
+        template.setKeySerializer(stringRedisSerializer);
+        template.setHashKeySerializer(stringRedisSerializer);
+
+        // 使用Fastjson2RedisSerializer来序列化和反序列化redis的value值
+
+        Fastjson2RedisSerializer<Object> fastjson2RedisSerializer = new Fastjson2RedisSerializer<>(Object.class);
+
+        template.setValueSerializer(fastjson2RedisSerializer);
+        template.setHashValueSerializer(fastjson2RedisSerializer);
+
+        template.afterPropertiesSet();
+        return template;
+    }
+}
--- a/src/main/java/com/qingqiu/interview/controller/AiSessionLogController.java
+++ b/src/main/java/com/qingqiu/interview/controller/AiSessionLogController.java
@@ -1,13 +1,14 @@
 package com.qingqiu.interview.controller;


-import com.alibaba.dashscope.common.Role;
 import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
 import com.qingqiu.interview.common.res.R;
 import com.qingqiu.interview.entity.AiSessionLog;
 import com.qingqiu.interview.service.IAiSessionLogService;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
+import org.springframework.ai.chat.messages.MessageType;
+import org.springframework.ai.chat.messages.SystemMessage;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.context.annotation.Lazy;
 import org.springframework.web.bind.annotation.GetMapping;
@@ -38,7 +39,7 @@ public class AiSessionLogController {
        return R.success(service.list(
                new LambdaQueryWrapper<AiSessionLog>()
                        .eq(AiSessionLog::getToken, sessionId)
-                        .ne(AiSessionLog::getRole, Role.SYSTEM.getValue())
+                        .ne(AiSessionLog::getRole, MessageType.SYSTEM.getValue())
        ));
    }

--- a/src/main/java/com/qingqiu/interview/controller/InterviewController.java
+++ b/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);
+    }
 }
--- a/src/main/java/com/qingqiu/interview/controller/InterviewQuestionProgressController.java
+++ b/src/main/java/com/qingqiu/interview/controller/InterviewQuestionProgressController.java
@@ -3,12 +3,10 @@ package com.qingqiu.interview.controller;

 import com.qingqiu.interview.common.res.R;
 import com.qingqiu.interview.dto.QuestionProgressPageParams;
+import com.qingqiu.interview.entity.InterviewQuestionProgress;
 import com.qingqiu.interview.service.IInterviewQuestionProgressService;
 import lombok.RequiredArgsConstructor;
-import org.springframework.web.bind.annotation.PostMapping;
-import org.springframework.web.bind.annotation.RequestBody;
-import org.springframework.web.bind.annotation.RequestMapping;
-import org.springframework.web.bind.annotation.RestController;
+import org.springframework.web.bind.annotation.*;

 /**
 * <p>
@@ -27,6 +25,7 @@ public class InterviewQuestionProgressController {

    /**
     * 面试问题进度列表
+     *
     * @param params 查询参数
     * @return data
     */
@@ -35,4 +34,16 @@ public class InterviewQuestionProgressController {
        return R.success(service.pageList(params));
    }

+    /**
+     * 获取面试问题进度详情
+     *
+     * @param id id
+     * @return data
+     */
+    @GetMapping("/{id}")
+    public R<InterviewQuestionProgress> getDetail(@PathVariable Long id) {
+        return R.success(service.getById(id));
+
+    }
+
 }
--- a/src/main/java/com/qingqiu/interview/dto/InterviewStartRequest.java
+++ b/src/main/java/com/qingqiu/interview/dto/InterviewStartRequest.java
@@ -25,5 +25,10 @@ public class InterviewStartRequest {
    /** 生成的面试题目数量 */
    private Integer totalQuestions = 10;

+    /**
+     * 岗位要求
+     */
+    private String jobRequirements;
+
    // 简历文件通过MultipartFile单独传递
 }
--- a/src/main/java/com/qingqiu/interview/entity/InterviewSession.java
+++ b/src/main/java/com/qingqiu/interview/entity/InterviewSession.java
@@ -31,6 +31,9 @@ public class InterviewSession implements Serializable {
    @TableField("resume_content")
    private String resumeContent;

+    @TableField("job_requirements")
+    private String jobRequirements;
+
    @TableField("extracted_skills")
    private String extractedSkills;
    @TableField("interview_type")
--- a/src/main/java/com/qingqiu/interview/entity/ai/EvaluateAiRes.java
+++ b/src/main/java/com/qingqiu/interview/entity/ai/EvaluateAiRes.java
@@ -0,0 +1,11 @@
+package com.qingqiu.interview.entity.ai;
+
+import com.fasterxml.jackson.annotation.JsonProperty;
+
+public record EvaluateAiRes(@JsonProperty("feedback") String feedback,
+                            @JsonProperty("suggestions") String suggestions,
+                            @JsonProperty("aiAnswer") String aiAnswer,
+                            @JsonProperty("score") double score,
+                            @JsonProperty("continueAsking") boolean continueAsking,
+                            @JsonProperty("followUpQuestion") String followUpQuestion) {
+}
--- a/src/main/java/com/qingqiu/interview/entity/ai/ExtractSkillAiRes.java
+++ b/src/main/java/com/qingqiu/interview/entity/ai/ExtractSkillAiRes.java
@@ -0,0 +1,10 @@
+package com.qingqiu.interview.entity.ai;
+
+import com.fasterxml.jackson.annotation.JsonProperty;
+
+import java.util.List;
+
+public record ExtractSkillAiRes(
+        @JsonProperty("skills") List<String> skills
+        ) {
+}
--- a/src/main/java/com/qingqiu/interview/entity/ai/InterviewReportAiRes.java
+++ b/src/main/java/com/qingqiu/interview/entity/ai/InterviewReportAiRes.java
@@ -0,0 +1,27 @@
+package com.qingqiu.interview.entity.ai;
+
+import com.fasterxml.jackson.annotation.JsonProperty;
+
+import java.util.List;
+import java.util.Map;
+
+public record InterviewReportAiRes(
+        @JsonProperty("overallScore") String overallScore,
+        @JsonProperty("overallFeedback") String overallFeedback,
+
+        @JsonProperty("technicalAssessment") Map<String, String> technicalAssessment,
+        @JsonProperty("strengthsAndWeaknesses") StrengthsAndWeaknesses strengthsAndWeaknesses,
+
+        @JsonProperty("suggestions") List<String> suggestions,
+        @JsonProperty("hiringRecommendation") String hiringRecommendation,
+        @JsonProperty("hiringReason") String hiringReason
+) {
+
+    /**
+     * 内部 Record：对应 "strengthsAndWeaknesses" 对象。
+     */
+    public record StrengthsAndWeaknesses(
+            @JsonProperty("strengths") List<String> strengths,
+            @JsonProperty("weaknesses") List<String> weaknesses
+    ) {}
+}
--- a/src/main/java/com/qingqiu/interview/entity/ai/QuestionAiRes.java
+++ b/src/main/java/com/qingqiu/interview/entity/ai/QuestionAiRes.java
@@ -0,0 +1,26 @@
+package com.qingqiu.interview.entity.ai;
+
+
+import com.fasterxml.jackson.annotation.JsonProperty;
+
+import java.util.List;
+
+public class QuestionAiRes {
+
+    /**
+     * 内部实体类：对应 JSON 数组中的每个元素。
+     * { "id": "ai-gen-1", "content": "问题内容..." }
+     */
+    public record Question(
+            @JsonProperty("id") String id,
+            @JsonProperty("content") String content
+    ) {}
+
+    /**
+     * 外部实体类：对应整个 JSON 响应的顶层结构。
+     * { "questions": [...] }
+     */
+    public record Wrapper(
+            @JsonProperty("questions") List<Question> questions
+    ) {}
+}
--- a/src/main/java/com/qingqiu/interview/entity/ai/QuestionClassificationAiRes.java
+++ b/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) {}
+}
--- a/src/main/java/com/qingqiu/interview/entity/ai/QuestionDeduplicationAiRes.java
+++ b/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
+) {
+}
--- a/src/main/java/com/qingqiu/interview/service/ChatService.java
+++ b/src/main/java/com/qingqiu/interview/service/ChatService.java
@@ -1,29 +0,0 @@
-package com.qingqiu.interview.service;
-
-import com.qingqiu.interview.dto.ChatDTO;
-import com.qingqiu.interview.dto.InterviewStartRequest;
-import com.qingqiu.interview.vo.ChatVO;
-import org.springframework.web.multipart.MultipartFile;
-
-/**
- * <h1></h1>
- *
- * @author qingqiu
- * @date 2025/9/18 12:45
- */
-public interface ChatService {
-
-    /**
-     * 创建普通会话
-     * @return sessionId
-     */
-    ChatVO createChat(ChatDTO dto);
-
-    /**
-     * 创建面试会话
-     * @param resume 简历
-     * @param request 面试信息
-     * @return sessionId
-     */
-    String createInterviewChat(MultipartFile resume, InterviewStartRequest request);
-}
--- a/src/main/java/com/qingqiu/interview/service/DashboardService.java
+++ b/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;
-import java.util.List;
-import java.util.Map;
-import java.util.stream.Collectors;
-import java.util.stream.IntStream;
-
-@Service
-@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());
-    }
-}
+package com.qingqiu.interview.service;
+
+
+import com.qingqiu.interview.dto.DashboardStatsResponse;
+
+/**
+ * @program: ai-interview
+ * @description: 工作台接口
+ * @author: huangpeng
+ * @create: 2025-11-07 14:54
+ **/
+public interface DashboardService {
+
+    DashboardStatsResponse getDashboardStats();
+}
--- a/src/main/java/com/qingqiu/interview/service/InterviewAiService.java
+++ b/src/main/java/com/qingqiu/interview/service/InterviewAiService.java
@@ -1,15 +1,17 @@
 package com.qingqiu.interview.service;

