feat: 提交

2 months ago · 19f147a6e0
32 changed files with 1376 additions and 0 deletions
--- a/.gitignore
+++ b/.gitignore
@ -0,0 +1,33 @@
+ReadMe.md
+target/
+!.mvn/wrapper/maven-wrapper.jar
+!**/src/main/**/target/
+!**/src/test/**/target/
+
+### STS ###
+.apt_generated
+.classpath
+.factorypath
+.project
+.settings
+.springBeans
+.sts4-cache
+
+### IntelliJ IDEA ###
+.idea
+*.iws
+*.iml
+*.ipr
+
+### NetBeans ###
+/nbproject/private/
+/nbbuild/
+/dist/
+/nbdist/
+/.nb-gradle/
+build/
+!**/src/main/**/build/
+!**/src/test/**/build/
+
+### VS Code ###
+.vscode/
--- a/pom.xml
+++ b/pom.xml
@ -0,0 +1,152 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+    <parent>
+        <groupId>org.springframework.boot</groupId>
+        <artifactId>spring-boot-starter-parent</artifactId>
+        <version>3.2.6</version>
+        <relativePath/> <!-- lookup parent from repository -->
+    </parent>
+    <groupId>cn.ai</groupId>
+    <artifactId>Spring-Ai-Gu</artifactId>
+    <version>0.0.1-SNAPSHOT</version>
+    <name>Spring-Ai-Gu</name>
+    <description>Spring-Ai-Gu</description>
+
+
+    <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>
+    </properties>
+
+    <dependencies>
+        <!-- web应用程序核心依赖 -->
+        <dependency>
+            <groupId>org.springframework.boot</groupId>
+            <artifactId>spring-boot-starter-web</artifactId>
+        </dependency>
+        <!-- 编写和运行测试用例 -->
+        <dependency>
+            <groupId>org.springframework.boot</groupId>
+            <artifactId>spring-boot-starter-test</artifactId>
+            <scope>test</scope>
+        </dependency>
+        <!-- 前后端分离中的后端接口测试工具 -->
+        <dependency>
+            <groupId>com.github.xiaoymin</groupId>
+            <artifactId>knife4j-openapi3-jakarta-spring-boot-starter</artifactId>
+            <version>${knife4j.version}</version>
+        </dependency>
+        <!-- 基于open-ai的langchain4j接口：ChatGPT、deepseek都是open-ai标准下的大模型 -->
+        <!--        <dependency>-->
+        <!--            <groupId>dev.langchain4j</groupId>-->
+        <!--            <artifactId>langchain4j-open-ai</artifactId>-->
+        <!--        </dependency>-->
+        <!-- spring整合openai-->
+        <!--        <dependency>-->
+        <!--            <groupId>dev.langchain4j</groupId>-->
+        <!--            <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>-->
+        <!--        </dependency>-->
+
+        <!-- 接入阿里云百炼平台 -->
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId>
+        </dependency>
+
+        <!--langchain4j高级功能-->
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-spring-boot-starter</artifactId>
+        </dependency>
+        <!-- Spring Boot Starter Data mongodb -->
+        <dependency>
+            <groupId>org.springframework.boot</groupId>
+            <artifactId>spring-boot-starter-data-mongodb</artifactId>
+        </dependency>
+
+        <!-- Mysql Connector -->
+        <dependency>
+            <groupId>com.mysql</groupId>
+            <artifactId>mysql-connector-j</artifactId>
+        </dependency>
+        <!--mybatis-plus 持久层-->
+        <dependency>
+            <groupId>com.baomidou</groupId>
+            <artifactId>mybatis-plus-spring-boot3-starter</artifactId>
+            <version>${mybatis-plus.version}</version>
+        </dependency>
+
+        <!--解析pdf文档-->
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-document-parser-apache-pdfbox</artifactId>
+        </dependency>
+
+        <!--简单的rag实现-->
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-easy-rag</artifactId>
+        </dependency>
+        <!--向量存储 -->
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-pinecone</artifactId>
+        </dependency>
+
+        <!--流式输出-->
+        <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依赖管理清单-->
+            <dependency>
+                <groupId>org.springframework.boot</groupId>
+                <artifactId>spring-boot-dependencies</artifactId>
+                <version>${spring-boot.version}</version>
+                <type>pom</type>
+                <scope>import</scope>
+            </dependency>
+            <!--引入langchain4j依赖管理清单-->
+            <dependency>
+                <groupId>dev.langchain4j</groupId>
+                <artifactId>langchain4j-bom</artifactId>
+                <version>${langchain4j.version}</version>
+                <type>pom</type>
+                <scope>import</scope>
+            </dependency>
+            <!--引入百炼依赖管理清单-->
+            <dependency>
+                <groupId>dev.langchain4j</groupId>
+                <artifactId>langchain4j-community-bom</artifactId>
+                <version>${langchain4j.version}</version>
+                <type>pom</type>
+                <scope>import</scope>
+            </dependency>
+        </dependencies>
+    </dependencyManagement>
+
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.springframework.boot</groupId>
+                <artifactId>spring-boot-maven-plugin</artifactId>
