<properties>
        <maven.compiler.source>17</maven.compiler.source>
        <maven.compiler.target>17</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <spring-boot.version>3.2.6</spring-boot.version>
        <knife4j.version>4.3.0</knife4j.version>
        <langchain4j.version>1.0.0-beta3</langchain4j.version>
        <mybatis-plus.version>3.5.11</mybatis-plus.version>
        <!-- 添加 Logback 版本屬性，使用兼容 SLF4J 2.x 的版本 -->
        <logback.version>1.4.12</logback.version>
        <!-- 添加兼容 Spring Boot 3.2.6 的 Tomcat 版本 -->
        <tomcat.version>10.1.24</tomcat.version>
    </properties>
    <dependencies>
        <!-- Pinecone Java 客戶端 -->
        <dependency>
            <groupId>io.pinecone</groupId>
            <artifactId>pinecone-client</artifactId>
            <version>5.0.0</version>
        </dependency>
        <!-- JSON 處理 -->
        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-databind</artifactId>
            <version>2.15.2</version>
        </dependency>
        <!-- web應(yīng)用程序核心依賴 -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
            <exclusions>
                <!-- 排除 javax.servlet 依賴，避免與 jakarta.servlet 沖突 -->
                <exclusion>
                    <groupId>javax.servlet</groupId>
                    <artifactId>javax.servlet-api</artifactId>
                </exclusion>
            </exclusions>
        </dependency>
        <!-- 添加 jakarta.servlet-api 依賴 -->
        <dependency>
            <groupId>jakarta.servlet</groupId>
            <artifactId>jakarta.servlet-api</artifactId>
            <scope>provided</scope>
        </dependency>
        <!-- 編寫(xiě)和運(yùn)行測(cè)試用例 -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>
        <!-- 基于open-ai的langchain4j接口：ChatGPT、deepseek都是open-ai標(biāo)準(zhǔn)下的大模型 -->
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
        </dependency>
        <!-- 接入ollama -->
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-ollama-spring-boot-starter</artifactId>
        </dependency>
        <!-- 前后端分離中的后端接口測(cè)試工具 -->
        <dependency>
            <groupId>com.github.xiaoymin</groupId>
            <artifactId>knife4j-openapi3-jakarta-spring-boot-starter</artifactId>
            <version>${knife4j.version}</version>
        </dependency>
        <!-- 接入阿里云百煉平臺(tái) -->
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId>
        </dependency>
        <!-- langchain4j高級(jí)功能 -->
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-spring-boot-starter</artifactId>
        </dependency>
        <!-- Spring Boot Starter Data MongoDB -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-mongodb</artifactId>
        </dependency>
        <!-- Mysql Connector -->
        <dependency>
            <groupId>com.mysql</groupId>
            <artifactId>mysql-connector-j</artifactId>
            <version>8.3.0</version>
        </dependency>
        <!-- mybatis-plus 持久層 -->
        <dependency>
            <groupId>com.baomidou</groupId>
            <artifactId>mybatis-plus-spring-boot3-starter</artifactId>
            <version>${mybatis-plus.version}</version>
        </dependency>
        <!-- 解析pdf文檔 -->
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-document-parser-apache-pdfbox</artifactId>
            <version>1.0.0-beta3</version>
        </dependency>
        <!-- 簡(jiǎn)單的rag實(shí)現(xiàn) -->
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-easy-rag</artifactId>
        </dependency>
        <!-- 集成pine -->
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-pinecone</artifactId>
        </dependency>
        <!-- 流式輸出 -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-webflux</artifactId>
        </dependency>
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-reactor</artifactId>
        </dependency>
    </dependencies>
    <dependencyManagement>
        <dependencies>
            <!--引入SpringBoot依賴管理清單-->
            <dependency>
                <groupId>org.springframework.boot</groupId>
                <artifactId>spring-boot-dependencies</artifactId>
                <version>${spring-boot.version}</version>
                <type>pom</type>
                <scope>import</scope>
            </dependency>
            <!--引入langchain4j依賴管理清單-->
            <dependency>
                <groupId>dev.langchain4j</groupId>
                <artifactId>langchain4j-bom</artifactId>
                <version>${langchain4j.version}</version>
                <type>pom</type>
                <scope>import</scope>
            </dependency>
            <!--引入百煉依賴管理清單-->
            <dependency>
                <groupId>dev.langchain4j</groupId>
                <artifactId>langchain4j-community-bom</artifactId>
                <version>${langchain4j.version}</version>
                <type>pom</type>
                <scope>import</scope>
            </dependency>
            <!-- 覆蓋logback的依賴配置，使用兼容 SLF4J 2.x 的版本 -->
            <dependency>
                <groupId>ch.qos.logback</groupId>
                <artifactId>logback-core</artifactId>
                <version>${logback.version}</version>
            </dependency>
            <dependency>
                <groupId>ch.qos.logback</groupId>
                <artifactId>logback-classic</artifactId>
                <version>${logback.version}</version>
            </dependency>
            <!-- 覆蓋tomcat的依賴配置，使用兼容 Spring Boot 3.2.6 的版本 -->
            <dependency>
                <groupId>org.apache.tomcat.embed</groupId>
                <artifactId>tomcat-embed-core</artifactId>
                <version>${tomcat.version}</version>
            </dependency>
            <dependency>
                <groupId>org.apache.tomcat.embed</groupId>
                <artifactId>tomcat-embed-el</artifactId>
                <version>${tomcat.version}</version>
            </dependency>
            <dependency>
                <groupId>org.apache.tomcat.embed</groupId>
                <artifactId>tomcat-embed-websocket</artifactId>
                <version>${tomcat.version}</version>
            </dependency>
        </dependencies>
    </dependencyManagement>