-import com.alibaba.fastjson2.JSONObject;
 import com.qingqiu.interview.entity.InterviewQuestionProgress;
 import com.qingqiu.interview.entity.InterviewSession;
 import com.qingqiu.interview.entity.Question;
+import com.qingqiu.interview.entity.ai.EvaluateAiRes;
+import com.qingqiu.interview.entity.ai.InterviewReportAiRes;
+import com.qingqiu.interview.entity.ai.QuestionAiRes;

 import java.util.List;

 /**
 * <h1>
- *     面试接入AI的接口
+ * 面试接入AI的接口
 * </h1>
 *
 * @author qingqiu
@@ -23,38 +25,38 @@ public interface InterviewAiService {
     * @param resumeContent 简历文本
     * @return 包含技能列表的JSON对象
     */
-    JSONObject extractSkillsFromResume(String resumeContent);
+    List<String> extractSkillsFromResume(String resumeContent);

    /**
     * 根据技能动态生成面试题目
     *
-     * @param skills      技能列表
+     * @param skills        技能列表
     * @param resumeContent 简历内容
-     * @param count       需要生成的题目数量
+     * @param count         需要生成的题目数量
     * @return 包含问题列表的JSON对象
     */
-    JSONObject generateQuestionsOfAi(String sessionId, List<String> skills, String resumeContent, int count);
+    List<QuestionAiRes.Question> generateQuestionsOfAi(String sessionId, List<String> skills, String jobRequirements, String resumeContent, int count);

-    JSONObject generateQuestionOfLocal(String sessionId, List<Question> questions, List<String> skills, String resumeContent, int count);
+    List<QuestionAiRes.Question> generateQuestionOfLocal(String sessionId, List<Question> questions, List<String> skills, String jobRequirements, String resumeContent, int count);

    /**
     * 评估用户的回答
     *
-     * @param question    问题内容
-     * @param userAnswer  用户的回答
-     * @param context     可选的上下文(之前的问答历史)
+     * @param question   问题内容
+     * @param userAnswer 用户的回答
+     * @param context    可选的上下文(之前的问答历史)
     * @return 包含评估结果的JSON对象
     */
-    JSONObject evaluateAnswer(String sessionId, String question, String userAnswer, List<InterviewQuestionProgress> context);
+    EvaluateAiRes evaluateAnswer(String sessionId, String question, String userAnswer, List<InterviewQuestionProgress> context);

    /**
     * 生成最终的面试评估报告
     *
-     * @param session        面试会话信息
-     * @param progressList   整个面试的问答记录
+     * @param session      面试会话信息
+     * @param progressList 整个面试的问答记录
     * @return 包含最终报告的JSON对象
     */
-    JSONObject generateFinalReport(InterviewSession session, List<InterviewQuestionProgress> progressList);
+    InterviewReportAiRes generateFinalReport(InterviewSession session, List<InterviewQuestionProgress> progressList);

    String generateFirstQuestion(String sessionId, String candidateName, String questionContent);

--- a/src/main/java/com/qingqiu/interview/service/InterviewService.java
+++ b/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;
 }
--- a/src/main/java/com/qingqiu/interview/service/QuestionClassificationService.java
+++ b/src/main/java/com/qingqiu/interview/service/QuestionClassificationService.java
@@ -1,138 +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.entity.Question;
-import com.qingqiu.interview.service.llm.LlmService;
-import lombok.RequiredArgsConstructor;
-import lombok.extern.slf4j.Slf4j;
-import org.springframework.stereotype.Service;
-
-import java.util.ArrayList;
-import java.util.List;
-
-@Slf4j
-@Service
-@RequiredArgsConstructor
-public class QuestionClassificationService {
-
-    private final LlmService llmService;
-    
-    private final ObjectMapper objectMapper = new ObjectMapper();
-
-    /**
-     * 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 = llmService.chat(prompt);
-        
-        log.info("AI分类响应: {}", aiResponse);
-        
-        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;
-    }
-}
+package com.qingqiu.interview.service;
+
+
+import com.qingqiu.interview.entity.Question;
+import com.qingqiu.interview.entity.ai.QuestionClassificationAiRes;
+
+import java.util.List;
+
+/**
+ * @program: ai-interview
+ * @description: 题型分类
+ * @author: huangpeng
+ * @create: 2025-11-06 19:59
+ **/
+public interface QuestionClassificationService {
+
+    List<QuestionClassificationAiRes.Item> classifyQuestions(String rawContent);
+}
--- a/src/main/java/com/qingqiu/interview/service/impl/ChatServiceImpl.java
+++ b/src/main/java/com/qingqiu/interview/service/impl/ChatServiceImpl.java
@@ -1,95 +0,0 @@
-package com.qingqiu.interview.service.impl;
-
-import cn.hutool.core.collection.CollectionUtil;
-import com.alibaba.dashscope.common.Message;
-import com.alibaba.dashscope.common.Role;
-import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
-import com.qingqiu.interview.ai.factory.AIClientManager;
-import com.qingqiu.interview.annotation.AiChatLog;
-import com.qingqiu.interview.common.enums.LLMProvider;
-import com.qingqiu.interview.common.utils.AIUtils;
-import com.qingqiu.interview.dto.ChatDTO;
-import com.qingqiu.interview.dto.InterviewStartRequest;
-import com.qingqiu.interview.entity.AiSessionLog;
-import com.qingqiu.interview.service.ChatService;
-import com.qingqiu.interview.service.IAiSessionLogService;
-import com.qingqiu.interview.vo.ChatVO;
-import lombok.RequiredArgsConstructor;
-import lombok.extern.slf4j.Slf4j;
-import org.springframework.stereotype.Service;
-import org.springframework.web.multipart.MultipartFile;
-
-import java.math.BigDecimal;
-import java.math.RoundingMode;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.concurrent.atomic.AtomicInteger;
-
-import static com.qingqiu.interview.common.constants.CommonConstant.DEFAULT_TRUNCATE_RATIO;
-import static com.qingqiu.interview.common.constants.CommonConstant.MAX_TOKEN;
-
-/**
- * <h1></h1>
- *
- * @author qingqiu
- * @date 2025/9/18 12:56
- */
-
-@Slf4j
-@Service
-@RequiredArgsConstructor
-public class ChatServiceImpl implements ChatService {
-
-    private final AIClientManager aiClientManager;
-
-    private final IAiSessionLogService aiSessionLogService;
-
-    @Override
-    @AiChatLog
-    public ChatVO createChat(ChatDTO dto) {
-        LLMProvider llmProvider = LLMProvider.fromCode(dto.getAiModel());
-        List<Message> messages = new ArrayList<>();
-        AtomicInteger tokens = new AtomicInteger();
-        // 如果会话id不为空 则从数据库中获取会话记录
-        if (dto.getSessionId() != null) {
-            List<AiSessionLog> list = aiSessionLogService.list(
-                    new LambdaQueryWrapper<AiSessionLog>()
-                            .eq(AiSessionLog::getToken, dto.getSessionId())
-                            .eq(AiSessionLog::getDataType, dto.getDataType())
-                            .orderByAsc(AiSessionLog::getCreatedTime)
-            );
-            if (CollectionUtil.isNotEmpty(list)) {
-                messages.addAll(list.stream().map(data -> {
-                    tokens.getAndAdd(AIUtils.getPromptTokens(data.getContent()));
-                    return AIUtils.createMessage(data.getRole(), data.getContent());
-                }).toList());
-            }
-
-        }
-        messages.add(AIUtils.createMessage(dto.getRole(), dto.getContent()));
-        List<Message> finalMessage = new ArrayList<>();
-        // 剪切 10%的消息
-        if (tokens.get() > MAX_TOKEN) {
-            BigDecimal size = new BigDecimal(String.valueOf(messages.size()));
-            size = size.multiply(DEFAULT_TRUNCATE_RATIO).setScale(0, RoundingMode.HALF_UP);
-            for (int i = size.intValue(); i < messages.size(); i++) {
-                finalMessage.add(messages.get(i));
-            }
-        } else {
-            finalMessage = messages;
-        }
-        String res = aiClientManager.getClient(llmProvider).chatCompletion(finalMessage);
-
-
-        return ChatVO.builder()
-                .role(Role.ASSISTANT.getValue())
-                .sessionId(dto.getSessionId())
-                .content(res)
-                .build();
-    }
-
-    @Override
-    public String createInterviewChat(MultipartFile resume, InterviewStartRequest request) {
-        return "";
-    }
-}
--- a/src/main/java/com/qingqiu/interview/service/impl/DashboardServiceImpl.java
+++ b/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());
+    }
+}
--- a/src/main/java/com/qingqiu/interview/service/impl/InterviewAiServiceImpl.java
+++ b/src/main/java/com/qingqiu/interview/service/impl/InterviewAiServiceImpl.java
@@ -1,24 +1,29 @@
 package com.qingqiu.interview.service.impl;