+            </plugin>
+        </plugins>
+    </build>
+
+</project>
--- a/src/main/java/cn/ai/springaigu/SpringAiGuApplication.java
+++ b/src/main/java/cn/ai/springaigu/SpringAiGuApplication.java
@ -0,0 +1,13 @@
+package cn.ai.springaigu;
+
+import org.springframework.boot.SpringApplication;
+import org.springframework.boot.autoconfigure.SpringBootApplication;
+
+@SpringBootApplication
+public class SpringAiGuApplication {
+
+    public static void main(String[] args) {
+        SpringApplication.run(SpringAiGuApplication.class, args);
+    }
+
+}
--- a/src/main/java/cn/ai/springaigu/asistant/Assistant.java
+++ b/src/main/java/cn/ai/springaigu/asistant/Assistant.java
@ -0,0 +1,15 @@
+package cn.ai.springaigu.asistant;
+
+import dev.langchain4j.service.spring.AiService;
+
+import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT;
+
+/**
+ * 创建接
+ */
+//因为我们在配置文件中同时配置了多个大语言模型，所以需要在这里明确指定（EXPLICIT）模型的beanName（qwenChatModel）
+@AiService(wiringMode = EXPLICIT, chatModel = "qwenChatModel")
+public interface Assistant {
+
+    String chat(String userMessage);
+}
--- a/src/main/java/cn/ai/springaigu/asistant/MemoryChatAssistant.java
+++ b/src/main/java/cn/ai/springaigu/asistant/MemoryChatAssistant.java
@ -0,0 +1,17 @@
+package cn.ai.springaigu.asistant;
+
+import dev.langchain4j.service.UserMessage;
+import dev.langchain4j.service.spring.AiService;
+
+import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT;
+
+// 使用AIService实现聊天记忆
+@AiService(
+        wiringMode = EXPLICIT,
+        chatModel = "qwenChatModel",
+        chatMemory = "chatMemory"
+)
+public interface MemoryChatAssistant {
+    @UserMessage("你是我的好朋友，请用上海话回答问题，并且添加一些表情符号。 {{it}}") //{{it}}表示这里唯一的参数的占位符
+    String chat(String message);
+}
--- a/src/main/java/cn/ai/springaigu/asistant/SeparateChatAssistant.java
+++ b/src/main/java/cn/ai/springaigu/asistant/SeparateChatAssistant.java
@ -0,0 +1,44 @@
+package cn.ai.springaigu.asistant;
+
+import dev.langchain4j.service.MemoryId;
+import dev.langchain4j.service.SystemMessage;
+import dev.langchain4j.service.UserMessage;
+import dev.langchain4j.service.V;
+import dev.langchain4j.service.spring.AiService;
+
+import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT;
+
+// 创建记忆隔离对话智能体
+@AiService(
+        wiringMode = EXPLICIT,
+        chatModel = "qwenChatModel",
+        chatMemory = "chatMemory",
+        chatMemoryProvider = "chatMemoryProvider",
+        tools = "calculatorTools" //配置tools
+)
+public interface SeparateChatAssistant {
+    /**
+     * 分离聊天记录
+     *
+     * @param memoryId    聊天id
+     * @param userMessage 用户消息
+     */
+    // @SystemMessage("你是我的好朋友，请用东北话回答问题。")//系统消息提示词
+    //@SystemMessage("你是我的好朋友，请用东北话回答问题。今天是{{current_date}}")//系统消息提示词,在提示词中添加当前日期的占位符{{current_date}}
+    @SystemMessage(fromResource = "my-prompt-template.txt")
+    String chat(@MemoryId int memoryId, @UserMessage String userMessage);
+
+
+    @UserMessage("你是我的好朋友，请用粤语回答问题。{{message}}")
+    String chat2(@MemoryId int memoryId, @V("message") String userMessage);
+
+
+    @SystemMessage(fromResource = "my-prompt-template3.txt")
+    String chat3(
+            @MemoryId int memoryId,
+            @UserMessage String userMessage,
+            @V("username") String username,
+            @V("age") int age
+    );
+
+}
--- a/src/main/java/cn/ai/springaigu/asistant/XiaozhiAgent.java
+++ b/src/main/java/cn/ai/springaigu/asistant/XiaozhiAgent.java
@ -0,0 +1,20 @@
+package cn.ai.springaigu.asistant;
+
+import dev.langchain4j.service.*;
+import dev.langchain4j.service.spring.AiService;
+import reactor.core.publisher.Flux;
+
+import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT;
+
+
+@AiService(
+        wiringMode = EXPLICIT,
+        //chatModel = "qwenChatModel",
+        streamingChatModel = "qwenStreamingChatModel",
+        chatMemoryProvider = "chatMemoryProviderXiaozhi",
+        tools = "appointmentTools", //tools配置
+        contentRetriever = "contentRetrieverXiaozhiPincone") //配置向量存储
+public interface XiaozhiAgent {
+    @SystemMessage(fromResource = "zhaozhi-prompt-template.txt")
+    Flux<String> chat(@MemoryId Long memoryId, @UserMessage String userMessage);
+}
--- a/src/main/java/cn/ai/springaigu/config/EmbeddingStoreConfig.java
+++ b/src/main/java/cn/ai/springaigu/config/EmbeddingStoreConfig.java
@ -0,0 +1,36 @@
+package cn.ai.springaigu.config;
+
+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.beans.factory.annotation.Value;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+@Configuration
+public class EmbeddingStoreConfig {
+    @Value("${pinecone.api-key}")
+    private String apiKey;
+
+    @Autowired
+    private EmbeddingModel embeddingModel;
+
+    @Bean
+    public EmbeddingStore<TextSegment> embeddingStore() {
+        //创建向量存储
+        EmbeddingStore<TextSegment> embeddingStore = PineconeEmbeddingStore.builder()
+                .apiKey(apiKey)
+                .index("ai-study-index")//如果指定的索引不存在，将创建一个新的索引