# web服務(wù)訪問(wèn)端口
server.port=8087
#langchain4j測(cè)試模型
#langchain4j.open-ai.chat-model.base-url=http://langchain4j.dev/demo/openai/v1
#langchain4j.open-ai.chat-model.api-key=demo
#langchain4j.open-ai.chat-model.model-name=gpt-4o-mini
##DEEP_SEEK_API_KEY=這個(gè)是deepseek的apikey
##DeepSeek
#langchain4j.open-ai.chat-model.base-url=https://api.deepseek.com
#langchain4j.open-ai.chat-model.api-key=${DEEP_SEEK_API_KEY}
##DeepSeek-V3
#langchain4j.open-ai.chat-model.model-name=deepseek-chat
DASH_SCOPE_API_KEY=阿里云百煉大模型的apikey
#阿里百煉平臺(tái)
langchain4j.community.dashscope.chat-model.api-key=${DASH_SCOPE_API_KEY}
langchain4j.community.dashscope.chat-model.model-name=qwen-max
#集成百煉-deepseek
langchain4j.open-ai.chat-model.base-url=https://dashscope.aliyuncs.com/compatible-mode/v1
langchain4j.open-ai.chat-model.api-key=${DASH_SCOPE_API_KEY}
langchain4j.open-ai.chat-model.model-name=deepseek-v3
#溫度系數(shù)：取值范圍通常在 0 到 1 之間。值越高，模型的輸出越隨機(jī)、富有創(chuàng)造性；
# 值越低，輸出越確定、保守。這里設(shè)置為 0.9，意味著模型會(huì)有一定的隨機(jī)性，生成的回復(fù)可能會(huì)比較多樣化。
langchain4j.open-ai.chat-model.temperature=0.9
#ollama
langchain4j.ollama.chat-model.base-url=http://localhost:11434
langchain4j.ollama.chat-model.model-name=deepseek-r1:1.5b
# 創(chuàng)造性控制
# 0.7到1.2?：適用于需要較高創(chuàng)造性的場(chǎng)景，如詩(shī)歌生成或頭腦風(fēng)暴
# 0.3到1.0?：適用于需要較高穩(wěn)定性和多樣性的場(chǎng)景，如技術(shù)文檔或法律文書(shū)的生成
langchain4j.ollama.chat-model.temperature=0.8
# 模型進(jìn)行通信的超時(shí)時(shí)間為60秒。
langchain4j.ollama.chat-model.timeout=PT60S
langchain4j.ollama.chat-model.log-requests=true
langchain4j.ollama.chat-model.log-responses=true
#MongoDB連接配置
spring.data.mongodb.uri=mongodb://localhost:27017/chat_memory_db
# 基本數(shù)據(jù)源配置
spring.datasource.url=jdbc:mysql://localhost:3306/guiguxiaozhi?useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai&useSSL=false
spring.datasource.username=root
spring.datasource.password=root
spring.datasource.driver-class-name=com.mysql.cj.jdbc.Driver
# 開(kāi)啟 SQL 日志打印
mybatis-plus.configuration.log-impl=org.apache.ibatis.logging.stdout.StdOutImpl
#集成阿里通義千問(wèn)-通用文本向量-v3
langchain4j.community.dashscope.embedding-model.api-key=${DASH_SCOPE_API_KEY}
langchain4j.community.dashscope.embedding-model.model-name=text-embedding-v3
#集成阿里通義千問(wèn)-流式輸出
langchain4j.community.dashscope.streaming-chat-model.api-key=${DASH_SCOPE_API_KEY}
langchain4j.community.dashscope.streaming-chat-model.model-name=qwen-plus

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class RuoyiLangChain4jApplication {
    public static void main(String[] args) {
        SpringApplication.run(RuoyiLangChain4jApplication.class, args);
    }
}

import dev.langchain4j.model.openai.OpenAiChatModel;
import org.junit.jupiter.api.Test;
import org.springframework.boot.test.context.SpringBootTest;
@SpringBootTest
public class LLMTest {
    /**
     * gpt-4o-mini語(yǔ)言模型接入測(cè)試
     */
    @Test
    public void testGPTDemo() {
        //初始化模型
        OpenAiChatModel model = OpenAiChatModel.builder()
                //LangChain4j提供的代理服務(wù)器，該代理服務(wù)器會(huì)將演示密鑰替換成真實(shí)密鑰， 再將請(qǐng)求轉(zhuǎn)發(fā)給OpenAI API
                //.baseUrl("http://langchain4j.dev/demo/openai/v1") //設(shè)置模型api地址（如果apiKey="demo"，則可省略baseUrl的配置）
                .apiKey("demo") //設(shè)置模型apiKey
                .modelName("gpt-4o-mini") //設(shè)置模型名稱
                .build();
        //向模型提問(wèn)
        String answer = model.chat("你好");
        //輸出結(jié)果
        System.out.println(answer);
    }
}

/**
 * 整合SpringBoot
 */
@Autowired
private OpenAiChatModel openAiChatModel;
@Test
public void testSpringBoot() {
    //向模型提問(wèn)
    String answer = openAiChatModel.chat("你好");
    //輸出結(jié)果
    System.out.println(answer);
}

/**
 * ollama接入
 */
@Autowired
private OllamaChatModel ollamaChatModel;
@Test
public void testOllama() {
    //向模型提問(wèn)
    String answer = ollamaChatModel.chat("你好");
    //輸出結(jié)果
    System.out.println(answer);
}

/**
 * 通義千問(wèn)大模型
 */
@Autowired
private QwenChatModel qwenChatModel;
@Test
public void testDashScopeQwen() {
    //向模型提問(wèn)
    String answer = qwenChatModel.chat("你好");
    //輸出結(jié)果
    System.out.println(answer);
}

@Test
public void testDashScopeWanx(){
    WanxImageModel wanxImageModel = WanxImageModel.builder()
            .modelName("wanx2.1-t2i-plus")
            .apiKey(System.getenv("DASH_SCOPE_API_KEY"))
            .build();
    Response<Image> response = wanxImageModel.generate("奇幻森林精靈：在一片彌漫著輕柔薄霧的古老森林深處，陽(yáng)光透過(guò)茂密枝葉灑下金色光斑。一位身材嬌小、長(zhǎng)著透明薄翼的精靈少女站在一朵碩大的蘑菇上。她有著海藻般的綠色長(zhǎng)發(fā)，發(fā)間點(diǎn)綴著藍(lán)色的小花，皮膚泛著珍珠般的微光。身上穿著由翠綠樹(shù)葉和白色藤蔓編織而成的連衣裙，手中捧著一顆散發(fā)著柔和光芒的水晶球，周圍環(huán)繞著五彩斑斕的蝴蝶，腳下是鋪滿苔蘚的地面，蘑菇和蕨類植物叢生，營(yíng)造出神秘而夢(mèng)幻的氛圍。");
    System.out.println(response.content().url());
}

import dev.langchain4j.service.MemoryId;
import dev.langchain4j.service.SystemMessage;
import dev.langchain4j.service.UserMessage;
import dev.langchain4j.service.spring.AiService;
import reactor.core.publisher.Flux;
import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT;
@AiService(
        wiringMode = EXPLICIT,
        streamingChatModel = "qwenStreamingChatModel",
        chatMemoryProvider = "chatMemoryProviderXiaozhi",
        tools = "appointmentTools",
        contentRetriever = "contentRetrieverXiaozhiPincone" )
public interface XiaozhiAgent {
    @SystemMessage(fromResource = "zhaozhi-prompt-template.txt")
    Flux<String> chat(@MemoryId Long memoryId, @UserMessage String userMessage);
}

@Autowired
private Assistant assistant;
@Test
public void testAssistant() {
    String answer = assistant.chat("Hello");
    System.out.println(answer);
}

@SpringBootTest
public class ChatMemoryTest {
    @Autowired
    private Assistant assistant;
    @Test
    public void testChatMemory() {
        String answer1 = assistant.chat("我是華仔");
        System.out.println(answer1);
        String answer2 = assistant.chat("我是誰(shuí)");
        System.out.println(answer2);
    }
}

import com.guigu.ai.langchain4j.assistant.Assistant;
import dev.langchain4j.community.model.dashscope.QwenChatModel;
import dev.langchain4j.data.message.AiMessage;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.model.chat.response.ChatResponse;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import java.util.Arrays;
@SpringBootTest
public class ChatMemoryTest {
    @Autowired
    private Assistant assistant;
    @Autowired
    private QwenChatModel qwenChatModel;
    @Test
    public void testChatMemory2() {
        UserMessage userMessage1 = UserMessage.userMessage("我是華仔");
        ChatResponse chatResponse1 = qwenChatModel.chat(userMessage1);
        AiMessage aiMessage1 = chatResponse1.aiMessage();
        System.out.println(aiMessage1.text());
        UserMessage userMessage2 = UserMessage.userMessage("我是誰(shuí)");
        ChatResponse chatResponse2= qwenChatModel.chat(Arrays.asList(userMessage1,aiMessage1,userMessage2));
        AiMessage aiMessage2 = chatResponse2.aiMessage();
        System.out.println(aiMessage2.text());
    }
}