-import com.alibaba.dashscope.common.Role;
+import cn.hutool.core.map.MapUtil;
 import com.alibaba.fastjson2.JSON;
 import com.alibaba.fastjson2.JSONObject;
-import com.qingqiu.interview.common.constants.CommonConstant;
-import com.qingqiu.interview.dto.ChatDTO;
+import com.qingqiu.interview.common.constants.ChatConstant;
+import com.qingqiu.interview.common.utils.PromptTemplateUtils;
 import com.qingqiu.interview.entity.InterviewQuestionProgress;
 import com.qingqiu.interview.entity.InterviewSession;
 import com.qingqiu.interview.entity.Question;
-import com.qingqiu.interview.service.ChatService;
+import com.qingqiu.interview.entity.ai.EvaluateAiRes;
+import com.qingqiu.interview.entity.ai.ExtractSkillAiRes;
+import com.qingqiu.interview.entity.ai.InterviewReportAiRes;
+import com.qingqiu.interview.entity.ai.QuestionAiRes;
 import com.qingqiu.interview.service.InterviewAiService;
-import com.qingqiu.interview.vo.ChatVO;
-import lombok.RequiredArgsConstructor;
+import jakarta.annotation.Resource;
 import lombok.extern.slf4j.Slf4j;
-import org.springframework.beans.factory.annotation.Autowired;
-import org.springframework.context.annotation.Lazy;
+import org.jetbrains.annotations.NotNull;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.memory.ChatMemory;
+import org.springframework.ai.chat.prompt.Prompt;
 import org.springframework.stereotype.Service;

 import java.util.List;
-import java.util.stream.Collectors;
+import java.util.Map;
+import java.util.UUID;

 /**
 * <h1></h1>
@@ -28,114 +33,168 @@ import java.util.stream.Collectors;
 */
@Slf4j
@Service
-@RequiredArgsConstructor(onConstructor_ = {@Autowired, @Lazy})
 public class InterviewAiServiceImpl implements InterviewAiService {

-    private final ChatService chatService;
+    @Resource
+    private ChatClient chatClient;
+
+    @Resource(name = ChatConstant.DASH_SCOPE_CHAT_CLIENT_BEAN_NAME)
+    private ChatClient dashScopeChatClient;
+
+    @Resource(name = ChatConstant.OPEN_AI_CHAT_CLIENT_BEAN_NAME)
+    private ChatClient openAiChatClient;

    @Override
-    public JSONObject extractSkillsFromResume(String resumeContent) {
-        String prompt = "你是一位资深的IT技术招聘专家。" +
-                "请仔细阅读以下简历内容，并提取出其中所有的关键技术技能。" +
-                "请严格按照以下JSON格式返回，不要添加任何额外的解释或说明：\n" +
-                "{\"skills\": [\"技能1\", \"技能2\", \"...\"]}\n\n" +
-                "简历内容如下：\n" + resumeContent;
-
-        ChatDTO chatDTO = new ChatDTO()
-                .setContent(prompt)
-                .setRole(Role.SYSTEM.getValue())
-                .setDataType(CommonConstant.ONE);
-        ChatVO chatVO = chatService.createChat(chatDTO);
-
-        return JSONObject.parse(chatVO.getContent());
+    public List<String> extractSkillsFromResume(String resumeContent) {
+        ExtractSkillAiRes entity = chatClient
+                .prompt(PromptTemplateUtils.getExtractSkillsPrompt(resumeContent))
+                .call()
+                .entity(ExtractSkillAiRes.class);
+        assert entity != null;
+        return entity.skills();
    }

    @Override
-    public JSONObject generateQuestionsOfAi(String sessionId, List<String> skills, String resumeContent, int count) {
-        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。" +
-                        "请根据候选人的以下技术栈、项目经历、简历内容，生成 %d 道有深度和广度的面试题。" +
-                        "题目应覆盖候选人的主要技术领域，并能考察其解决问题的能力。" +
-                        "请严格按照以下JSON格式返回，question数组中必须包含 %d 个问题对象：\n" +
-                        "{\"questions\": [{\"id\": \"ai-gen-1\", \"content\": \"问题1内容...\"}, {\"id\": \"ai-gen-2\", \"content\": \"问题2内容...\"}]}\n\n" +
-                        "候选人技术栈：%s\n" +
-                        "候选人简历：%s",
-                count, count, skillsStr, resumeContent
-        );
-
-        ChatDTO chatDTO = new ChatDTO()
-                .setSessionId(sessionId)
-                .setContent(prompt)
-                .setRole(Role.SYSTEM.getValue())
-                .setDataType(CommonConstant.ONE);
-        ChatVO chatVO = chatService.createChat(chatDTO);
-        return JSON.parseObject(chatVO.getContent());
+    public List<QuestionAiRes.Question> generateQuestionsOfAi(String sessionId,
+                                                              List<String> skills,
+                                                              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。" +
+//                        "请根据候选人的以下技术栈、项目经历、简历内容，生成 %d 道有深度和广度的面试题。" +
+//                        "题目应覆盖候选人的主要技术领域，并能考察其解决问题的能力。" +
+//                        "请严格按照以下JSON格式返回，question数组中必须包含 %d 个问题对象：\n" +
+//                        "{\"questions\": [{\"id\": \"ai-gen-1\", \"content\": \"问题1内容...\"}, {\"id\": \"ai-gen-2\", \"content\": \"问题2内容...\"}]}\n\n" +
+//                        "候选人技术栈：%s\n" +
+//                        "候选人简历：%s",
+//                count, count, skillsStr, resumeContent
+//        );
+        Map<String, Object> params = MapUtil.<String, Object>builder()
+                .put("count", count)
+                .put("jobRequirements", jobRequirements)
+                .put("skills", JSONObject.toJSONString(skills))
+                .put("resume", resumeContent)
+                .build();
+        Prompt aiInterviewerPrompt = PromptTemplateUtils.getAiInterviewerPrompt(params);
+        QuestionAiRes.Wrapper entity = openAiChatClient
+                .prompt(aiInterviewerPrompt)
+                .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))
+//                .call()
+//
+//                .content()
+//                ;
+//        ChatDTO chatDTO = new ChatDTO()
+//                .setSessionId(sessionId)
+//                .setContent(prompt)
+//                .setRole(Role.SYSTEM.getValue())
+//                .setDataType(CommonConstant.ONE);
+//        ChatVO chatVO = chatService.createChat(chatDTO);
+        return entity.questions();
    }

