新增App，集成客服咨询和商品信息抽取功能，并实现基于文件和基于内存的两种对话记忆持久化方案。

pom.xml

@@ -52,6 +52,18 @@
             <artifactId>knife4j-openapi3-jakarta-spring-boot-starter</artifactId>
             <version>4.4.0</version>
         </dependency>
+        <!-- 支持结构化输出 -->
+        <dependency>
+            <groupId>com.github.victools</groupId>
+            <artifactId>jsonschema-generator</artifactId>
+            <version>4.38.0</version>
+        </dependency>
+        <!-- 支持文件会话记忆持久化的序列化 -->
+        <dependency>
+            <groupId>com.esotericsoftware</groupId>
+            <artifactId>kryo</artifactId>
+            <version>5.6.2</version>
+        </dependency>
         <dependency>
             <groupId>org.projectlombok</groupId>
             <artifactId>lombok</artifactId>

src/main/java/com/wok/supportbot/advisor/MyLoggerAdvisor.java

@@ -0,0 +1,62 @@
+package com.wok.supportbot.advisor;
+
+import lombok.extern.slf4j.Slf4j;
+import reactor.core.publisher.Flux;
+
+import org.springframework.ai.chat.client.advisor.api.AdvisedRequest;
+import org.springframework.ai.chat.client.advisor.api.AdvisedResponse;
+import org.springframework.ai.chat.client.advisor.api.CallAroundAdvisor;
+import org.springframework.ai.chat.client.advisor.api.CallAroundAdvisorChain;
+import org.springframework.ai.chat.client.advisor.api.StreamAroundAdvisor;
+import org.springframework.ai.chat.client.advisor.api.StreamAroundAdvisorChain;
+import org.springframework.ai.chat.model.MessageAggregator;
+
+/**
+ * 自定义日志 Advisor
+ * 打印 info 级别日志、只输出单次用户提示词和 AI 回复的文本
+ */
+@Slf4j
+public class MyLoggerAdvisor implements CallAroundAdvisor, StreamAroundAdvisor {
+
+	@Override
+	public String getName() {
+		return this.getClass().getSimpleName();
+	}
+
+	@Override
+	public int getOrder() {
+		return 0;
+	}
+
+	private AdvisedRequest before(AdvisedRequest request) {
+		log.info("AI Request: {}", request.userText());
+		return request;
+	}
+
+	private void observeAfter(AdvisedResponse advisedResponse) {
+		log.info("AI Response: {}", advisedResponse.response().getResult().getOutput().getText());
+	}
+
+	@Override
+	public AdvisedResponse aroundCall(AdvisedRequest advisedRequest, CallAroundAdvisorChain chain) {
+
+		advisedRequest = before(advisedRequest);
+
+		AdvisedResponse advisedResponse = chain.nextAroundCall(advisedRequest);
+
+		observeAfter(advisedResponse);
+
+		return advisedResponse;
+	}
+
+	@Override
+	public Flux<AdvisedResponse> aroundStream(AdvisedRequest advisedRequest, StreamAroundAdvisorChain chain) {
+
+		advisedRequest = before(advisedRequest);
+
+		Flux<AdvisedResponse> advisedResponses = chain.nextAroundStream(advisedRequest);
+
+		return new MessageAggregator().aggregateAdvisedResponse(advisedResponses, this::observeAfter);
+	}
+
+}

src/main/java/com/wok/supportbot/advisor/ReReadingAdvisor.java

@@ -0,0 +1,53 @@
+package com.wok.supportbot.advisor;
+
+import org.springframework.ai.chat.client.advisor.api.*;
+import reactor.core.publisher.Flux;
+
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * 自定义 Re2 Advisor
+ * 可提高大型语言模型的推理能力
+ */
+public class ReReadingAdvisor implements CallAroundAdvisor, StreamAroundAdvisor {
+
+	/**
+	 * 执行请求前，改写 Prompt
+	 * @param advisedRequest
+	 * @return
+	 */
+	private AdvisedRequest before(AdvisedRequest advisedRequest) {
+
+		Map<String, Object> advisedUserParams = new HashMap<>(advisedRequest.userParams());
+		advisedUserParams.put("re2_input_query", advisedRequest.userText());
+
+		return AdvisedRequest.from(advisedRequest)
+			.userText("""
+			    {re2_input_query}
+			    Read the question again: {re2_input_query}
+			    """)
+			.userParams(advisedUserParams)
+			.build();
+	}
+
+	@Override
+	public AdvisedResponse aroundCall(AdvisedRequest advisedRequest, CallAroundAdvisorChain chain) {
+		return chain.nextAroundCall(this.before(advisedRequest));
+	}
+
+	@Override
+	public Flux<AdvisedResponse> aroundStream(AdvisedRequest advisedRequest, StreamAroundAdvisorChain chain) {
+		return chain.nextAroundStream(this.before(advisedRequest));
+	}
+
+	@Override
+	public int getOrder() { 
+		return 0;
+	}
+
+    @Override
+    public String getName() { 
+		return this.getClass().getSimpleName();
+	}
+}