@Test
public void testChatMemory3() {
    //創(chuàng)建chatMemory
    MessageWindowChatMemory chatMemory = MessageWindowChatMemory.withMaxMessages(10);
    //創(chuàng)建AIService
    Assistant assistant = AiServices
            .builder(Assistant.class)
            .chatLanguageModel(qwenChatModel)
            .chatMemory(chatMemory)
            .build();
    //調(diào)用service的接口
    String answer1 = assistant.chat("我是華仔");
    System.out.println(answer1);
    String answer2 = assistant.chat("我是誰(shuí)");
    System.out.println(answer2);
}

import dev.langchain4j.memory.ChatMemory;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class MemoryChatAssistantConfig {
    @Bean
    ChatMemory chatMemory(){
        //設(shè)置聊天記憶記錄的message數(shù)量
        return MessageWindowChatMemory.withMaxMessages(10);
    }
}

@Autowired
private MemoryChatAssistant memoryChatAssistant;
@Test
public void testChatMemory4() {
    String answer1 = memoryChatAssistant.chat("我是華仔");
    System.out.println(answer1);
    String answer2 = memoryChatAssistant.chat("我是誰(shuí)");
    System.out.println(answer2);
}

import com.ruoyi.langchain4j.store.MongoChatMemoryStore;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class SeparateChatAssistantConfig {
    @Autowired
    private MongoChatMemoryStore mongoChatMemoryStore;
    @Bean
    ChatMemoryProvider chatMemoryProvider() {
        return memoryId -> MessageWindowChatMemory.builder()
                .id(memoryId)
                .maxMessages(10)
                .chatMemoryStore(mongoChatMemoryStore)//配置持久化對(duì)象
                .build();
    }
}

@Autowired
private SeparateChatAssistant separateChatAssistant;
@Test
public void testChatMemory5() {
    String answer1 = separateChatAssistant.chat(1,"我是華仔");
    System.out.println(answer1);
    String answer2 = separateChatAssistant.chat(1,"我是誰(shuí)");
    System.out.println(answer2);
    String answer3 = separateChatAssistant.chat(2,"我是誰(shuí)");
    System.out.println(answer3);
}

mongosh

mongosh --host <hostname>:<port>

import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.bson.types.ObjectId;
import org.springframework.data.annotation.Id;
import org.springframework.data.mongodb.core.mapping.Document;
@Data
@AllArgsConstructor
@NoArgsConstructor
@Document("chat_messages")
public class ChatMessages {
    @Id
    private ObjectId id;
    private int messageId;
    //存聊天記錄的
    private String content;
}

@SpringBootTest
public class MongoCrudTest {
    @Autowired
    private MongoTemplate mongoTemplate;
    /**
     * 插入文檔
     */
   /* @Test
    public void testInsert() {
        mongoTemplate.insert(new ChatMessages(1L, "聊天記錄"));
    }*/
    /**
     * 插入文檔
     */
    @Test
    public void testInsert2() {
        ChatMessages chatMessages = new ChatMessages();
        chatMessages.setContent("聊天記錄列表");
        mongoTemplate.insert(chatMessages);
    }
    /**
     * 根據(jù)id查詢文檔
     */
    @Test
    public void testFindById() {
        ChatMessages chatMessages = mongoTemplate.findById("6801ead733ba9c4a0d9b6c7b", ChatMessages.class);
        System.out.println(chatMessages);
    }
    /**
     * 修改文檔
     */
    @Test
    public void testUpdate() {
        Criteria criteria = Criteria.where("_id").is("6801ead733ba9c4a0d9b6c7b");
        Query query = new Query(criteria);
        Update update = new Update();
        update.set("content", "新的聊天記錄列表");
        //修改或新增
        mongoTemplate.upsert(query, update, ChatMessages.class);
    }
    /**
     * 新增或修改文檔
     */
    @Test
    public void testUpdate2() {
        Criteria criteria = Criteria.where("_id").is("100");
        Query query = new Query(criteria);
        Update update = new Update();
        update.set("content", "新的聊天記錄列表");
        //修改或新增
        mongoTemplate.upsert(query, update, ChatMessages.class);
    }
    /**
     * 刪除文檔
     */
    @Test
    public void testDelete() {
        Criteria criteria = Criteria.where("_id").is("100");
        Query query = new Query(criteria);
        mongoTemplate.remove(query, ChatMessages.class);
    }
}

import com.ruoyi.langchain4j.bean.ChatMessages;
import dev.langchain4j.data.message.ChatMessage;
import dev.langchain4j.data.message.ChatMessageDeserializer;
import dev.langchain4j.data.message.ChatMessageSerializer;
import dev.langchain4j.store.memory.chat.ChatMemoryStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.mongodb.core.MongoTemplate;
import org.springframework.data.mongodb.core.query.Criteria;
import org.springframework.data.mongodb.core.query.Query;
import org.springframework.data.mongodb.core.query.Update;
import org.springframework.stereotype.Component;
import java.util.LinkedList;
import java.util.List;
@Component
public class MongoChatMemoryStore implements ChatMemoryStore {
    @Autowired
    private MongoTemplate mongoTemplate;
    @Override
    public List<ChatMessage> getMessages(Object memoryId) {
        Criteria memoryId1 = Criteria.where("memoryId").is(memoryId);
        Query query = new Query(memoryId1);
        ChatMessages one = mongoTemplate.findOne(query, ChatMessages.class);
        if(one == null){
            return new LinkedList<>();
        }
        return ChatMessageDeserializer.messagesFromJson(one.getContent());
    }
    @Override
    public void updateMessages(Object memoryId, List<ChatMessage> messages) {
        Criteria criteria = Criteria.where("memoryId").is(memoryId);
        Query query = new Query(criteria);
        Update update = new Update();
        update.set("content", ChatMessageSerializer.messagesToJson(messages));
        //根據(jù)query條件能查詢出文檔，則修改文檔；否則新增文檔
        mongoTemplate.upsert(query, update, ChatMessages.class);
    }
    @Override
    public void deleteMessages(Object memoryId) {
        Criteria criteria = Criteria.where("memoryId").is(memoryId);
        Query query = new Query(criteria);
        mongoTemplate.remove(query, ChatMessages.class);
    }
}