    @Override
-    public JSONObject generateQuestionOfLocal(String sessionId, List<Question> questions, List<String> skills, String resumeContent, int count) {
-        String skillsStr = String.join(", ", skills);
-        // 2. 构建发送给AI的提示
-        String prompt = String.format("""
-                你是一位专业的面试官。请根据以下候选人的技术栈、项目经历、简历内容，从提供的题库中，精心挑选出 %d 道最相关的题目进行面试。
-                题目应覆盖候选人的主要技术领域，并能考察其解决问题的能力。
-                要求：
-                1. 题目必须严格从【题库JSON】中选择。
-                2. 挑选的题目应根据候选人的简历内容来抽取。
-                3. 返回一个只包含所选题目ID的JSON数组，格式为：{"question_ids": [1, 5, 23, ...]}。
-                4. 不要返回任何多余的代码，包括markdown形式的代码，我只需要JSON对象，请严格按照api接口形式返回
-                5. 不要返回任何额外的解释或文字，只返回JSON对象。
-                6. 严格按照前后端分离的接口形式返回JSON数据给我，不要返回"```json```"
-                7. 请保证返回数据的完整性，不要返回不完整的数据，否则我的JSON解析会报错！！！
-                
-                【候选人技术栈】：
-                %s
-                【候选人简历】:
-                [%s]
-                【题库JSON】:
-                %s
-                """, count, skillsStr, resumeContent, JSONObject.toJSONString(questions));
-        ChatDTO chatDTO = new ChatDTO()
-                .setSessionId(sessionId)
-                .setContent(prompt)
-                .setRole(Role.SYSTEM.getValue())
-                .setDataType(CommonConstant.ONE);
-        ChatVO chatVO = chatService.createChat(chatDTO);
-        return JSON.parseObject(chatVO.getContent());
+    public List<QuestionAiRes.Question> generateQuestionOfLocal(String sessionId,
+                                                                List<Question> questions,
+                                                                List<String> skills,
+                                                                String jobRequirements,
+                                                                String resumeContent,
+                                                                int count) {
+        Map<String, Object> params = MapUtil.<String, Object>builder()
+                .put("count", count)
+                .put("jobRequirements", jobRequirements)
+                .put("skills", JSONObject.toJSONString(skills))
+                .put("resume", resumeContent)
+                .build();
+
+        Prompt aiInterviewerPrompt = PromptTemplateUtils.getLocalInterviewPrompt(params);
+        QuestionAiRes.Wrapper entity = openAiChatClient.prompt(aiInterviewerPrompt)
+                .advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, sessionId))
+                .call()
+                .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("""
+//                你是一位专业的面试官。请根据以下候选人的技术栈、项目经历、简历内容，从提供的题库中，精心挑选出 %d 道最相关的题目进行面试。
+//                题目应覆盖候选人的主要技术领域，并能考察其解决问题的能力。
+//                要求：
+//                1. 题目必须严格从【题库JSON】中选择。
+//                2. 挑选的题目应根据候选人的简历内容来抽取。
+//                3. 返回一个只包含所选题目ID的JSON数组，格式为：{"question_ids": [1, 5, 23, ...]}。
+//                4. 不要返回任何多余的代码，包括markdown形式的代码，我只需要JSON对象，请严格按照api接口形式返回
+//                5. 不要返回任何额外的解释或文字，只返回JSON对象。
+//                6. 严格按照前后端分离的接口形式返回JSON数据给我，不要返回"```json```"
+//                7. 请保证返回数据的完整性，不要返回不完整的数据，否则我的JSON解析会报错！！！
+//
+//                【候选人技术栈】：
+//                %s
+//                【候选人简历】:
+//                [%s]
+//                【题库JSON】:
+//                %s
+//                """, count, skillsStr, resumeContent, JSONObject.toJSONString(questions));
+//        ChatDTO chatDTO = new ChatDTO()
+//                .setSessionId(sessionId)
+//                .setContent(prompt)
+//                .setRole(Role.SYSTEM.getValue())
+//                .setDataType(CommonConstant.ONE);
+//        ChatVO chatVO = chatService.createChat(chatDTO);
+//        return JSON.parseObject(chatVO.getContent());
    }

    @Override
-    public JSONObject evaluateAnswer(String sessionId, String question, String userAnswer, List<InterviewQuestionProgress> context) {
+    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()))
-                .collect(Collectors.joining("\n---\n"));
-
-        String prompt = "你是一位资深的技术面试官，以严格和深入著称。" +
-                "你需要评估候选人对以下问题的回答。请注意：\n" +
-                "1. 如果回答模糊、不完整或有错误，你可以提出一个具体的追问问题（followUpQuestion）来深入考察，此时'continueAsking'应为true。\n" +
-                "2. 如果回答得很好，则'continueAsking'为false，'followUpQuestion'为空字符串。\n" +
-                "3. 'score'范围为0-100分。\n" +
-                "4. 'feedback'和'suggestions'需要给出专业、有建设性的意见。\n" +
-                "5. 追问最好有限制，不要无限制的向下追问，注意追问是支线而非主线！追问至多3个问题，之后必须切回主线\n" +
-                "请严格按照以下JSON格式返回，不要有任何额外说明：\n" +
-                "{\"feedback\": \"...\", \"suggestions\": \"...\", \"aiAnswer\": \"...\", \"score\": 85.5, \"continueAsking\": false, \"followUpQuestion\": \"...\"}\n\n" +
-                "面试历史上下文：\n" + history + "\n\n" +
-                "当前问题：\n" + question + "\n\n" +
-                "候选人回答：\n" + userAnswer;
-
-        ChatDTO chatDTO = new ChatDTO()
-                .setSessionId(sessionId)
-                .setContent(prompt)
-                .setRole(Role.SYSTEM.getValue())
-                .setDataType(CommonConstant.ONE);
-        ChatVO chatVO = chatService.createChat(chatDTO);
-        return JSON.parseObject(chatVO.getContent());
+//        String history = context.stream()
+//                .map(p -> String.format("Q: %s\nA: %s", p.getQuestionContent(), p.getUserAnswer()))
+//                .collect(Collectors.joining("\n---\n"));
+//
+//        String prompt = "你是一位资深的技术面试官，以严格和深入著称。" +
+//                "你需要评估候选人对以下问题的回答。请注意：\n" +
+//                "1. 如果回答模糊、不完整或有错误，你可以提出一个具体的追问问题（followUpQuestion）来深入考察，此时'continueAsking'应为true。\n" +
+//                "2. 如果回答得很好，则'continueAsking'为false，'followUpQuestion'为空字符串。\n" +
+//                "3. 'score'范围为0-100分。\n" +
+//                "4. 'feedback'和'suggestions'需要给出专业、有建设性的意见。\n" +
+//                "5. 追问最好有限制，不要无限制的向下追问，注意追问是支线而非主线！追问至多3个问题，之后必须切回主线\n" +
+//                "请严格按照以下JSON格式返回，不要有任何额外说明：\n" +
+//                "{\"feedback\": \"...\", \"suggestions\": \"...\", \"aiAnswer\": \"...\", \"score\": 85.5, \"continueAsking\": false, \"followUpQuestion\": \"...\"}\n\n" +
+//                "面试历史上下文：\n" + history + "\n\n" +
+//                "当前问题：\n" + question + "\n\n" +
+//                "候选人回答：\n" + userAnswer;
+//
+//        ChatDTO chatDTO = new ChatDTO()
+//                .setSessionId(sessionId)
+//                .setContent(prompt)
+//                .setRole(Role.SYSTEM.getValue())
+//                .setDataType(CommonConstant.ONE);
+//        ChatVO chatVO = chatService.createChat(chatDTO);
+        Map<String, Object> params = MapUtil.<String, Object>builder()
+                .put("question", question)
+                .put("candidateAnswer", userAnswer)
+                .build();
+        Prompt prompt = PromptTemplateUtils.getEvaluatePrompt(params);
+        return openAiChatClient
+                .prompt(prompt)
+                .advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, sessionId))
+                .call()
+                .entity(EvaluateAiRes.class);
    }