+                .nameSpace("ai-study-namespace") //如果指定的名称空间不存在，将创建一个新的名称 空间
+                .createIndex(PineconeServerlessIndexConfig.builder()
+                        .cloud("AWS") //指定索引部署在 AWS 云服务上。
+                        .region("us-east-1") //指定索引所在的 AWS 区域为 us-east-1。
+                        .dimension(embeddingModel.dimension()) //指定索引的向量维度，该维度与 embeddedModel 生成的向量维度相同。
+                        .build())
+                .build();
+        return embeddingStore;
+    }
+}
--- a/src/main/java/cn/ai/springaigu/config/MemoryChatAssistantConfig.java
+++ b/src/main/java/cn/ai/springaigu/config/MemoryChatAssistantConfig.java
@ -0,0 +1,16 @@
+package cn.ai.springaigu.config;
+
+import dev.langchain4j.memory.ChatMemory;
+import dev.langchain4j.memory.chat.MessageWindowChatMemory;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+//  配置ChatMemory
+@Configuration
+public class MemoryChatAssistantConfig {
+    @Bean
+    ChatMemory chatMemory() {
+        //设置聊天记忆记录的message数量
+        return MessageWindowChatMemory.withMaxMessages(10);
+    }
+}
--- a/src/main/java/cn/ai/springaigu/config/SeparateChatAssistantConfig.java
+++ b/src/main/java/cn/ai/springaigu/config/SeparateChatAssistantConfig.java
@ -0,0 +1,24 @@
+package cn.ai.springaigu.config;
+
+import cn.ai.springaigu.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)//配置持久化对象
+                .build();
+    }
+}
--- a/src/main/java/cn/ai/springaigu/config/XiaozhiAgentConfig.java
+++ b/src/main/java/cn/ai/springaigu/config/XiaozhiAgentConfig.java
@ -0,0 +1,74 @@
+package cn.ai.springaigu.config;
+
+import cn.ai.springaigu.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;
+
+    @Bean
+    ChatMemoryProvider chatMemoryProviderXiaozhi() {
+        return memoryId -> MessageWindowChatMemory.builder()
+                .id(memoryId)
+                .maxMessages(20)
+                .chatMemoryStore(mongoChatMemoryStore)
+                .build();
+    }
+
+    @Bean
+    ContentRetriever contentRetrieverXiaozhi() {
+        //使用FileSystemDocumentLoader读取指定目录下的知识库文档
+        //并使用默认的文档解析器对文档进行解析
+        Document document1 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/医院信息.md");
+        Document document2 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/科室信息.md");
+        Document document3 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/神经内科.md");
+        List<Document> documents = Arrays.asList(document1, document2, document3);
+        //使用内存向量存储 生产不建议使用
+        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
+        //使用默认的文档分割器
+        EmbeddingStoreIngestor.ingest(documents, embeddingStore);
+        //从嵌入存储（EmbeddingStore）里检索和查询内容相关的信息
+        return EmbeddingStoreContentRetriever.from(embeddingStore);
+    }
+
+    @Autowired
+    private EmbeddingStore embeddingStore;
+    @Autowired
+    private EmbeddingModel embeddingModel;
+
+    @Bean
+    ContentRetriever contentRetrieverXiaozhiPincone() {
+        // 创建一个 EmbeddingStoreContentRetriever 对象，用于从嵌入存储中检索内容
+        return EmbeddingStoreContentRetriever
+                .builder()
+                // 设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储
+                .embeddingStore(embeddingStore)
+                // 设置最大检索结果数量，这里表示最多返回 1 条匹配结果
+                .maxResults(1)
+                // 设置最小得分阈值，只有得分大于等于 0.8 的结果才会被返回
+                .minScore(0.8)
+                // 构建最终的 EmbeddingStoreContentRetriever 实例
+                .build();
+    }
+}
--- a/src/main/java/cn/ai/springaigu/controller/XiaozhiController.java
+++ b/src/main/java/cn/ai/springaigu/controller/XiaozhiController.java
@ -0,0 +1,49 @@
+package cn.ai.springaigu.controller;
+
+import cn.ai.springaigu.asistant.XiaozhiAgent;
+import cn.ai.springaigu.domain.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 = "硅谷小智")
+@RestController
+@RequestMapping("/xiaozhi")
+public class XiaozhiController {
+    @Autowired
+    private XiaozhiAgent xiaozhiAgent;
+//    @Operation(summary = "对话")
+//    @PostMapping("/chat")
+//    public String chat(@RequestBody ChatForm chatForm) {
+//        return xiaozhiAgent.chat(chatForm.getMemoryId(), chatForm.getMessage());
+//    }
+
+    @Operation(summary = "对话")
+    @PostMapping(value = "/chat", produces = "text/stream;charset=utf-8")
+    public Flux<String> chat(@RequestBody ChatForm chatForm) {
+        return xiaozhiAgent.chat(chatForm.getMemoryId(), chatForm.getMessage());
+    }
+}
+
+
+// 测试：
+//{
+//  "memoryId": 0,
+//  "message": "我想预约明天上午的神经内科"
+//}
+// {
+//  "memoryId": 1,
+//  "message": "帮我预约一下后天的皮肤科，信息是：翠花,523921199204234816,刘医生"
+//}
+//{
+//  "memoryId": 1,
+//  "message": "对不起，临时有事，帮我取消一下预约"
+//}
+// 我头疼应该怎么处理
+// 姓名：姚翠花、230104190000000000、神经内科、明天下午，最好的医生。
+// 医院的位置在哪，神经内科怎么找
--- a/src/main/java/cn/ai/springaigu/domain/Appointment.java
+++ b/src/main/java/cn/ai/springaigu/domain/Appointment.java
@ -0,0 +1,21 @@
+package cn.ai.springaigu.domain;
+
+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;
+}
--- a/src/main/java/cn/ai/springaigu/domain/ChatForm.java