你的名字是“智能車聯(lián)的助理”，你是幫助人們答惑解疑的智能客服。
你是一個(gè)擁有海量智能車聯(lián)的智能小助手。
你態(tài)度友好、溫柔、禮貌且言辭簡(jiǎn)潔。
1、請(qǐng)僅在用戶發(fā)起第一次會(huì)話時(shí)，和用戶打個(gè)招呼，并介紹你是誰(shuí)。
一、基本信息
角色名稱：智能車聯(lián)系統(tǒng)智能助理
核心定位：專注于智能車聯(lián)領(lǐng)域的服務(wù)型助理，為用戶解決智能車聯(lián)系統(tǒng)使用中的各類問(wèn)題，提供專業(yè)、便捷的支持
交互基調(diào)：態(tài)度友好溫柔、言辭簡(jiǎn)潔禮貌，搭配輕松可愛(ài)的圖標(biāo) / 表情，提升用戶交互體驗(yàn)
二、核心功能
1. 智能車聯(lián)功能答疑
覆蓋場(chǎng)景：導(dǎo)航聯(lián)動(dòng)（如手機(jī)導(dǎo)航同步車機(jī)）、車機(jī)互聯(lián)（如 CarPlay/HiCar 連接）、語(yǔ)音控制（如語(yǔ)音調(diào)空調(diào) / 切歌）等核心功能
服務(wù)內(nèi)容：用簡(jiǎn)潔語(yǔ)言解釋功能原理、 step-by-step 說(shuō)明使用方法，避免技術(shù)術(shù)語(yǔ)堆砌
2. 故障排查指引
針對(duì)問(wèn)題：車聯(lián)系統(tǒng)連接失?。ㄈ缢{(lán)牙 / USB 連不上）、功能卡頓（如導(dǎo)航加載慢）、語(yǔ)音指令無(wú)響應(yīng)等常見(jiàn)故障
服務(wù)內(nèi)容：提供 3-5 步簡(jiǎn)易排查步驟，優(yōu)先推薦用戶可自主操作的解決方案（如重啟車機(jī)、檢查連接權(quán)限）
3. 場(chǎng)景化功能推薦
場(chǎng)景匹配：根據(jù)用戶使用場(chǎng)景（長(zhǎng)途駕駛、日常通勤、家庭出行等）推薦適配功能
示例：長(zhǎng)途駕駛時(shí)推薦 “導(dǎo)航中途休息提醒”“語(yǔ)音控制免手動(dòng)操作”；日常通勤推薦 “通勤路線記憶”“實(shí)時(shí)路況同步” ??
三、服務(wù)規(guī)則
內(nèi)容要求：回答必須準(zhǔn)確（避免錯(cuò)誤指引）、相關(guān)（不偏離智能車聯(lián)主題）、有幫助（解決用戶實(shí)際問(wèn)題）
表達(dá)要求：簡(jiǎn)潔明了（避免冗長(zhǎng)）、有條理（分點(diǎn) / 分步驟說(shuō)明）、有邏輯（不矛盾），禁用模糊或隨意表述
交互要求：全程禮貌無(wú)冒犯，對(duì)用戶疑問(wèn)耐心回應(yīng)，若遇暫無(wú)法解答的問(wèn)題，需明確告知 “當(dāng)前暫未覆蓋該問(wèn)題，可提供更多細(xì)節(jié)以便進(jìn)一步協(xié)助”
四、交互規(guī)范
首次會(huì)話：主動(dòng)打招呼并自我介紹，示例：“你好呀！?? 我是智能車聯(lián)系統(tǒng)智能助理，專門(mén)幫你解決車聯(lián)功能使用、故障排查等問(wèn)題，有需求隨時(shí)跟我說(shuō)哦～”
功能調(diào)用：用戶提及相關(guān)需求（如 “車機(jī)連不上手機(jī)”“推薦通勤用的功能”），直接對(duì)應(yīng)核心功能提供服務(wù)，無(wú)需額外冗余引導(dǎo)
表情使用：關(guān)鍵功能 / 場(chǎng)景搭配適配圖標(biāo)（如功能答疑用 “??”，故障排查用 “??”），每段回復(fù)表情不超過(guò) 2 個(gè)，避免過(guò)度堆砌

import dev.langchain4j.service.MemoryId;
import dev.langchain4j.service.SystemMessage;
import dev.langchain4j.service.UserMessage;
import dev.langchain4j.service.spring.AiService;
import reactor.core.publisher.Flux;
import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT;
@AiService(
        wiringMode = EXPLICIT,
        streamingChatModel = "qwenStreamingChatModel",
        chatMemoryProvider = "chatMemoryProviderXiaozhi",
        tools = "appointmentTools",
        contentRetriever = "contentRetrieverXiaozhiPincone" )
public interface XiaozhiAgent {
    @SystemMessage(fromResource = "zhaozhi-prompt-template.txt")
    Flux<String> chat(@MemoryId Long memoryId, @UserMessage String userMessage);
}

import lombok.Data;
@Data
public class ChatForm {
    private Long memoryId;//對(duì)話id
    private String message;//用戶問(wèn)題
}

import com.ruoyi.langchain4j.assistant.XiaozhiAgent;
import com.ruoyi.langchain4j.bean.ChatForm;
import io.swagger.v3.oas.annotations.Operation;
import io.swagger.v3.oas.annotations.tags.Tag;
import org.springframework.beans.factory.annotation.Autowired;
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 reactor.core.publisher.Flux;
@Tag(name = "智能車聯(lián)助手")
@RestController
@RequestMapping("/xiaozhi")
public class XiaozhiController {
    @Autowired
    private XiaozhiAgent xiaozhiAgent;
    @Operation(summary = "對(duì)話")
    @PostMapping(value = "/chat", produces = "text/stream;charset=utf-8")
    public Flux<String> chat(@RequestBody ChatForm chatForm)  {
        return xiaozhiAgent.chat(chatForm.getMemoryId(), chatForm.getMessage());
    }
}

import dev.langchain4j.agent.tool.Tool;
import org.springframework.stereotype.Component;
@Component
public class CalculatorTools {
    @Tool
    double sum(double a, double b) {
        System.out.println("調(diào)用加法運(yùn)算");
        return a + b;
    }
    @Tool
    double squareRoot(double x) {
        System.out.println("調(diào)用平方根運(yùn)算");
        return Math.sqrt(x);
    }
}

@SpringBootTest
public class ToolsTest {
    @Autowired
    private SeparateChatAssistant separateChatAssistant;
    @Test
    public void testCalculatorTools() {
        String answer = separateChatAssistant.chat(1, "1+2等于幾，475695037565的平方根是多少？");
        //答案：3，689706.4865
        System.out.println(answer);
    }
}

CREATE DATABASE `guiguxiaozhi`;
USE `guiguxiaozhi`;
CREATE TABLE `appointment` (
  `id` BIGINT NOT NULL AUTO_INCREMENT,
  `username` VARCHAR(50) NOT NULL,
  `id_card` VARCHAR(18) NOT NULL,
  `department` VARCHAR(50) NOT NULL,
  `date` VARCHAR(10) NOT NULL,
  `time` VARCHAR(10) NOT NULL,
  `doctor_name` VARCHAR(50) DEFAULT NULL,
   PRIMARY KEY (`id`)
);

import com.baomidou.mybatisplus.annotation.IdType;
import com.baomidou.mybatisplus.annotation.TableId;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
@Data
@AllArgsConstructor
@NoArgsConstructor
public class Appointment {
    @TableId(type = IdType.AUTO)
    private Long id;
    private String username;
    private String idCard;
    private String department;
    private String date;
    private String time;
    private String doctorName;
}

import com.baomidou.mybatisplus.core.mapper.BaseMapper;
import com.ruoyi.langchain4j.entity.Appointment;
import org.apache.ibatis.annotations.Mapper;
@Mapper
public interface AppointmentMapper extends BaseMapper<Appointment> {
}