    @Override
-    public JSONObject generateFinalReport(InterviewSession session, List<InterviewQuestionProgress> progressList) {
+    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()))
@@ -148,23 +207,26 @@ public class InterviewAiServiceImpl implements InterviewAiService {
 //                "{\"overallScore\": 88.0, \"summary\": \"...\", \"strengths\": [\"...\"], \"weaknesses\": [\"...\"]}\n\n" +
 //                "候选人姓名：" + session.getCandidateName() + "\n" +
 //                "面试完整记录：\n" + transcript;
-
-        String prompt = buildFinalReportPrompt(session, progressList);
-
-        ChatDTO chatDTO = new ChatDTO()
-                .setRole(Role.SYSTEM.getValue())
-                .setDataType(CommonConstant.ONE)
-                .setContent(prompt);
-        ChatVO chatVO = chatService.createChat(chatDTO);
-        return JSON.parseObject(chatVO.getContent());
+//        ChatDTO chatDTO = new ChatDTO()
+//                .setRole(Role.SYSTEM.getValue())
+//                .setDataType(CommonConstant.ONE)
+//                .setContent(prompt);
+//        ChatVO chatVO = chatService.createChat(chatDTO);
+        Map<String, Object> params = getFinalReportParams(session, progressList);
+        Prompt prompt = PromptTemplateUtils.getFinalReportPrompt(params);
+        return openAiChatClient.prompt(prompt)
+                .advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, UUID.randomUUID().toString().replace("-", "")))
+                .call()
+                .entity(InterviewReportAiRes.class);
    }

-    private String buildFinalReportPrompt(InterviewSession session, List<InterviewQuestionProgress> progressList) {
+    @NotNull
+    private static Map<String, Object> getFinalReportParams(InterviewSession session, List<InterviewQuestionProgress> progressList) {
        StringBuilder historyBuilder = new StringBuilder();
        for (InterviewQuestionProgress progress : progressList) {
            historyBuilder.append(
                    String.format("\n【问题】: %s\n【回答】: %s\n【AI单题反馈】: %s\n【AI单题建议】: %s\n【AI单题评分】: %s/5.0\n",
-                    progress.getQuestionContent(),
+                            progress.getQuestionContent(),
                            progress.getUserAnswer(),
                            progress.getFeedback(),
                            progress.getSuggestions(),
@@ -172,6 +234,27 @@ public class InterviewAiServiceImpl implements InterviewAiService {
                    )
            );
        }
+        return MapUtil.<String, Object>builder()
+                .put("resume", session.getResumeContent())
+                .put("history", historyBuilder.toString())
+                .build();
+    }
+
+    /*
+    private String buildFinalReportPrompt(InterviewSession session, List<InterviewQuestionProgress> progressList) {
+        StringBuilder historyBuilder = new StringBuilder();
+        for (InterviewQuestionProgress progress : progressList) {
+            historyBuilder.append(
+                    String.format("\n【问题】: %s\n【回答】: %s\n【AI单题反馈】: %s\n【AI单题建议】: %s\n【AI单题评分】: %s/5.0\n",
+                            progress.getQuestionContent(),
+                            progress.getUserAnswer(),
+                            progress.getFeedback(),
+                            progress.getSuggestions(),
+                            progress.getScore()
+                    )
+            );
+        }
+

        return String.format("""
                你是一位资深的HR和技术总监。请根据以下候选人的简历、完整的面试问答历史和AI对每一题的初步评估，给出一份全面、专业、有深度的最终面试报告。
@@ -206,22 +289,36 @@ public class InterviewAiServiceImpl implements InterviewAiService {
                %s
                """, session.getResumeContent(), historyBuilder.toString());
    }
+    */

    @Override
    public String generateFirstQuestion(String sessionId, String candidateName, String questionContent) {
+
+//        ChatDTO chatDTO = new ChatDTO()
+//                .setSessionId(sessionId)
+//                .setRole(Role.SYSTEM.getValue())
+//                .setDataType(CommonConstant.ONE)
+//                .setContent(prompt);
+//        ChatVO chatVO = chatService.createChat(chatDTO);
+//        return chatVO.getContent();
        String prompt = String.format("""
                你是一位专业的技术面试官。现在要开始面试，候选人是 %s。
-                
+               \s
                第一个问题是：%s
-                
+               \s
                请以友好但专业的语气提出这个问题，可以适当添加一些引导性的话语。
-                """, candidateName, questionContent);
-        ChatDTO chatDTO = new ChatDTO()
-                .setSessionId(sessionId)
-                .setRole(Role.SYSTEM.getValue())
-                .setDataType(CommonConstant.ONE)
-                .setContent(prompt);
-        ChatVO chatVO = chatService.createChat(chatDTO);
-        return chatVO.getContent();
+                严格按照JSON格式输出，不得包含任何markdown标记或额外解释：
+                请返回JSON格式的数据:\s
+                {
+                    "content": "xxx"
+                }
+               \s""", candidateName, questionContent);
+        String content = openAiChatClient.prompt()
+                .user(prompt)
+                .advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, sessionId))
+                .call()
+                .content();
+        return JSON.parseObject(content).getString("content");
+
    }
 }
--- a/src/main/java/com/qingqiu/interview/service/impl/InterviewChatServiceImpl.java
+++ b/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")) {
            
--- a/src/main/java/com/qingqiu/interview/service/impl/InterviewQuestionProgressServiceImpl.java
+++ b/src/main/java/com/qingqiu/interview/service/impl/InterviewQuestionProgressServiceImpl.java
@@ -37,9 +37,10 @@ public class InterviewQuestionProgressServiceImpl extends ServiceImpl<InterviewQ
                                        InterviewQuestionProgress.Status.COMPLETED.name()
                                )
                        )
+                        .orderByDesc(InterviewQuestionProgress::getCreatedTime)
                        .orderByAsc(InterviewQuestionProgress::getStatus)
                        .orderByDesc(InterviewQuestionProgress::getUpdatedTime)
-                        .orderByDesc(InterviewQuestionProgress::getCreatedTime)
+
                );
    }

--- a/src/main/java/com/qingqiu/interview/service/impl/InterviewServiceImpl.java
+++ b/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;
@@ -12,6 +11,9 @@ import com.qingqiu.interview.dto.InterviewReportResponse;
 import com.qingqiu.interview.dto.InterviewStartRequest;
 import com.qingqiu.interview.dto.SubmitAnswerDTO;
 import com.qingqiu.interview.entity.*;
+import com.qingqiu.interview.entity.ai.EvaluateAiRes;
+import com.qingqiu.interview.entity.ai.InterviewReportAiRes;
+import com.qingqiu.interview.entity.ai.QuestionAiRes;
 import com.qingqiu.interview.mapper.InterviewEvaluationMapper;
 import com.qingqiu.interview.mapper.InterviewMessageMapper;
 import com.qingqiu.interview.mapper.InterviewSessionMapper;
@@ -32,10 +34,9 @@ import org.springframework.transaction.annotation.Transactional;
 import org.springframework.web.multipart.MultipartFile;

 import java.io.IOException;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Objects;
-import java.util.UUID;
+import java.math.BigDecimal;
+import java.util.*;
+import java.util.concurrent.ConcurrentHashMap;
 import java.util.stream.Collectors;