src/main/java/com/wok/supportbot/app/AssistantApp.java

@@ -0,0 +1,76 @@
+package com.wok.supportbot.app;
+
+import com.wok.supportbot.advisor.MyLoggerAdvisor;
+import com.wok.supportbot.advisor.ReReadingAdvisor;
+import com.wok.supportbot.record.AssistantReport;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
+import org.springframework.ai.chat.memory.ChatMemory;
+import org.springframework.ai.chat.memory.InMemoryChatMemory;
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.chat.model.ChatResponse;
+import org.springframework.stereotype.Component;
+
+import java.util.List;
+
+import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_CONVERSATION_ID_KEY;
+import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_RETRIEVE_SIZE_KEY;
+
+/**
+ * @Classname AssistantApp
+ * @Description
+ * @Version 1.0.0
+ * @Date 2025/06/27 14:11
+ * @Author lyx
+ */
+@Component
+@Slf4j
+public class AssistantApp {
+
+    private final ChatClient chatClient;
+
+    private static final String SYSTEM_PROMPT = "你是一名电商平台的智能客服助手，负责解答用户关于商品、订单、支付、物流和售后等问题。" +
+            "请主动引导用户提供关键信息（如订单号、商品名），并尽量在不转人工的情况下解决问题。保持专业、耐心、礼貌。";
+
+    /**
+     * 初始化 ChatClient
+     * @param dashscopeChatModel
+     */
+    public AssistantApp(ChatModel dashscopeChatModel) {
+        // 初始化基于文件的对话记忆
+        //String fileDir = System.getProperty("user.dir") + "/tmp/chat-memory";
+        //ChatMemory chatMemory = new FileBasedChatMemory(fileDir);
+        // 初始化基于内存的对话记忆
+        ChatMemory chatMemory = new InMemoryChatMemory();
+        chatClient = ChatClient.builder(dashscopeChatModel)
+                .defaultSystem(SYSTEM_PROMPT)
+                .defaultAdvisors(
+                        new MessageChatMemoryAdvisor(chatMemory),
+                        // 自定义日志 Advisor，可按需开启
+                        new MyLoggerAdvisor()
+                        // 自定义推理增强 Advisor，可按需开启
+                        //,new ReReadingAdvisor()
+                )
+                .build();
+    }
+
+    /**
+     * AI 基础对话（支持多轮对话记忆）
+     * @param message
+     * @param chatId
+     * @return
+     */
+    public String doChat(String message, String chatId) {
+        ChatResponse chatResponse = chatClient
+                .prompt()
+                .user(message)
+                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
+                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
+                .call()
+                .chatResponse();
+        String content = chatResponse.getResult().getOutput().getText();
+        //log.info("content: {}", content);
+        return content;
+    }
+}