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE mapper
        PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN"
        "http://mybatis.org/dtd/mybatis-3-mapper.dtd">
<mapper namespace="com.ruoyi.langchain4j.mapper.AppointmentMapper">
</mapper>

import com.baomidou.mybatisplus.extension.service.IService;
import com.ruoyi.langchain4j.entity.Appointment;
public interface AppointmentService extends IService<Appointment> {
    Appointment getOne(Appointment appointment);
}

package com.ruoyi.langchain4j.service.Impl;
import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
import com.baomidou.mybatisplus.extension.service.impl.ServiceImpl;
import com.ruoyi.langchain4j.entity.Appointment;
import com.ruoyi.langchain4j.mapper.AppointmentMapper;
import com.ruoyi.langchain4j.service.AppointmentService;
import org.springframework.stereotype.Service;
@Service
public class AppointmentServiceImpl extends ServiceImpl<AppointmentMapper, Appointment> implements AppointmentService {
    @Override
    public Appointment getOne(Appointment appointment) {
        //查詢數(shù)據(jù)是否存在
        LambdaQueryWrapper<Appointment> appointmentLambdaQueryWrapper = new LambdaQueryWrapper<>();
        appointmentLambdaQueryWrapper.eq(Appointment::getUsername,appointment.getUsername());
        appointmentLambdaQueryWrapper.eq(Appointment::getIdCard,appointment.getIdCard());
        appointmentLambdaQueryWrapper.eq(Appointment::getDepartment,appointment.getDepartment());
        appointmentLambdaQueryWrapper.eq(Appointment::getDate,appointment.getDate());
        appointmentLambdaQueryWrapper.eq(Appointment::getTime,appointment.getTime());
        Appointment appointment1 = baseMapper.selectOne(appointmentLambdaQueryWrapper);
        return appointment1;
    }
}

@SpringBootTest
public class RAGTest {
    @Test
    public void testReadDocument() {
        //使用FileSystemDocumentLoader讀取指定目錄下的知識(shí)庫(kù)文檔
        //并使用默認(rèn)的文檔解析器TextDocumentParser對(duì)文檔進(jìn)行解析
        Document document = FileSystemDocumentLoader.loadDocument("E:/knowledge/測(cè)試.txt");
        System.out.println(document.text());
    }
}

 /**
     * 解析PDF
     */
@Test
public void testParsePDF() {
    Document document = FileSystemDocumentLoader.loadDocument(
            "E:/knowledge/醫(yī)院信息.pdf",
            new ApachePdfBoxDocumentParser()
    );
    System.out.println(document);
}

/**
 * 加載文檔并存入向量數(shù)據(jù)庫(kù)
 */
@Test
public void testReadDocumentAndStore() {
    //使用FileSystemDocumentLoader讀取指定目錄下的知識(shí)庫(kù)文檔
    //并使用默認(rèn)的文檔解析器對(duì)文檔進(jìn)行解析(TextDocumentParser)
    Document document = FileSystemDocumentLoader.loadDocument("E:/knowledge/人工智能.md");
    //為了簡(jiǎn)單起見(jiàn)，我們暫時(shí)使用基于內(nèi)存的向量存儲(chǔ)
    InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
    //ingest
    //1、分割文檔：默認(rèn)使用遞歸分割器，將文檔分割為多個(gè)文本片段，每個(gè)片段包含不超過(guò) 300個(gè)token，并且有 30個(gè)token的重疊部分保證連貫性
    //DocumentByParagraphSplitter(DocumentByLineSplitter(DocumentBySentenceSplitter(DocumentByWordSplitter)))
    //2、文本向量化：使用一個(gè)LangChain4j內(nèi)置的輕量化向量模型對(duì)每個(gè)文本片段進(jìn)行向量化
    //3、將原始文本和向量存儲(chǔ)到向量數(shù)據(jù)庫(kù)中(InMemoryEmbeddingStore)
    EmbeddingStoreIngestor.ingest(document, embeddingStore);
    //查看向量數(shù)據(jù)庫(kù)內(nèi)容
    System.out.println(embeddingStore);
}

/**
 * 文檔分割
 */
@Test
public void testDocumentSplitter() {
    //使用FileSystemDocumentLoader讀取指定目錄下的知識(shí)庫(kù)文檔
    //并使用默認(rèn)的文檔解析器對(duì)文檔進(jìn)行解析(TextDocumentParser)
    Document document = FileSystemDocumentLoader.loadDocument("E:/knowledge/人工智能.md");
    //為了簡(jiǎn)單起見(jiàn)，我們暫時(shí)使用基于內(nèi)存的向量存儲(chǔ)
    InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
    //自定義文檔分割器
    //按段落分割文檔：每個(gè)片段包含不超過(guò) 300個(gè)token，并且有 30個(gè)token的重疊部分保證連貫性
    //注意：當(dāng)段落長(zhǎng)度總和小于設(shè)定的最大長(zhǎng)度時(shí)，就不會(huì)有重疊的必要。
    DocumentByParagraphSplitter documentSplitter = new DocumentByParagraphSplitter(
            300,
            30,
            //token分詞器：按token計(jì)算
            new HuggingFaceTokenizer());
    //按字符計(jì)算
    //DocumentByParagraphSplitter documentSplitter = new DocumentByParagraphSplitter(300, 30);
    EmbeddingStoreIngestor
            .builder()
            .embeddingStore(embeddingStore)
            .documentSplitter(documentSplitter)
            .build()
            .ingest(document);
}

import com.ruoyi.langchain4j.store.MongoChatMemoryStore;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import java.util.Arrays;
import java.util.List;
@Configuration
public class XiaozhiAgentConfig {
    @Autowired
    private MongoChatMemoryStore mongoChatMemoryStore;
    @Autowired
    private EmbeddingStore embeddingStore;
    @Autowired
    private EmbeddingModel embeddingModel;
    @Bean
    ChatMemoryProvider chatMemoryProviderXiaozhi() {
        return memoryId -> MessageWindowChatMemory.builder()
                .id(memoryId)
                .maxMessages(20)
                .chatMemoryStore(mongoChatMemoryStore)
                .build();
    }
    @Bean
    ContentRetriever contentRetrieverXiaozhi() {
        //使用FileSystemDocumentLoader讀取指定目錄下的知識(shí)庫(kù)文檔
        //并使用默認(rèn)的文檔解析器對(duì)文檔進(jìn)行解析
        Document document1 = FileSystemDocumentLoader.loadDocument("D:/knowledge/醫(yī)院信息.md");
        Document document2 = FileSystemDocumentLoader.loadDocument("D:/knowledge/科室信息.md");
        Document document3 = FileSystemDocumentLoader.loadDocument("D:/knowledge/神經(jīng)內(nèi)科.md");
        List<Document> documents = Arrays.asList(document1, document2, document3);
        //使用內(nèi)存向量存儲(chǔ)
        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
        //使用默認(rèn)的文檔分割器
        EmbeddingStoreIngestor.ingest(documents, embeddingStore);
        //從嵌入存儲(chǔ)（EmbeddingStore）里檢索和查詢內(nèi)容相關(guān)的信息
        return EmbeddingStoreContentRetriever.from(embeddingStore);
    }
    @Bean
    ContentRetriever contentRetrieverXiaozhiPincone() {
        // 創(chuàng)建一個(gè) EmbeddingStoreContentRetriever 對(duì)象，用于從嵌入存儲(chǔ)中檢索內(nèi)容
        return EmbeddingStoreContentRetriever
                .builder()
                // 設(shè)置用于生成嵌入向量的嵌入模型
                .embeddingModel(embeddingModel)
                // 指定要使用的嵌入存儲(chǔ)
                .embeddingStore(embeddingStore)
                // 設(shè)置最大檢索結(jié)果數(shù)量，這里表示最多返回 1 條匹配結(jié)果
                .maxResults(1)
                // 設(shè)置最小得分閾值，只有得分大于等于 0.8 的結(jié)果才會(huì)被返回
                .minScore(0.8)
                // 構(gòu)建最終的 EmbeddingStoreContentRetriever 實(shí)例
                .build();
    }
}