 /**
@@ -61,12 +62,14 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In

    private final DocumentParserManager documentParserManager;

+    private final Map<String, Integer> flowedQuestions = new ConcurrentHashMap<>();
+
    @Override
    @Transactional(rollbackFor = Exception.class)
    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);
@@ -76,13 +79,14 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
        session.setStatus(InterviewSession.Status.ACTIVE.name());
        session.setTotalQuestions(dto.getTotalQuestions());
        session.setModel(dto.getModel());
+        session.setJobRequirements(dto.getJobRequirements());
        this.baseMapper.insert(session); // 先插入以获取ID

        // 2. 调用AI服务从简历提取技能
-        JSONObject skillsJson = aiService.extractSkillsFromResume(resumeContent);
+        List<String> skills = aiService.extractSkillsFromResume(resumeContent);
+
        // ---> 解析AI返回的JSON数据，获取技能列表 <---
-        List<String> skills = skillsJson.getList("skills", String.class);
-        session.setExtractedSkills(skillsJson.toJSONString());
+        session.setExtractedSkills(JSONObject.toJSONString(skills));

        // 3. 准备面试问题（本地 + AI生成）
        if (dto.getModel().equals("local")) {
@@ -94,11 +98,15 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
        // 4. 更新会话信息
        this.baseMapper.updateById(session);
        InterviewQuestionProgress nextQuestion = progressService.getNextQuestion(sessionId);
-        aiService.generateFirstQuestion(session.getSessionId(), session.getCandidateName(), nextQuestion.getQuestionContent());
+        String aiRes = aiService.generateFirstQuestion(session.getSessionId(),
+                session.getCandidateName(),
+                nextQuestion.getQuestionContent()
+        );
        saveMessage(sessionId,
                InterviewMessage.MessageType.QUESTION.name(),
                InterviewMessage.Sender.AI.name(),
-                nextQuestion.getQuestionContent(),
+//                nextQuestion.getQuestionContent(),
+                aiRes,
                nextQuestion.getId()
        );
        return session;
@@ -107,25 +115,23 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In

    private void aiGenerateQuestions(InterviewSession session, List<String> skills) {
        List<InterviewQuestionProgress> progressList = new ArrayList<>();
-        JSONObject aiQuestionsJson = aiService.generateQuestionsOfAi(
+        List<QuestionAiRes.Question> aiQuestionRes = aiService.generateQuestionsOfAi(
                session.getSessionId(),
                skills,
+                session.getJobRequirements(),
                session.getResumeContent(),
                session.getTotalQuestions()
        );
-        // ---> 解析AI返回的JSON数据，获取问题列表 <---
-        JSONArray questions = aiQuestionsJson.getJSONArray("questions");
-        if (questions != null) {
-            questions.forEach(item -> {
-                JSONObject q = (JSONObject) item;
+        if (CollectionUtil.isNotEmpty(aiQuestionRes)) {
+            for (QuestionAiRes.Question aiQuestionRe : aiQuestionRes) {
                InterviewQuestionProgress progress = new InterviewQuestionProgress();
                progress.setSessionId(session.getSessionId());
                progress.setQuestionId(0L); // AI生成的问题没有本地ID
                // ---> 解析单个问题内容 <---
-                progress.setQuestionContent(q.getString("content"));
+                progress.setQuestionContent(aiQuestionRe.content());
                progress.setStatus(InterviewQuestionProgress.Status.DEFAULT.name());
                progressList.add(progress);
-            });
+            }
        }
        // 批量保存问题进度
        if (CollectionUtil.isNotEmpty(progressList)) {
@@ -158,19 +164,19 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
            );
        }
        // ai调用返回的内容进行提取
-        JSONObject jsonObject = aiService.generateQuestionOfLocal(
+        List<QuestionAiRes.Question> questions = aiService.generateQuestionOfLocal(
                session.getSessionId(),
                localQuestionDataList,
                skills,
+                session.getJobRequirements(),
                session.getResumeContent(),
                session.getTotalQuestions()
        );
-        JSONArray questionIds = jsonObject.getJSONArray("question_ids");
-        List<Long> list = questionIds.toList(Long.class);
+        Set<String> resQuestionIds = questions.stream().map(QuestionAiRes.Question::id).collect(Collectors.toSet());
        // 查询返回的内容 并将其保存为问题进度的相关数据
        List<Question> questionList = questionService.list(
                new LambdaQueryWrapper<Question>()
-                        .in(Question::getId, list)
+                        .in(Question::getId, resQuestionIds)
        );
        List<InterviewQuestionProgress> progressList = new ArrayList<>();
        questionList.forEach(q -> {
@@ -256,7 +262,8 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
                        .eq(InterviewQuestionProgress::getStatus, InterviewQuestionProgress.Status.COMPLETED.name())
                        .orderByAsc(InterviewQuestionProgress::getId)
        );
-        JSONObject evalResult = aiService.evaluateAnswer(
+        Integer flowedQuestionCount = this.flowedQuestions.getOrDefault(currentProgress.getSessionId(), 0);
+        EvaluateAiRes evaluateAiRes = aiService.evaluateAnswer(
                currentProgress.getSessionId(),
                currentProgress.getQuestionContent(),
                dto.getAnswer(),
@@ -264,40 +271,45 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
        );

        // 3. ---> 解析AI返回的JSON评估结果并存入数据库 <---
-        currentProgress.setFeedback(evalResult.getString("feedback"));
-        currentProgress.setSuggestions(evalResult.getString("suggestions"));
-        currentProgress.setAiAnswer(evalResult.getString("aiAnswer"));
-        currentProgress.setScore(evalResult.getBigDecimal("score"));
+        currentProgress.setFeedback(evaluateAiRes.feedback());
+        currentProgress.setSuggestions(evaluateAiRes.suggestions());
+        currentProgress.setAiAnswer(evaluateAiRes.aiAnswer());
+        currentProgress.setScore(BigDecimal.valueOf(evaluateAiRes.score()));
        currentProgress.setStatus(InterviewQuestionProgress.Status.COMPLETED.name());
        progressService.updateById(currentProgress);

        // 4. 将单题评估结果存入 evaluation 表用于分析
-        saveEvaluationRecord(currentProgress, evalResult);
+        saveEvaluationRecord(currentProgress, evaluateAiRes);


        // 5. ---> 解析AI的是否追问判断，并处理追问逻辑 <---
-        if (evalResult.getBooleanValue("continueAsking", false)) {
+        if (evaluateAiRes.continueAsking()) {
            // 创建一个新的、状态为ACTIVE的追问问题
            InterviewQuestionProgress followUp = new InterviewQuestionProgress();
            followUp.setSessionId(currentProgress.getSessionId());
            followUp.setQuestionId(0L); // 追问问题没有本地ID
-            followUp.setQuestionContent(evalResult.getString("followUpQuestion"));
+            followUp.setQuestionContent(evaluateAiRes.followUpQuestion());
            followUp.setStatus(InterviewQuestionProgress.Status.ACTIVE.name()); // 直接设为激活状态，作为下一个问题
            progressService.save(followUp);
+            // 记录追问题目数量
+            flowedQuestionCount++;
+            this.flowedQuestions.put(currentProgress.getSessionId(), flowedQuestionCount);
            return followUp; // 将这个新的追问问题返回给前端
        }
+        // 清空追问题目数量
+        this.flowedQuestions.put(currentProgress.getSessionId(), 0);

        return currentProgress;
    }

-    private void saveEvaluationRecord(InterviewQuestionProgress progress, JSONObject evalResult) {
+    private void saveEvaluationRecord(InterviewQuestionProgress progress, EvaluateAiRes evaluateAiRes) {
        InterviewEvaluation evaluation = new InterviewEvaluation();
        evaluation.setSessionId(progress.getSessionId());
        evaluation.setQuestionId(progress.getQuestionId());
        evaluation.setUserAnswer(progress.getUserAnswer());
        // ---> 解析AI评估结果并存入分析表 <---
-        evaluation.setAiFeedback(evalResult.getString("feedback"));
-        evaluation.setScore(evalResult.getBigDecimal("score"));
+        evaluation.setAiFeedback(evaluateAiRes.feedback());
+        evaluation.setScore(BigDecimal.valueOf(evaluateAiRes.score()));
        evaluationMapper.insert(evaluation);
    }