+++ b/src/main/java/cn/ai/springaigu/domain/ChatForm.java
@ -0,0 +1,9 @@
+package cn.ai.springaigu.domain;
+
+import lombok.Data;
+
+@Data
+public class ChatForm {
+    private Long memoryId;//对话id
+    private String message;//用户问题
+}
--- a/src/main/java/cn/ai/springaigu/domain/ChatMessages.java
+++ b/src/main/java/cn/ai/springaigu/domain/ChatMessages.java
@ -0,0 +1,21 @@
+package cn.ai.springaigu.domain;
+
+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 {
+
+    //唯一标识，映射到 MongoDB 文档的 _id 字段
+    @Id
+    private ObjectId id;
+    private Long messageId;
+    private String content; //存储当前聊天记录列表的json字符串
+}
--- a/src/main/java/cn/ai/springaigu/mapper/AppointmentMapper.java
+++ b/src/main/java/cn/ai/springaigu/mapper/AppointmentMapper.java
@ -0,0 +1,9 @@
+package cn.ai.springaigu.mapper;
+
+import cn.ai.springaigu.domain.Appointment;
+import com.baomidou.mybatisplus.core.mapper.BaseMapper;
+import org.apache.ibatis.annotations.Mapper;
+
+@Mapper
+public interface AppointmentMapper extends BaseMapper<Appointment> {
+}
--- a/src/main/java/cn/ai/springaigu/service/AppointmentService.java
+++ b/src/main/java/cn/ai/springaigu/service/AppointmentService.java
@ -0,0 +1,8 @@
+package cn.ai.springaigu.service;
+
+import cn.ai.springaigu.domain.Appointment;
+import com.baomidou.mybatisplus.extension.service.IService;
+
+public interface AppointmentService extends IService<Appointment> {
+    Appointment getOne(Appointment appointment);
+}
--- a/src/main/java/cn/ai/springaigu/service/impl/AppointmentServiceImpl.java
+++ b/src/main/java/cn/ai/springaigu/service/impl/AppointmentServiceImpl.java
@ -0,0 +1,27 @@
+package cn.ai.springaigu.service.impl;
+
+import cn.ai.springaigu.domain.Appointment;
+import cn.ai.springaigu.mapper.AppointmentMapper;
+import cn.ai.springaigu.service.AppointmentService;
+import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
+import com.baomidou.mybatisplus.extension.service.impl.ServiceImpl;
+import org.springframework.stereotype.Service;
+
+@Service
+public class AppointmentServiceImpl extends ServiceImpl<AppointmentMapper, Appointment> implements AppointmentService {
+    /**
+     * 查询订单是否存在
+     *
+     * @param appointment
+     */
+    @Override
+    public Appointment getOne(Appointment appointment) {
+        LambdaQueryWrapper<Appointment> queryWrapper = new LambdaQueryWrapper<>();
+        queryWrapper.eq(Appointment::getUsername, appointment.getUsername());
+        queryWrapper.eq(Appointment::getIdCard, appointment.getIdCard());
+        queryWrapper.eq(Appointment::getDepartment, appointment.getDepartment());
+        queryWrapper.eq(Appointment::getDate, appointment.getDate());
+        queryWrapper.eq(Appointment::getTime, appointment.getTime());
+        return baseMapper.selectOne(queryWrapper);
+    }
+}
--- a/src/main/java/cn/ai/springaigu/store/MongoChatMemoryStore.java
+++ b/src/main/java/cn/ai/springaigu/store/MongoChatMemoryStore.java
@ -0,0 +1,46 @@
+package cn.ai.springaigu.store;
+
+import cn.ai.springaigu.domain.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 criteria = Criteria.where("memoryId").is(memoryId);
+        Query query = new Query(criteria);
+        ChatMessages chatMessages = mongoTemplate.findOne(query, ChatMessages.class);
+        if(chatMessages == null) return new LinkedList<>();
+        return ChatMessageDeserializer.messagesFromJson(chatMessages.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));
+        //根据query条件能查询出文档，则修改文档；否则新增文档
+        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);
+    }
+}
--- a/src/main/java/cn/ai/springaigu/tools/AppointmentTools.java
+++ b/src/main/java/cn/ai/springaigu/tools/AppointmentTools.java
@ -0,0 +1,66 @@
+package cn.ai.springaigu.tools;
+
+import cn.ai.springaigu.domain.Appointment;
+import cn.ai.springaigu.service.AppointmentService;
+import dev.langchain4j.agent.tool.P;
+import dev.langchain4j.agent.tool.Tool;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.stereotype.Component;
+
+@Component
+public class AppointmentTools {
+
+    @Autowired
+    private AppointmentService appointmentService;
+
+    // @Tool(name = "预约挂号", value = "根据参数，先执行工具方法queryDepartment查询是否可预约，并直接给用户回答是否可预约，并让用户确认所有预约信息，用户确认后再进行预约。")
+    @Tool(name="预约挂号", value = "根据参数，先执行工具方法queryDepartment查询是否可预约，并直接给用户回答是否可预约，并让用户确认所有预约信息，用户确认后再进行预约。如果用户没有提供具体的医生姓名，请从向量存储中找到一位医生。")
+    public String bookAppointment(Appointment appointment) {
+        //查找数据库中是否包含对应的预约记录
+        Appointment appointmentDB = appointmentService.getOne(appointment);
+        if (appointmentDB == null) {
+            appointment.setId(null);//防止大模型幻觉设置了id
+            if (appointmentService.save(appointment)) {
+                return "预约成功，并返回预约详情";
+            } else {
+                return "预约失败";
+            }
+        }
+        return "您在相同的科室和时间已有预约";
+    }
+
+
+    @Tool(name = "取消预约挂号", value = "根据参数，查询预约是否存在，如果存在则删除预约记录并返回取消预约成功，否则返回取消预约失败")
+    public String cancelAppointment(Appointment appointment) {