import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import java.util.Arrays;
import java.util.List;
@SpringBootTest
public class EmbeddingTest {
    @Autowired
    private EmbeddingStore embeddingStore;
    @Autowired
    private EmbeddingModel embeddingModel;
    @Test
    public void testUploadKnowledgeLibrary() {
        //使用FileSystemDocumentLoader讀取指定目錄下的知識(shí)庫(kù)文檔
        //并使用默認(rèn)的文檔解析器對(duì)文檔進(jìn)行解析
        Document document1 = FileSystemDocumentLoader.loadDocument("D:/knowledge/醫(yī)院信息.md");
        Document document2 = FileSystemDocumentLoader.loadDocument("D:/knowledge/科室信息.md");
        Document document3 = FileSystemDocumentLoader.loadDocument("D:/knowledge/神經(jīng)內(nèi)科.md");
        List<Document> documents = Arrays.asList(document1, document2, document3);
        //文本向量化并存入向量數(shù)據(jù)庫(kù)：將每個(gè)片段進(jìn)行向量化，得到一個(gè)嵌入向量
        EmbeddingStoreIngestor
                .builder()
                .embeddingStore(embeddingStore)
                .embeddingModel(embeddingModel)
                .build()
                .ingest(documents);
        System.out.println("上傳知識(shí)庫(kù)成功");
    }
}

import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.pinecone.PineconeEmbeddingStore;
import dev.langchain4j.store.embedding.pinecone.PineconeServerlessIndexConfig;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class EmbeddingStoreConfig {
    @Autowired
    private EmbeddingModel embeddingModel;
    @Bean
    public EmbeddingStore<TextSegment> embeddingStore() {
        //創(chuàng)建向量存儲(chǔ)
        EmbeddingStore<TextSegment> embeddingStore = PineconeEmbeddingStore.builder()
                .apiKey("相對(duì)應(yīng)的apikey")
                .index("xiaozhi-index")//如果指定的索引不存在，將創(chuàng)建一個(gè)新的索引
                .nameSpace("xiaozhi-namespace") //如果指定的名稱空間不存在，將創(chuàng)建一個(gè)新的名稱空間
                .createIndex(PineconeServerlessIndexConfig.builder()
                        .cloud("AWS") //指定索引部署在 AWS 云服務(wù)上。
                        .region("us-east-1") //指定索引所在的 AWS 區(qū)域?yàn)?us-east-1。
                        .dimension(embeddingModel.dimension()) //指定索引的向量維度，該維度與 embeddedModel 生成的向量維度相同。
                        .build())
                .build();
        return embeddingStore;
    }
}

import com.ruoyi.langchain4j.store.MongoChatMemoryStore;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import java.util.Arrays;
import java.util.List;
@Configuration
public class XiaozhiAgentConfig {
    @Autowired
    private MongoChatMemoryStore mongoChatMemoryStore;
    @Autowired
    private EmbeddingStore embeddingStore;
    @Autowired
    private EmbeddingModel embeddingModel;
    @Bean
    ChatMemoryProvider chatMemoryProviderXiaozhi() {
        return memoryId -> MessageWindowChatMemory.builder()
                .id(memoryId)
                .maxMessages(20)
                .chatMemoryStore(mongoChatMemoryStore)
                .build();
    }
    @Bean
    ContentRetriever contentRetrieverXiaozhi() {
        //使用FileSystemDocumentLoader讀取指定目錄下的知識(shí)庫(kù)文檔
        //并使用默認(rèn)的文檔解析器對(duì)文檔進(jìn)行解析
        Document document1 = FileSystemDocumentLoader.loadDocument("D:/knowledge/醫(yī)院信息.md");
        Document document2 = FileSystemDocumentLoader.loadDocument("D:/knowledge/科室信息.md");
        Document document3 = FileSystemDocumentLoader.loadDocument("D:/knowledge/神經(jīng)內(nèi)科.md");
        List<Document> documents = Arrays.asList(document1, document2, document3);
        //使用內(nèi)存向量存儲(chǔ)
        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
        //使用默認(rèn)的文檔分割器
        EmbeddingStoreIngestor.ingest(documents, embeddingStore);
        //從嵌入存儲(chǔ)（EmbeddingStore）里檢索和查詢內(nèi)容相關(guān)的信息
        return EmbeddingStoreContentRetriever.from(embeddingStore);
    }
    @Bean
    ContentRetriever contentRetrieverXiaozhiPincone() {
        // 創(chuàng)建一個(gè) EmbeddingStoreContentRetriever 對(duì)象，用于從嵌入存儲(chǔ)中檢索內(nèi)容
        return EmbeddingStoreContentRetriever
                .builder()
                // 設(shè)置用于生成嵌入向量的嵌入模型
                .embeddingModel(embeddingModel)
                // 指定要使用的嵌入存儲(chǔ)
                .embeddingStore(embeddingStore)
                // 設(shè)置最大檢索結(jié)果數(shù)量，這里表示最多返回 1 條匹配結(jié)果
                .maxResults(1)
                // 設(shè)置最小得分閾值，只有得分大于等于 0.8 的結(jié)果才會(huì)被返回
                .minScore(0.8)
                // 構(gòu)建最終的 EmbeddingStoreContentRetriever 實(shí)例
                .build();
    }
}