src/main/java/com/wok/supportbot/app/ProductInfoApp.java

@@ -0,0 +1,64 @@
+package com.wok.supportbot.app;
+
+import com.wok.supportbot.advisor.MyLoggerAdvisor;
+import com.wok.supportbot.record.ProductInfo;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
+import org.springframework.ai.chat.memory.ChatMemory;
+import org.springframework.ai.chat.memory.InMemoryChatMemory;
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.chat.model.ChatResponse;
+import org.springframework.stereotype.Component;
+
+import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_CONVERSATION_ID_KEY;
+import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_RETRIEVE_SIZE_KEY;
+
+/**
+ * @Classname ProductInfoApp
+ * @Description 电商商品信息抽取助手App
+ * @Version 1.0.0
+ * @Date 2025/06/27
+ * @Author lyx
+ */
+@Component
+@Slf4j
+public class ProductInfoApp {
+
+    private final ChatClient chatClient;
+
+    private static final String SYSTEM_PROMPT = "你是一名电商商品信息抽取助手，" +
+            "请从用户提供的商品网页内容中提取标题(title)、描述(description)、价格(price)、评分(rating)、评论数(reviewCount)、品牌(brand)、分类(category)等字段。" +
+            "请严格按照JSON格式返回，不要带任何解释和多余内容。";
+
+    public ProductInfoApp(ChatModel dashscopeChatModel) {
+        ChatMemory chatMemory = new InMemoryChatMemory();
+        chatClient = ChatClient.builder(dashscopeChatModel)
+                .defaultSystem(SYSTEM_PROMPT)
+                .defaultAdvisors(
+                        new MessageChatMemoryAdvisor(chatMemory),
+                        // 可以按需添加日志或其它advisor
+                        new MyLoggerAdvisor()
+                )
+                .build();
+    }
+
+    /**
+     * 商品信息结构化抽取
+     * @param rawContent 爬取的商品网页内容
+     * @param chatId 对话ID
+     * @return 结构化的商品信息对象
+     */
+    public ProductInfo extractProductInfo(String rawContent, String chatId) {
+        ProductInfo productInfo = chatClient
+                .prompt()
+                .system(SYSTEM_PROMPT)
+                .user(rawContent)
+                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
+                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
+                .call()
+                .entity(ProductInfo.class);
+        log.info("Extracted product info: {}", productInfo);
+        return productInfo;
+    }
+}

src/main/java/com/wok/supportbot/chatmemory/FileBasedChatMemory.java

@@ -0,0 +1,88 @@
+package com.wok.supportbot.chatmemory;
+
+import com.esotericsoftware.kryo.Kryo;
+import com.esotericsoftware.kryo.io.Input;
+import com.esotericsoftware.kryo.io.Output;
+import org.objenesis.strategy.StdInstantiatorStrategy;
+import org.springframework.ai.chat.memory.ChatMemory;
+import org.springframework.ai.chat.messages.Message;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * 基于文件持久化的对话记忆
+ */
+public class FileBasedChatMemory implements ChatMemory {
+
+    private final String BASE_DIR;
+    private static final Kryo kryo = new Kryo();
+
+    static {
+        kryo.setRegistrationRequired(false);
+        // 设置实例化策略
+        kryo.setInstantiatorStrategy(new StdInstantiatorStrategy());
+    }
+
+    // 构造对象时，指定文件保存目录
+    public FileBasedChatMemory(String dir) {
+        this.BASE_DIR = dir;
+        File baseDir = new File(dir);
+        if (!baseDir.exists()) {
+            baseDir.mkdirs();
+        }
+    }
+
+    @Override
+    public void add(String conversationId, List<Message> messages) {
+        List<Message> conversationMessages = getOrCreateConversation(conversationId);
+        conversationMessages.addAll(messages);
+        saveConversation(conversationId, conversationMessages);
+    }
+
+    @Override
+    public List<Message> get(String conversationId, int lastN) {
+        List<Message> allMessages = getOrCreateConversation(conversationId);
+        return allMessages.stream()
+                .skip(Math.max(0, allMessages.size() - lastN))
+                .toList();
+    }
+
+    @Override
+    public void clear(String conversationId) {
+        File file = getConversationFile(conversationId);
+        if (file.exists()) {
+            file.delete();
+        }
+    }
+
+    private List<Message> getOrCreateConversation(String conversationId) {
+        File file = getConversationFile(conversationId);
+        List<Message> messages = new ArrayList<>();
+        if (file.exists()) {
+            try (Input input = new Input(new FileInputStream(file))) {
+                messages = kryo.readObject(input, ArrayList.class);
+            } catch (IOException e) {
+                e.printStackTrace();
+            }
+        }
+        return messages;
+    }
+
+    private void saveConversation(String conversationId, List<Message> messages) {
+        File file = getConversationFile(conversationId);
+        try (Output output = new Output(new FileOutputStream(file))) {
+            kryo.writeObject(output, messages);
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+    }
+
+    private File getConversationFile(String conversationId) {
+        return new File(BASE_DIR, conversationId + ".kryo");
+    }
+}