+        Appointment appointmentDB = appointmentService.getOne(appointment);
+        if (appointmentDB != null) {
+            //删除预约记录
+            if (appointmentService.removeById(appointmentDB.getId())) {
+                return "取消预约成功";
+            } else {
+                return "取消预约失败";
+            }
+        }
+        //取消失败
+        return "您没有预约记录，请核对预约科室和时间";
+    }
+
+    @Tool(name = "查询是否有号源", value = "根据科室名称，日期，时间和医生查询是否有号源，并返回给用户")
+    public boolean queryDepartment(
+            @P(value = "科室名称") String name,
+            @P(value = "日期") String date,
+            @P(value = "时间，可选值：上午、下午") String time,
+            @P(value = "医生名称", required = false) String doctorName
+    ) {
+        System.out.println("查询是否有号源");
+        System.out.println("科室名称：" + name);
+        System.out.println("日期：" + date);
+        System.out.println("时间：" + time);
+        System.out.println("医生名称：" + doctorName);
+        //TODO 维护医生的排班信息：
+        //如果没有指定医生名字，则根据其他条件查询是否有可以预约的医生（有返回true，否则返回false）；
+        //如果指定了医生名字，则判断医生是否有排班（没有排版返回false）
+        //如果有排班，则判断医生排班时间段是否已约满（约满返回false，有空闲时间返回true）
+        return true;
+    }
+}
--- a/src/main/java/cn/ai/springaigu/tools/CalculatorTools.java
+++ b/src/main/java/cn/ai/springaigu/tools/CalculatorTools.java
@ -0,0 +1,36 @@
+package cn.ai.springaigu.tools;
+
+import dev.langchain4j.agent.tool.P;
+import dev.langchain4j.agent.tool.Tool;
+import dev.langchain4j.agent.tool.ToolMemoryId;
+import org.springframework.stereotype.Component;
+
+@Component
+public class CalculatorTools {
+
+//    @Tool
+//    double sum(double a, double b) {
+//        System.out.println("调用加法运算");
+//        return a + b;
+//    }
+//    @Tool
+//    double squareRoot(double x) {
+//        System.out.println("调用平方根运算");
+//        return Math.sqrt(x);
+//    }
+
+    @Tool(name = "加法", value = "返回两个参数相加之和")
+    double sum(
+            @ToolMemoryId int memoryId,
+            @P(value="加数1", required = true) double a,
+            @P(value="加数2", required = true) double b) {
+        System.out.println("调用加法运算 " + memoryId);
+        return a + b;
+    }
+    @Tool(name = "平方根", value = "返回给定参数的平方根")
+    double squareRoot(
+            @ToolMemoryId int memoryId, double x) {
+        System.out.println("调用平方根运算 " + memoryId);
+        return Math.sqrt(x);
+    }
+}
--- a/src/main/resources/application.yml
+++ b/src/main/resources/application.yml
@ -0,0 +1,46 @@
+# langchain4j测试模型
+langchain4j:
+  open-ai:
+     chat-model:
+       base-url: https://api.deepseek.com
+       api-key: sk-d2c693bbb4c64956809a03d7f40e3e70
+       # 推理模型 deepseek-reasoner
+       model-name: deepseek-chat
+       log-requests: true
+       log-responses: true
+  community:
+    dashscope:
+      chat-model:
+        api-key: sk-10a57fb7b31e4806ab5c4ea6ffa02f5a
+        model-name: qwen-max
+      embedding-model:
+        api-key: sk-10a57fb7b31e4806ab5c4ea6ffa02f5a
+        model-name: text-embedding-v3
+      streaming-chat-model:
+        api-key: sk-10a57fb7b31e4806ab5c4ea6ffa02f5a
+        model-name: qwen-plus
+
+spring:
+  data:
+    mongodb:
+      uri: mongodb://localhost:27017/chat_memory_db
+  datasource:
+    driver-class-name: com.mysql.cj.jdbc.Driver
+    url: jdbc:mysql://117.72.43.105:3306/ai_study?useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai&useSSL=false
+    username: root
+    password: Yuan625621105.
+
+pinecone:
+  api-key: pcsk_5qjBiY_FrVVSC7NHrMem43SrCJ4CK6EGXduToZKZmJ4dGFDonGJPmVux2mQNiyqypTvkVx
+
+# 开启 SQL 日志打印
+mybatis-plus:
+    configuration:
+       log-impl: org.apache.ibatis.logging.stdout.StdOutImpl
+
+
+
+# 启用日志debug级别
+logging:
+   level:
+     root: info
--- a/src/main/resources/mapper/AppointmentMapper.xml
+++ b/src/main/resources/mapper/AppointmentMapper.xml
@ -0,0 +1,5 @@
+<?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="cn.ai.springaigu.mapper.AppointmentMapper">
+</mapper>
--- a/src/main/resources/my-prompt-template.txt
+++ b/src/main/resources/my-prompt-template.txt
@ -0,0 +1,3 @@
+你是我的好朋友，请用东北话回答问题，回答问题的时候适当添加表情符号。
+今天是 {{current_date}}。
+
--- a/src/main/resources/my-prompt-template3.txt
+++ b/src/main/resources/my-prompt-template3.txt
@ -0,0 +1,3 @@
+你是我的好朋友，我是{{username}}，我的年龄是{{age}}，请用东北话回答问题，回答问题的时候适当添加表情
+符号。
+今天是 {{current_date}}。
--- a/src/main/resources/zhaozhi-prompt-template.txt
+++ b/src/main/resources/zhaozhi-prompt-template.txt
@ -0,0 +1,18 @@
+你的名字是“硅谷小智”，你是一家名为“北京协和医院”的智能客服。
+你是一个训练有素的医疗顾问和医疗伴诊助手。
+你态度友好、礼貌且言辞简洁。
+1、请仅在用户发起第一次会话时，和用户打个招呼，并介绍你是谁。
+2、作为一个训练有素的医疗顾问：
+请基于当前临床实践和研究，针对患者提出的特定健康问题，提供详细、准确且实用的医疗建议。请同时考虑可能的病
+因、诊断流程、治疗方案以及预防措施，并给出在不同情境下的应对策略。对于药物治疗，请特别指明适用的药品名
+称、剂量和疗程。如果需要进一步的检查或就医，也请明确指示。
+3、作为医疗伴诊助手，你可以回答用户就医流程中的相关问题，主要包含以下功能：
+AI分导诊：根据患者的病情和就医需求，智能推荐最合适的科室。
+AI挂号助手：实现智能查询是否有挂号号源服务；实现智能预约挂号服务；实现智能取消挂号服务。
+4、你必须遵守的规则如下：
+在获取挂号预约详情或取消挂号预约之前，你必须确保自己知晓用户的姓名（必选）、身份证号（必选）、预约科室
+（必选）、预约日期（必选，格式举例：2025-04-14）、预约时间（必选，格式：上午 或 下午）、预约医生（可
+选）。
+当被问到其他领域的咨询时，要表示歉意并说明你无法在这方面提供帮助。