<template>
  <div class="app-layout">
    <div class="sidebar">
      <div class="logo-section">
        <div class="car-animation">
          <i class="fas fa-car-side car-icon"></i>
          <div class="glow-effect"></div>
        </div>
        <span class="logo-text">智能車聯(lián)助手</span>
        <span class="logo-subtitle">AI駕駛伴侶</span>
        <div class="status-indicator">
          <span class="status-dot"></span>
          <span class="status-text">系統(tǒng)在線</span>
        </div>
      </div>
      <el-button class="new-chat-button" @click="newChat">
        <i class="fas fa-plus"></i>
        <span class="button-text">新旅程</span>
      </el-button>
    </div>
    <div class="main-content">
      <div class="chat-container">
        <div class="message-list" ref="messaggListRef">
          <div
            v-for="(message, index) in messages"
            :key="index"
            :class="
              message.isUser ? 'message user-message' : 'message bot-message'
            "
          >
            <!-- 會(huì)話圖標(biāo) -->
            <i
              :class="
                message.isUser
                  ? 'fa-solid fa-user message-icon'
                  : 'fa-solid fa-robot message-icon'
              "
            ></i>
            <!-- 會(huì)話內(nèi)容 -->
            <span>
              <span v-html="message.content"></span>
              <!-- loading -->
              <span
                class="loading-dots"
                v-if="message.isThinking || message.isTyping"
              >
                <span class="dot"></span>
                <span class="dot"></span>
              </span>
            </span>
          </div>
        </div>
        <div class="input-container">
          <el-input
            v-model="inputMessage"
            placeholder="請(qǐng)輸入消息"
            @keyup.enter="sendMessage"
          ></el-input>
          <el-button @click="sendMessage" :disabled="isSending" type="primary"
            >發(fā)送</el-button
          >
        </div>
      </div>
    </div>
  </div>
</template>
<script setup>
import { onMounted, ref, watch } from 'vue'
import axios from 'axios'
import { v4 as uuidv4 } from 'uuid'
const messaggListRef = ref()
const isSending = ref(false)
const uuid = ref()
const inputMessage = ref('')
const messages = ref([])
onMounted(() => {
  initUUID()
  // 移除 setInterval，改用手動(dòng)滾動(dòng)
  watch(messages, () => scrollToBottom(), { deep: true })
  hello()
})
const scrollToBottom = () => {
  if (messaggListRef.value) {
    messaggListRef.value.scrollTop = messaggListRef.value.scrollHeight
  }
}
const hello = () => {
  sendRequest('你好')
}
const sendMessage = () => {
  if (inputMessage.value.trim()) {
    sendRequest(inputMessage.value.trim())
    inputMessage.value = ''
  }
}
const sendRequest = (message) => {
  isSending.value = true
  const userMsg = {
    isUser: true,
    content: message,
    isTyping: false,
    isThinking: false,
  }
  //第一條默認(rèn)發(fā)送的用戶消息”你好“不放入會(huì)話列表
  if(messages.value.length > 0){
    messages.value.push(userMsg)
  }
  // 添加機(jī)器人加載消息
  const botMsg = {
    isUser: false,
    content: '', // 增量填充
    isTyping: true, // 顯示加載動(dòng)畫(huà)
    isThinking: false,
  }
  messages.value.push(botMsg)
  const lastMsg = messages.value[messages.value.length - 1]
  scrollToBottom()
  axios
    .post(
      '/api/xiaozhi/chat',
      { memoryId: uuid.value, message },
      {
        responseType: 'stream', // 必須為合法值 "text"
        onDownloadProgress: (e) => {
          const fullText = e.event.target.responseText // 累積的完整文本
          let newText = fullText.substring(lastMsg.content.length)
          lastMsg.content += newText //增量更新
          console.log(lastMsg)
          scrollToBottom() // 實(shí)時(shí)滾動(dòng)
        },
      }
    )
    .then(() => {
      // 流結(jié)束后隱藏加載動(dòng)畫(huà)
      messages.value.at(-1).isTyping = false
      isSending.value = false
    })
    .catch((error) => {
      console.error('流式錯(cuò)誤:', error)
      messages.value.at(-1).content = '請(qǐng)求失敗，請(qǐng)重試'
      messages.value.at(-1).isTyping = false
      isSending.value = false
    })
}
// 初始化 UUID
const initUUID = () => {
  let storedUUID = localStorage.getItem('user_uuid')
  if (!storedUUID) {
    storedUUID = uuidToNumber(uuidv4())
    localStorage.setItem('user_uuid', storedUUID)
  }
  uuid.value = storedUUID
}
const uuidToNumber = (uuid) => {
  let number = 0
  for (let i = 0; i < uuid.length && i < 6; i++) {
    const hexValue = uuid[i]
    number = number * 16 + (parseInt(hexValue, 16) || 0)
  }
  return number % 1000000
}
// 轉(zhuǎn)換特殊字符
const convertStreamOutput = (output) => {
  return output
    .replace(/\n/g, '
')
    .replace(/\t/g, '    ')
    .replace(/&/g, '&') // 新增轉(zhuǎn)義，避免 HTML 注入
    .replace(/</g, '<')
    .replace(/>/g, '>')
}
const newChat = () => {
  // 這里添加新會(huì)話的邏輯
  console.log('開(kāi)始新會(huì)話')
  localStorage.removeItem('user_uuid')
  window.location.reload()
}
</script>
<style scoped>
.app-layout {
  display: flex;
  height: 100vh;
  background: linear-gradient(135deg, #0f0f23 0%, #1a1a3a 50%, #0f0f23 100%);
  color: #ffffff;
}
.sidebar {
  width: 200px;
  background: linear-gradient(180deg, #1a1a3a 0%, #0f0f23 100%);
  border-right: 1px solid #2a2a5a;
  padding: 20px;
  display: flex;
  flex-direction: column;
  align-items: center;
  box-shadow: 0 0 20px rgba(0, 136, 255, 0.1);
}
.logo-section {
  display: flex;
  flex-direction: column;
  align-items: center;
  margin-bottom: 30px;
}
.car-animation {
  position: relative;
  margin-bottom: 15px;
}
.car-icon {
  font-size: 48px;
  color: #00d4ff;
  text-shadow: 0 0 20px rgba(0, 212, 255, 0.8);
  animation: pulse 2s ease-in-out infinite;
}
.glow-effect {
  position: absolute;
  top: 50%;
  left: 50%;
  transform: translate(-50%, -50%);
  width: 80px;
  height: 80px;
  background: radial-gradient(circle, rgba(0, 212, 255, 0.3) 0%, transparent 70%);
  border-radius: 50%;
  animation: rotate 4s linear infinite;
}
@keyframes rotate {
  from {
    transform: translate(-50%, -50%) rotate(0deg);
  }
  to {
    transform: translate(-50%, -50%) rotate(360deg);
  }
}
.logo-text {
  font-size: 22px;
  font-weight: bold;
  color: #ffffff;
  margin-bottom: 5px;
  text-shadow: 0 0 10px rgba(0, 212, 255, 0.5);
  letter-spacing: 1px;
}
.logo-subtitle {
  font-size: 13px;
  color: #00d4ff;
  opacity: 0.9;
  margin-bottom: 15px;
  font-weight: 300;
}
.status-indicator {
  display: flex;
  align-items: center;
  gap: 8px;
  margin-bottom: 20px;
}
.status-dot {
  width: 8px;
  height: 8px;
  background: #00ff88;
  border-radius: 50%;
  animation: blink 2s ease-in-out infinite;
  box-shadow: 0 0 5px rgba(0, 255, 136, 0.5);
}
@keyframes blink {
  0%, 100% {
    opacity: 1;
  }
  50% {
    opacity: 0.3;
  }
}
.status-text {
  font-size: 11px;
  color: #8a8aa5;
  font-weight: 300;
}
.new-chat-button {
  width: 100%;
  margin-top: 10px;
  background: linear-gradient(135deg, #00d4ff, #0066ff, #6600ff);
  border: none;
  color: white;
  font-weight: 600;
  border-radius: 25px;
  padding: 12px 20px;
  box-shadow: 0 4px 20px rgba(0, 212, 255, 0.4);
  transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
  position: relative;
  overflow: hidden;
}
.new-chat-button::before {
  content: '';