@@ -319,12 +331,12 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
        }

        // 2. 调用AI服务生成最终报告
-        JSONObject finalReportJson = aiService.generateFinalReport(session, completedProgresses);
+        InterviewReportAiRes interviewReportAiRes = aiService.generateFinalReport(session, completedProgresses);

        // 3. ---> 解析AI返回的最终报告JSON，更新会话状态 <---
        session.setStatus(InterviewSession.Status.COMPLETED.name());
-        session.setScore(finalReportJson.getBigDecimal("overallScore"));
-        session.setFinalReport(finalReportJson.toJSONString());
+        session.setScore(new BigDecimal(interviewReportAiRes.overallScore()));
+        session.setFinalReport(JSONObject.toJSONString(interviewReportAiRes));
        this.baseMapper.updateById(session);

        return session;
@@ -387,7 +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
@@ -398,7 +411,7 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In


    private InterviewMessage saveMessage(String sessionId, String messageType, String sender,
-                             String content, Long questionId) {
+                                         String content, Long questionId) {
        int nextOrder = messageMapper.selectMaxOrderBySessionId(sessionId) + 1;
        InterviewMessage message = new InterviewMessage()
                .setSessionId(sessionId)
--- a/src/main/java/com/qingqiu/interview/service/impl/QuestionClassificationServiceImpl.java
+++ b/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));
+    }
+}
--- a/src/main/java/com/qingqiu/interview/service/impl/QuestionServiceImpl.java
+++ b/src/main/java/com/qingqiu/interview/service/impl/QuestionServiceImpl.java
@@ -1,29 +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.llm.LlmService;
 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;
@@ -39,11 +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 LlmService llmService;
    private final IQuestionCategoryService questionCategoryService;

+    private final ChatClient chatClient;
+
    /**
     * 分页查询题库
     */
@@ -98,19 +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);
-                questionMapper.insert(question);
+                validateQuestion(item.content(), null);
+                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);
    }

    /**
@@ -129,16 +137,18 @@ public class QuestionServiceImpl extends ServiceImpl<QuestionMapper, Question> i
            return;
        }
        String prompt = getPrompt(questions);
-        log.info("发送内容: {}", prompt);
-        // 验证token上下文长度
-        Integer promptTokens = llmService.getPromptTokens(prompt);
-        log.info("当前prompt长度: {}", promptTokens);
-        String chat = llmService.chat(prompt, LLMProvider.DEEPSEEK);
+
+
+        QuestionDeduplicationAiRes entity = chatClient.prompt().user(prompt).call().entity(QuestionDeduplicationAiRes.class);
+        assert entity != null;
        // 调用AI
-        log.info("AI返回内容: {}", chat);
-        JSONObject parse = JSONObject.parse(chat);
-        JSONArray questionsIds = parse.getJSONArray("questions");
-        List<Long> list = questionsIds.toList(Long.class);
+        log.info("AI返回内容: {}", JSONObject.toJSONString(entity));
+        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(
                new LambdaQueryWrapper<Question>()
                        .notIn(Question::getId, list)
@@ -157,25 +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】
-                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。
-                请按照下述格式返回，已被剔除掉的数据无需返回
-                {
-                  "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());
+
+        return PromptTemplate.builder()
+                .renderer(
+                        StTemplateRenderer.builder()
+                                .startDelimiterToken('<')
+                                .endDelimiterToken('>')
+                                .build()
+                )
+                .template("""
+                        请对以下题库JSON数据进行智能去重处理。
+                        
+                        ## 任务说明
+                        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。
+                        
+                        ## 语义相似度判断标准
+                        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
+                        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());
    }