+5、请在回答的结果中适当包含一些轻松可爱的图标和表情。
+6、今天是 {{current_date}}。
--- a/src/test/java/cn/ai/springaigu/AppointmentServiceTest.java
+++ b/src/test/java/cn/ai/springaigu/AppointmentServiceTest.java
@ -0,0 +1,42 @@
+package cn.ai.springaigu;
+
+import cn.ai.springaigu.domain.Appointment;
+import cn.ai.springaigu.service.AppointmentService;
+import org.junit.jupiter.api.Test;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.boot.test.context.SpringBootTest;
+
+@SpringBootTest
+class AppointmentServiceTest {
+    @Autowired
+    private AppointmentService appointmentService;
+
+    @Test
+    void testGetOne() {
+        Appointment appointment = new Appointment();
+        appointment.setUsername("张三");
+        appointment.setIdCard("123456789012345678");
+        appointment.setDepartment("内科");
+        appointment.setDate("2025-04-14");
+        appointment.setTime("上午");
+        Appointment appointmentDB = appointmentService.getOne(appointment);
+        System.out.println(appointmentDB);
+    }
+
+    @Test
+    void testSave() {
+        Appointment appointment = new Appointment();
+        appointment.setUsername("张三");
+        appointment.setIdCard("123456789012345678");
+        appointment.setDepartment("内科");
+        appointment.setDate("2025-04-14");
+        appointment.setTime("上午");
+        appointment.setDoctorName("张医生");
+        appointmentService.save(appointment);
+    }
+
+    @Test
+    void testRemoveById() {
+        appointmentService.removeById(1L);
+    }
+}
--- a/src/test/java/cn/ai/springaigu/EmbeddingTest.java
+++ b/src/test/java/cn/ai/springaigu/EmbeddingTest.java
@ -0,0 +1,94 @@
+package cn.ai.springaigu;
+
+import dev.langchain4j.data.document.Document;
+import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
+import dev.langchain4j.data.embedding.Embedding;
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.model.embedding.EmbeddingModel;
+import dev.langchain4j.model.output.Response;
+import dev.langchain4j.store.embedding.*;
+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 EmbeddingModel embeddingModel;
+
+    @Test
+    public void testEmbeddingModel() {
+        Response<Embedding> embed = embeddingModel.embed("你好");
+        System.out.println("向量维度：" + embed.content().vector().length);
+        System.out.println("向量输出：" + embed.toString());
+    }
+
+    // 测试向量存储
+    @Autowired
+    private EmbeddingStore embeddingStore;
+
+
+    /**
+     * 将文本转换成向量，然后存储到pinecone中
+     * <p>
+     * 参考：
+     * https://docs.langchain4j.dev/tutorials/embedding-stores
+     */
+    @Test
+    public void testPineconeEmbeded() {
+        //将文本转换成向量
+        TextSegment segment1 = TextSegment.from("我喜欢羽毛球");
+        Embedding embedding1 = embeddingModel.embed(segment1).content();
+        //存入向量数据库
+        embeddingStore.add(embedding1, segment1);
+        TextSegment segment2 = TextSegment.from("今天天气很好");
+        Embedding embedding2 = embeddingModel.embed(segment2).content();
+        embeddingStore.add(embedding2, segment2);
+    }
+
+    /**
+     * Pinecone-相似度匹配
+     */
+    @Test
+    public void embeddingSearch() {
+        //提问，并将问题转成向量数据
+        Embedding queryEmbedding = embeddingModel.embed("你最喜欢的运动是什么？").content();
+        //创建搜索请求对象
+        EmbeddingSearchRequest searchRequest = EmbeddingSearchRequest.builder()
+                .queryEmbedding(queryEmbedding)
+                .maxResults(1) //匹配最相似的一条记录
+                //.minScore(0.8)
+                .build();
+        //根据搜索请求 searchRequest 在向量存储中进行相似度搜索
+        EmbeddingSearchResult<TextSegment> searchResult = embeddingStore.search(searchRequest);
+        //searchResult.matches()：获取搜索结果中的匹配项列表。
+        //.get(0)：从匹配项列表中获取第一个匹配项
+        EmbeddingMatch<TextSegment> embeddingMatch = searchResult.matches().get(0);
+        //获取匹配项的相似度得分
+        System.out.println(embeddingMatch.score()); // 0.8144288515898701
+        //返回文本结果
+        System.out.println(embeddingMatch.embedded().text());
+    }
+
+    // 上传知识库到Pinecone
+    @Test
+    public void testUploadKnowledgeLibrary() {
+        //使用FileSystemDocumentLoader读取指定目录下的知识库文档
+        //并使用默认的文档解析器对文档进行解析
+        Document document1 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/医院信息.md");
+        Document document2 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/科室信息.md");
+        Document document3 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/神经内科.md");
+        List<Document> documents = Arrays.asList(document1, document2, document3);
+        //文本向量化并存入向量数据库：将每个片段进行向量化，得到一个嵌入向量
+        EmbeddingStoreIngestor
+                .builder()
+                .embeddingStore(embeddingStore)
+                .embeddingModel(embeddingModel)
+                .build()
+                .ingest(documents);
+    }
+}
--- a/src/test/java/cn/ai/springaigu/MongoCrudTest.java
+++ b/src/test/java/cn/ai/springaigu/MongoCrudTest.java
@ -0,0 +1,77 @@
+package cn.ai.springaigu;
+
+import cn.ai.springaigu.domain.ChatMessages;