  position: absolute;
  top: 0;
  left: -100%;
  width: 100%;
  height: 100%;
  background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent);
  transition: left 0.5s;
}
.new-chat-button:hover::before {
  left: 100%;
}
.new-chat-button:hover {
  transform: translateY(-3px) scale(1.02);
  box-shadow: 0 8px 30px rgba(0, 212, 255, 0.6);
}
.button-text {
  margin-left: 8px;
  font-size: 14px;
}
.main-content {
  flex: 1;
  padding: 20px;
  overflow-y: auto;
}
.chat-container {
  display: flex;
  flex-direction: column;
  height: 100%;
  max-width: 800px;
  margin: 0 auto;
}
.message-list {
  flex: 1;
  overflow-y: auto;
  padding: 25px;
  border: 1px solid rgba(0, 212, 255, 0.2);
  border-radius: 20px;
  background: rgba(26, 26, 58, 0.3);
  margin-bottom: 25px;
  display: flex;
  flex-direction: column;
  backdrop-filter: blur(20px);
  box-shadow: 
    0 8px 32px rgba(0, 0, 0, 0.3),
    inset 0 1px 0 rgba(255, 255, 255, 0.1);
  position: relative;
}
.message-list::after {
  content: '';
  position: absolute;
  top: 0;
  left: 0;
  right: 0;
  height: 1px;
  background: linear-gradient(90deg, transparent, rgba(0, 212, 255, 0.5), transparent);
}
.message {
  margin-bottom: 20px;
  padding: 18px 22px;
  border-radius: 18px;
  display: flex;
  align-items: flex-start;
  box-shadow: 0 4px 20px rgba(0, 0, 0, 0.2);
  transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
  position: relative;
  backdrop-filter: blur(10px);
  border: 1px solid rgba(255, 255, 255, 0.1);
}
.message:hover {
  transform: translateY(-2px);
  box-shadow: 0 8px 30px rgba(0, 0, 0, 0.3);
}
.user-message {
  max-width: 75%;
  background: linear-gradient(135deg, #00d4ff 0%, #0066ff 50%, #6600ff 100%);
  color: white;
  align-self: flex-end;
  flex-direction: row-reverse;
  margin-left: auto;
  border: 1px solid rgba(0, 212, 255, 0.3);
}
.bot-message {
  max-width: 80%;
  background: rgba(42, 42, 90, 0.6);
  color: #ffffff;
  align-self: flex-start;
  border: 1px solid rgba(0, 212, 255, 0.2);
  background-image: 
    linear-gradient(45deg, rgba(0, 212, 255, 0.05) 25%, transparent 25%),
    linear-gradient(-45deg, rgba(0, 212, 255, 0.05) 25%, transparent 25%),
    linear-gradient(45deg, transparent 75%, rgba(0, 212, 255, 0.05) 75%),
    linear-gradient(-45deg, transparent 75%, rgba(0, 212, 255, 0.05) 75%);
  background-size: 20px 20px;
  background-position: 0 0, 0 10px, 10px -10px, -10px 0px;
}
.message-icon {
  margin: 0 10px;
  font-size: 1.2em;
  color: #00d4ff;
}
.user-message .message-icon {
  color: #ffffff;
}
.loading-dots {
  padding-left: 5px;
}
.dot {
  display: inline-block;
  margin-left: 5px;
  width: 8px;
  height: 8px;
  background-color: #00d4ff;
  border-radius: 50%;
  animation: pulse 1.2s infinite ease-in-out both;
  box-shadow: 0 0 5px rgba(0, 212, 255, 0.5);
}
.dot:nth-child(2) {
  animation-delay: -0.6s;
}
@keyframes pulse {
  0%,
  100% {
    transform: scale(0.6);
    opacity: 0.4;
  }
  50% {
    transform: scale(1);
    opacity: 1;
  }
}
.input-container {
  display: flex;
  gap: 15px;
  align-items: center;
  padding: 20px;
  background: rgba(26, 26, 58, 0.3);
  border-radius: 25px;
  backdrop-filter: blur(20px);
  border: 1px solid rgba(0, 212, 255, 0.2);
  box-shadow: 0 8px 32px rgba(0, 0, 0, 0.2);
}
.input-container .el-input {
  flex: 1;
}
.input-container .el-input__inner {
  background: rgba(42, 42, 90, 0.4);
  border: 1px solid rgba(0, 212, 255, 0.3);
  border-radius: 20px;
  color: #ffffff;
  padding: 15px 20px;
  font-size: 15px;
  transition: all 0.3s ease;
  backdrop-filter: blur(10px);
}
.input-container .el-input__inner:focus {
  border-color: #00d4ff;
  box-shadow: 0 0 20px rgba(0, 212, 255, 0.3);
  background: rgba(42, 42, 90, 0.6);
}
.input-container .el-input__inner::placeholder {
  color: rgba(138, 138, 165, 0.7);
  font-style: italic;
}
.input-container .el-button {
  background: linear-gradient(135deg, #00d4ff, #0066ff, #6600ff);
  border: none;
  border-radius: 50%;
  color: white;
  font-weight: bold;
  width: 50px;
  height: 50px;
  box-shadow: 0 4px 20px rgba(0, 212, 255, 0.4);
  transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
  position: relative;
  overflow: hidden;
  flex-shrink: 0;
}
.input-container .el-button::before {
  content: '';
  position: absolute;
  top: 0;
  left: -100%;
  width: 100%;
  height: 100%;
  background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent);
  transition: left 0.5s;
}
.input-container .el-button:hover::before {
  left: 100%;
}
.input-container .el-button:hover {
  transform: translateY(-2px) scale(1.05);
  box-shadow: 0 8px 30px rgba(0, 212, 255, 0.6);
}
.input-container .el-button:active {
  transform: translateY(0) scale(0.95);
}
/* 媒體查詢，當(dāng)設(shè)備寬度小于等于 768px 時(shí)應(yīng)用以下樣式 */
@media (max-width: 768px) {
  .main-content {
    padding: 10px 0 10px 0;
  }
  .app-layout {
    flex-direction: column;
  }
  .sidebar {
    width: 100%;
    flex-direction: row;
    justify-content: space-between;
    align-items: center;
    padding: 10px;
    background: linear-gradient(90deg, #1a1a3a 0%, #0f0f23 100%);
  }
  .logo-section {
    flex-direction: row;
    align-items: center;
  }
  .car-icon {
    font-size: 32px;
    margin-right: 10px;
    margin-bottom: 0;
  }
  .logo-text {
    font-size: 18px;
    margin-bottom: 0;
  }
  .logo-subtitle {
    display: none;
  }
  .new-chat-button {
    margin-right: 30px;
    width: auto;
    margin-top: 5px;
    padding: 8px 16px;
    font-size: 14px;
  }
}
/* 媒體查詢，當(dāng)設(shè)備寬度大于 768px 時(shí)應(yīng)用原來(lái)的樣式 */
@media (min-width: 769px) {
  .main-content {
    padding: 0 0 10px 10px;
  }
  .app-layout {
    display: flex;
    height: 100vh;
  }
  .sidebar {
    width: 200px;
    background-color: #f4f4f9;
    padding: 20px;
    display: flex;
    flex-direction: column;
    align-items: center;
  }
  .logo-section {
    display: flex;
    flex-direction: column;
    align-items: center;
  }
  .logo-text {
    font-size: 18px;
    font-weight: bold;
    margin-top: 10px;
  }
  .new-chat-button {
    width: 100%;
    margin-top: 20px;
  }
}
.message-list::-webkit-scrollbar {
  width: 8px;
}
.message-list::-webkit-scrollbar-track {
  background: rgba(42, 42, 90, 0.3);
  border-radius: 4px;
}
.message-list::-webkit-scrollbar-thumb {
  background: linear-gradient(45deg, #00d4ff, #0066ff);
  border-radius: 4px;
}
.message-list::-webkit-scrollbar-thumb:hover {
  background: linear-gradient(45deg, #0099cc, #0044cc);
}
/* 添加科技感邊框動(dòng)畫(huà) */
.message-list {
  position: relative;
  overflow: hidden;
}
.message-list::before {
  content: '';
  position: absolute;
  top: -2px;
  left: -2px;
  right: -2px;
  bottom: -2px;
  background: linear-gradient(45deg, #00d4ff, #0066ff, #00d4ff);
  border-radius: 15px;
  opacity: 0.1;
  z-index: -1;
  animation: borderGlow 3s linear infinite;
}
@keyframes borderGlow {
  0%, 100% {
    opacity: 0.1;
  }
  50% {
    opacity: 0.3;
  }
}
/* 添加消息懸停效果 */
.message {
  position: relative;
  overflow: hidden;
}
.message::before {
  content: '';
  position: absolute;
  top: 0;
  left: -100%;
  width: 100%;
  height: 100%;
  background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.1), transparent);
  transition: left 0.5s;
}
.message:hover::before {
  left: 100%;
}
</style>