    /**
@@ -211,7 +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,
--- a/src/main/java/com/qingqiu/interview/service/llm/LlmService.java
+++ b/src/main/java/com/qingqiu/interview/service/llm/LlmService.java
@@ -1,35 +0,0 @@
-package com.qingqiu.interview.service.llm;
-
-import com.qingqiu.interview.common.enums.LLMProvider;
-
-public interface LlmService {
-
-
-    /**
-     * 与模型进行单轮对话
-     * @param prompt 提示词
-     * @return ai回复
-     */
-    String chat(String prompt);
-    String chat(String prompt, LLMProvider provider);
-
-    /**
-     * 与模型进行多轮对话
-     * @param prompt 提示词
-     * @param token 会话token
-     * @return ai回复
-     */
-    String chat(String prompt, String token);
-
-    /**
-     * 与模型进行多轮对话 指定模型
-     * @param prompt 提示词
-     * @param model 模型名称
-     * @param token 会话token
-     * @return ai回复
-     */
-    String chat(String prompt, String token, LLMProvider provider);
-
-    Integer getPromptTokens(String prompt);
-}
-
--- a/src/main/java/com/qingqiu/interview/service/llm/qwen/QwenService.java
+++ b/src/main/java/com/qingqiu/interview/service/llm/qwen/QwenService.java
@@ -1,191 +0,0 @@
-package com.qingqiu.interview.service.llm.qwen;
-
-import cn.hutool.core.collection.CollectionUtil;
-import com.alibaba.dashscope.aigc.generation.Generation;
-import com.alibaba.dashscope.common.Message;
-import com.alibaba.dashscope.common.Role;
-import com.alibaba.dashscope.tokenizers.Tokenizer;
-import com.alibaba.dashscope.tokenizers.TokenizerFactory;
-import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
-import com.qingqiu.interview.common.enums.LLMProvider;
-import com.qingqiu.interview.ai.factory.AIClientManager;
-import com.qingqiu.interview.entity.AiSessionLog;
-import com.qingqiu.interview.mapper.AiSessionLogMapper;
-import com.qingqiu.interview.service.llm.LlmService;
-import lombok.RequiredArgsConstructor;
-import lombok.extern.slf4j.Slf4j;
-import org.springframework.beans.factory.annotation.Value;
-import org.springframework.stereotype.Service;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.stream.Collectors;
-
-import static com.qingqiu.interview.common.utils.AIUtils.createMessage;
-
-@Slf4j
-@Service("qwenService")
-@RequiredArgsConstructor
-public class QwenService implements LlmService {
-
-    private final Generation generation;
-
-    private final AiSessionLogMapper aiSessionLogMapper;
-
-    @Value("${dashscope.api-key}")
-    private String apiKey;
-
-    private final AIClientManager aiClientManager;
-
-
-    @Override
-    public String chat(String prompt) {
-//        log.info("开始调用API....");
-//        long l = System.currentTimeMillis();
-        return chat(prompt, LLMProvider.DEEPSEEK);
-//        GenerationParam param = GenerationParam.builder()
-//                .model(DEEPSEEK_3) // 可根据需要更换模型
-//                .messages(Collections.singletonList(createMessage(Role.USER.getValue(), prompt)))
-//                .resultFormat(GenerationParam.ResultFormat.MESSAGE)
-//                .apiKey(apiKey)
-//                .build();
-//
-//        GenerationResult result = null;
-//        try {
-//            result = generation.call(param);
-//            log.info("调用成功，耗时: {} ms", System.currentTimeMillis() - l);
-//            log.debug("响应结果: {}", result.getOutput().getChoices().get(0).getMessage().getContent());
-//            return result.getOutput().getChoices().get(0).getMessage().getContent();
-//        } catch (ApiException | InputRequiredException e) {
-//            throw new RuntimeException("调用AI服务失败", e);
-//        } catch (NoApiKeyException e) {
-//            throw new RuntimeException("请检查API密钥是否正确", e);
-//        }
-    }
-
-    @Override
-    public String chat(String prompt, LLMProvider provider) {
-        return aiClientManager.getClient(provider).chatCompletion(prompt);
-    }
-
-    @Override
-    public String chat(String prompt, String token) {
-        return chat(prompt, token, LLMProvider.DEEPSEEK);
-
-//        // 调用AI模型
-//        try {
-//            log.info("开始调用API....");
-//            long l = System.currentTimeMillis();
-//            GenerationParam param = GenerationParam.builder()
-//                    .model(DEEPSEEK_3_1) // 可根据需要更换模型
-//                    .messages(messages)
-//                    .resultFormat(GenerationParam.ResultFormat.MESSAGE)
-//                    .apiKey(apiKey)
-//                    .build();
-//
-//            GenerationResult result = generation.call(param);
-//            log.info("调用成功，耗时: {} ms", System.currentTimeMillis() - l);
-//            String aiResponse = result.getOutput().getChoices().get(0).getMessage().getContent();
-//            log.debug("响应结果: {}", aiResponse);
-//            // 存储用户提问
-//            AiSessionLog userLog = new AiSessionLog();
-//            userLog.setToken(token);
-//            userLog.setRole(Role.USER.getValue());
-//            userLog.setContent(prompt);
-//            aiSessionLogMapper.insert(userLog);
-//
-//            // 存储AI回复
-//            AiSessionLog aiLog = new AiSessionLog();
-//            aiLog.setToken(token);
-//            aiLog.setRole(Role.ASSISTANT.getValue());
-//            aiLog.setContent(aiResponse);
-//            aiSessionLogMapper.insert(aiLog);
-//
-//            return aiResponse;
-//        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
-//            throw new RuntimeException("调用AI服务失败", e);
-//        }
-    }
-
-    @Override
-    public String chat(String prompt, String token, LLMProvider provider) {
-        // 根据token查询会话记录
-        List<AiSessionLog> aiSessionLogs = aiSessionLogMapper.selectList(
-                new LambdaQueryWrapper<AiSessionLog>()
-                        .eq(AiSessionLog::getToken, token)
-                        .orderByDesc(AiSessionLog::getCreatedTime)
-        );
-        // 构造发给ai的消息
-        List<Message> messages = new ArrayList<>();
-        if (CollectionUtil.isNotEmpty(aiSessionLogs)) {
-            // 预估tokens
-            StringBuilder sb = new StringBuilder();
-            for (AiSessionLog aiSessionLog : aiSessionLogs) {
-                sb.append(aiSessionLog.getContent());
-            }
-            // 加上本次对话内容
-            sb.append(prompt);
-            Integer promptTokens = getPromptTokens(sb.toString());
-            // 如果token大于了模型上限，则执行丢弃操作
-            int size = aiSessionLogs.size();
-            log.info("当前会话id: {}, tokens: {}", token, promptTokens);
-
-            // 假设模型上限为30000个token（根据实际模型调整）
-            int maxTokens = 100000;
-            if (promptTokens > maxTokens) {
-                // 需要丢弃30%的会话记录（按时间倒序，丢弃最旧的）
-                int discardCount = (int) (size * 0.3);
-                // 从当前会话记录列表中移除旧的会话记录，而不是删除数据库中的记录
-                for (int i = 0; i < discardCount; i++) {
-                    aiSessionLogs.remove(aiSessionLogs.size() - 1);
-                }
-            }
-            // 移除旧记录后再按时间正序排序（最旧的在前面，最新的在后面）
-            aiSessionLogs = aiSessionLogs.stream()
-                    .sorted((log1, log2) -> log1.getCreatedTime().compareTo(log2.getCreatedTime()))
-                    .collect(Collectors.toList());
-            for (AiSessionLog aiSessionLog : aiSessionLogs) {
-                messages.add(
-                        createMessage(aiSessionLog.getRole(), aiSessionLog.getContent())
-                );
-            }
-
-
-        }
-
-        messages.add(
-                createMessage(Role.USER.getValue(), prompt)
-        );
-
-        String aiResponse = aiClientManager.getClient(provider).chatCompletion(messages);
-        // 存储用户提问
-        AiSessionLog userLog = new AiSessionLog();
-        userLog.setToken(token);
-        userLog.setRole(Role.USER.getValue());
-        userLog.setContent(prompt);
-        aiSessionLogMapper.insert(userLog);
-
-        // 存储AI回复
-        AiSessionLog aiLog = new AiSessionLog();
-        aiLog.setToken(token);
-        aiLog.setRole(Role.ASSISTANT.getValue());
-        aiLog.setContent(aiResponse);
-        aiSessionLogMapper.insert(aiLog);
-        return aiResponse;
-    }
-
-    /**
-     * 获取prompt的token数
-     *
-     * @param prompt 输入
-     * @return tokens
-     */
-    @Override
-    public Integer getPromptTokens(String prompt) {
-        Tokenizer tokenizer = TokenizerFactory.qwen();
-        List<Integer> integers = tokenizer.encodeOrdinary(prompt);
-        return integers.size();
-    }
-
-}
-
--- a/src/main/resources/application.yml
+++ b/src/main/resources/application.yml
@@ -1,14 +1,43 @@
-dashscope:
-  api-key: sk-58d6fed688c54e8db02e6a7ffbfc7a5f
-deepseek:
-  api-url: https://api.deepseek.com/chat/completions
-  api-key: sk-faaa2a1b485442ccbf115ff1271a3480
 spring:
  datasource:
    url: jdbc:mysql://gz-cynosdbmysql-grp-5ai5zw7r.sql.tencentcdb.com:24944/ai_interview?createDatabaseIfNotExist=true&useUnicode=true&characterEncoding=utf8&serverTimezone=GMT%2B8
    username: qingqiu
    password: 020979hP
    driver-class-name: com.mysql.cj.jdbc.Driver
+  ai:
+    openai:
+      base-url: https://api.ruyun.fun
+      api-key: ${RUYUN_API_KEY}
+      chat:
+        options:
+          model: gemini-2.5-flash-nothinking
+    dashscope:
+      api-key: ${DASHSCOPE_API_KEY}
+      read-timeout: 600
+      chat:
+        options:
+          model: qwen3-max
+    memory:
+      redis:
+        host: 127.0.0.1
+        port: 6379
+        password: 123456
+        timeout: 6000
+  data:
+    redis:
+      host: localhost
+      port: 6379
+      password: 123456
+      database: 0
+      timeout: 6000
+      jedis:
+        pool:
+          max-active: 16
+          max-idle: 8
+          min-idle: 0
+          max-wait: -1ms
+
+# TODO: 考虑删除已注释的配置
 #  ai:
 #    openai:
 #      api-key: sk-faaa2a1b485442ccbf115ff1271a3480
@@ -16,6 +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
@@ -24,4 +61,4 @@ mybatis-plus:
    db-config:
      logic-delete-field: deleted # 全局逻辑删除字段名
      logic-delete-value: 1 # 逻辑已删除值。可选，默认值为 1
-      logic-not-delete-value: 0 # 逻辑未删除值。可选，默认值为 0
+      logic-not-delete-value: 0 # 逻辑未删除值。可选，默认值为 0
Author	SHA1	Message	Date
qingqiu	80dcb23bbc	使用spring-ai-alibaba重构项目	2025-11-24 13:37:37 +08:00
qingqiu	5fb4ed754c	使用spring-ai-alibaba重构项目	2025-10-22 19:53:26 +08:00
qingqiu	fac1346104	使用spring-ai-alibaba重构项目	2025-10-10 18:03:16 +08:00