+import org.junit.jupiter.api.Test;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.boot.test.context.SpringBootTest;
+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;
+
+@SpringBootTest
+public class MongoCrudTest {
+    @Autowired
+    private MongoTemplate mongoTemplate;
+
+    /**
+     * 插入文档
+     */
+    /*
+    @Test
+    public void testInsert() {
+    mongoTemplate.insert(new ChatMessages(1L, "聊天记录"));
+    }*/
+
+    /**
+     * 插入文档
+     */
+    @Test
+    public void testInsert2() {
+        ChatMessages chatMessages = new ChatMessages();
+        chatMessages.setContent("聊天记录列表");
+        mongoTemplate.insert(chatMessages);
+    }
+
+    /**
+     * 根据id查询文档
+     */
+    @Test
+    public void testFindById() {
+        ChatMessages chatMessages = mongoTemplate.findById("6801ead733ba9c4a0d9b6c7b", ChatMessages.class);
+        System.out.println(chatMessages);
+    }
+    /**
+     * 修改文档
+     */
+    @Test
+    public void testUpdate() {
+        Criteria criteria = Criteria.where("_id").is("6801ead733ba9c4a0d9b6c7b");
+        Query query = new Query(criteria);
+        Update update = new Update();
+        update.set("content", "新的聊天记录列表");
+        //修改或新增
+        mongoTemplate.upsert(query, update, ChatMessages.class);
+    }
+    /**
+     * 新增或修改文档
+     */
+    @Test
+    public void testUpdate2() {
+        Criteria criteria = Criteria.where("_id").is("100");
+        Query query = new Query(criteria);
+        Update update = new Update();
+        update.set("content", "新的聊天记录列表");
+        //修改或新增
+        mongoTemplate.upsert(query, update, ChatMessages.class);
+    }
+    /**
+     * 删除文档
+     */
+    @Test
+    public void testDelete() {
+        Criteria criteria = Criteria.where("_id").is("100");
+        Query query = new Query(criteria);
+        mongoTemplate.remove(query, ChatMessages.class);
+    }
+}
--- a/src/test/java/cn/ai/springaigu/RAGTest.java
+++ b/src/test/java/cn/ai/springaigu/RAGTest.java
@ -0,0 +1,137 @@
+package cn.ai.springaigu;
+
+import dev.langchain4j.data.document.Document;
+import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
+import dev.langchain4j.data.document.parser.TextDocumentParser;
+import dev.langchain4j.data.document.parser.apache.pdfbox.ApachePdfBoxDocumentParser;
+import dev.langchain4j.data.document.splitter.DocumentByParagraphSplitter;
+import dev.langchain4j.data.embedding.Embedding;
+import dev.langchain4j.data.message.UserMessage;
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.model.embedding.EmbeddingModel;
+import dev.langchain4j.model.embedding.onnx.HuggingFaceTokenizer;
+import dev.langchain4j.store.embedding.*;
+import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
+import org.junit.jupiter.api.Test;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.boot.test.context.SpringBootTest;
+
+import java.nio.file.FileSystems;
+import java.nio.file.PathMatcher;
+import java.util.Arrays;
+import java.util.List;
+
+
+@SpringBootTest
+public class RAGTest {
+
+
+    // 文档加载
+    @Test
+    public void testReadDocument() {
+//        // 加载单个文档
+//        Document document = FileSystemDocumentLoader.loadDocument("E:/knowledge/file.txt", new TextDocumentParser());
+//        // 从一个目录中加载所有文档
+//        List<Document> documents = FileSystemDocumentLoader.loadDocuments("E:/knowledge", new TextDocumentParser());
+//        // 从一个目录中加载所有的.txt文档
+//        PathMatcher pathMatcher = FileSystems.getDefault().getPathMatcher("glob:*.txt");
+//        List<Document> documents = FileSystemDocumentLoader.loadDocuments("E:/knowledge",pathMatcher, new TextDocumentParser());
+//        // 从一个目录及其子目录中加载所有文档
+//        List<Document> documents = FileSystemDocumentLoader.loadDocumentsRecursively("E:/knowledge", new TextDocumentParser());
+
+        //使用FileSystemDocumentLoader读取指定目录下的知识库文档
+        //并使用默认的文档解析器TextDocumentParser对文档进行解析
+        Document document = FileSystemDocumentLoader.loadDocument("D:\\IdeaProjects\\learn\\Spring-Ai-Gu\\src\\main\\resources\\my-prompt-template.txt");
+        System.out.println(document.text());
+    }
+
+    // 来自 langchain4j 模块的文本文档解析器（TextDocumentParser），它能够解析纯文本格式的文件（例如 TXT、HTML、MD 等）。
+    //  ApachePdfBoxDocumentParser 解析PDF
+    //  ApachePoiDocumentParser 解析微软办公软件的文件格式（例如 DOC、DOCX、PPT、PPTX、XLS、XLSX 等）
+    //  ApacheTikaDocumentParser 以自动检测并解析几乎所有现有的文件格式
+
+    /**
+     * 解析PDF
+     */
+    @Test
+    public void testParsePDF() {
+        Document document = FileSystemDocumentLoader.loadDocument("C:\\Users\\admin\\Desktop\\工作\\幼儿园E-portfolio\\Biannual Portfolios\\N1：认知、语言.pdf", new ApachePdfBoxDocumentParser());
+        System.out.println(document);
+    }
+
+    // 文档分割
+    // 按段落文档分割器（DocumentByParagraphSplitter）
+    // 按行文档分割器（DocumentByLineSplitter）
+    // 按句子文档分割器（DocumentBySentenceSplitter）
+    // 按单词文档分割器（DocumentByWordSplitter）
+    // 按字符文档分割器（DocumentByCharacterSplitter）
+    // 按正则表达式文档分割器（DocumentByRegexSplitter）
+    // 递归分割：DocumentSplitters.recursive (...)