src/main/java/com/wok/supportbot/demo/invoke/SpringAiAiInvoke.java

@@ -18,7 +18,7 @@ public class SpringAiAiInvoke implements CommandLineRunner {
 
     @Override
     public void run(String... args) throws Exception {
-        AssistantMessage assistantMessage = dashscopeChatModel.call(new Prompt("你好，我在测试我能否用Spring AI来调用某个AI模型"))
+        AssistantMessage assistantMessage = dashscopeChatModel.call(new Prompt("hello"))
                 .getResult()
                 .getOutput();
         System.out.println(assistantMessage.getText());

src/main/java/com/wok/supportbot/record/AssistantReport.java

@@ -0,0 +1,14 @@
+package com.wok.supportbot.record;
+
+import java.util.List;
+
+/**
+ * @Classname AssistantReport
+ * @Description
+ * @Version 1.0.0
+ * @Date 2025/06/27 14:21
+ * @Author lyx
+ */
+
+public record AssistantReport(String title, List<String> suggestions) {
+}

src/main/java/com/wok/supportbot/record/ProductInfo.java

@@ -0,0 +1,14 @@
+package com.wok.supportbot.record;
+
+import lombok.Data;
+
+@Data
+public class ProductInfo {
+    private String title;
+    private String description;
+    private String price;
+    private String rating;
+    private Integer reviewCount;
+    private String brand;
+    private String category;
+}

src/test/java/com/wok/supportbot/SupportBotApplicationTests.java

@@ -1,13 +1,73 @@
 package com.wok.supportbot;
 
+import com.wok.supportbot.app.AssistantApp;
+import com.wok.supportbot.app.ProductInfoApp;
+import com.wok.supportbot.record.AssistantReport;
+import com.wok.supportbot.record.ProductInfo;
+import jakarta.annotation.Resource;
+import org.junit.jupiter.api.Assertions;
 import org.junit.jupiter.api.Test;
+import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.boot.test.context.SpringBootTest;
 
+import java.util.UUID;
+
 @SpringBootTest
 class SupportBotApplicationTests {
+    @Resource
+    private AssistantApp assistantApp;
+
+    @Autowired
+    private ProductInfoApp productInfoApp;
 
     @Test
-    void contextLoads() {
+    void testChat() {
+        String chatId = UUID.randomUUID().toString();
+        // 第一轮：商品咨询
+        String message = "你好，我想买一台适合学生用的笔记本电脑，有推荐吗？";
+        String answer = assistantApp.doChat(message, chatId);
+        Assertions.assertNotNull(answer);
+
+        // 第二轮：物流问题
+        message = "我上周买的那台电脑现在还没到，能查一下物流吗？";
+        answer = assistantApp.doChat(message, chatId);
+        Assertions.assertNotNull(answer);
+
+        // 第三轮：售后问题
+        message = "电脑到了，但有点问题。你刚刚说的售后流程能再说一遍吗？";
+        answer = assistantApp.doChat(message, chatId);
+        Assertions.assertNotNull(answer);
     }
 
+    @Test
+    public void testExtractProductInfo() {
+        // 模拟爬取的网页内容，建议写简洁但包含关键信息
+        String rawContent = "这是商品标题：智能手表Pro 2025，" +
+                "描述：这款智能手表支持心率监测和GPS，" +
+                "价格：299美元，评分：4.7星，评论数：1567，品牌：TechBrand，分类：电子产品。";
+
+        // 生成随机聊天ID，模拟独立会话
+        String chatId = UUID.randomUUID().toString();
+
+        // 调用方法
+        ProductInfo productInfo = productInfoApp.extractProductInfo(rawContent, chatId);
+
+        // 断言结果不为空
+        Assertions.assertNotNull(productInfo);
+
+        // 断言关键字段合理（你也可以根据实际字段调整）
+        Assertions.assertNotNull(productInfo.getTitle());
+        Assertions.assertTrue(productInfo.getTitle().contains("智能手表"));
+
+        Assertions.assertNotNull(productInfo.getPrice());
+        Assertions.assertTrue(productInfo.getPrice().contains("299"));
+
+        Assertions.assertNotNull(productInfo.getBrand());
+        Assertions.assertEquals("TechBrand", productInfo.getBrand());
+
+        // 你可以打印结果，方便调试
+        System.out.println("提取的商品信息: " + productInfo);
+    }
+
+
 }