+    // 默认情况下每个文本片段最多不能超过300个token
+
+    /**
+     * 加载文档并存入向量数据库
+     */
+    @Test
+    public void testReadDocumentAndStore() {
+        //使用FileSystemDocumentLoader读取指定目录下的知识库文档
+        //并使用默认的文档解析器对文档进行解析(TextDocumentParser)
+        Document document = FileSystemDocumentLoader.loadDocument("C:\\Users\\admin\\Desktop\\评估报告.md");
+        //为了简单起见，我们暂时使用基于内存的向量存储
+        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
+        //ingest
+        //1、分割文档：默认使用递归分割器，将文档分割为多个文本片段，每个片段包含不超过 300个token，并且 有 30 个token的重叠部分保证连贯性
+        //DocumentByParagraphSplitter(DocumentByLineSplitter(DocumentBySentenceSplitter(DocumentByWordSplitter)))
+        //2、文本向量化：使用一个LangChain4j内置的轻量化向量模型对每个文本片段进行向量化
+        //3、将原始文本和向量存储到向量数据库中(InMemoryEmbeddingStore)
+        EmbeddingStoreIngestor.ingest(document, embeddingStore);
+        //查看向量数据库内容
+        System.out.println(embeddingStore);
+    }
+
+
+    /**
+     * 文档分割
+     */
+    @Test
+    public void testDocumentSplitter() {
+        //使用FileSystemDocumentLoader读取指定目录下的知识库文档
+        //并使用默认的文档解析器对文档进行解析(TextDocumentParser)
+        Document document = FileSystemDocumentLoader.loadDocument("C:\\Users\\admin\\Desktop\\评估报告.md");
+        //为了简单起见，我们暂时使用基于内存的向量存储
+        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
+        //自定义文档分割器
+        //按段落分割文档：每个片段包含不超过 300个token，并且有 30个token的重叠部分保证连贯性
+        //注意：当段落长度总和小于设定的最大长度时，就不会有重叠的必要。
+        DocumentByParagraphSplitter documentSplitter = new DocumentByParagraphSplitter(
+                300,
+                30,
+                //token分词器：按token计算
+                new HuggingFaceTokenizer());
+        //按字符计算
+        //DocumentByParagraphSplitter documentSplitter = new DocumentByParagraphSplitter(300, 30);
+        EmbeddingStoreIngestor
+                .builder()
+                .embeddingStore(embeddingStore)
+                .documentSplitter(documentSplitter)
+                .build()
+                .ingest(document);
+    }
+
+    /**
+     * 计算 token 长度
+     */
+    @Test
+    public void testTokenCount() {
+        String text = "这是一个示例文本，用于测试 token 长度的计算。";
+        UserMessage userMessage = UserMessage.userMessage(text);
+        //计算 token 长度
+        //QwenTokenizer tokenizer = new QwenTokenizer(System.getenv("DASH_SCOPE_API_KEY"),"qwen-max");
+        HuggingFaceTokenizer tokenizer = new HuggingFaceTokenizer();
+        int count = tokenizer.estimateTokenCountInMessage(userMessage);
+        System.out.println("token长度：" + count);
+    }
+
+
+
+}
--- a/src/test/java/cn/ai/springaigu/TestGPT.java
+++ b/src/test/java/cn/ai/springaigu/TestGPT.java
@ -0,0 +1,196 @@
+package cn.ai.springaigu;
+
+import cn.ai.springaigu.asistant.Assistant;
+import cn.ai.springaigu.asistant.MemoryChatAssistant;
+import cn.ai.springaigu.asistant.SeparateChatAssistant;
+import dev.langchain4j.community.model.dashscope.QwenChatModel;
+//import dev.langchain4j.model.openai.OpenAiChatModel;
+import dev.langchain4j.community.model.dashscope.WanxImageModel;
+import dev.langchain4j.data.image.Image;
+import dev.langchain4j.data.message.AiMessage;
+import dev.langchain4j.data.message.UserMessage;
+import dev.langchain4j.memory.chat.MessageWindowChatMemory;
+import dev.langchain4j.model.chat.response.ChatResponse;
+import dev.langchain4j.model.output.Response;
+import dev.langchain4j.service.AiServices;
+import org.junit.jupiter.api.Test;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.boot.test.context.SpringBootTest;
+
+import java.util.Arrays;
+
+
+@SpringBootTest
+public class TestGPT {
+
+    //    /**
+//     * 整合SpringBoot
+//     */
+//    @Autowired
+//    private OpenAiChatModel openAiChatModel;
+    @Value("${langchain4j.community.dashscope.chat-model.api-key}")
+    private String apiKey;
+    /**
+     * 通义千问大模型
+     */
+    @Autowired
+    private QwenChatModel qwenChatModel;
+
+    @Autowired
+    private Assistant assistant;
+
+    /**
+     * gpt-4o-mini语言模型接入测试
+     * 如果你暂时没有密钥，也可以使用LangChain4j 提供的演示密钥，这个密钥是免费的，有使用配额限制，且仅限于 gpt-4o-mini 模型。
+     */
+//    @Test
+//    public void testGPTDemo() {
+//        //初始化模型
+//        OpenAiChatModel model = OpenAiChatModel.builder()
+//                //LangChain4j提供的代理服务器，该代理服务器会将演示密钥替换成真实密钥， 再将请求转发给OpenAI API
+//                .baseUrl("http://langchain4j.dev/demo/openai/v1") //设置模型api地址（如果apiKey = "demo"，则可省略baseUrl的配置）
+//                .apiKey("demo") //设置模型apiKey
+//                .modelName("gpt-4o-mini") //设置模型名称
+//                .build();
+//        //向模型提问
+//        String answer = model.chat("你好");
+//        //输出结果
+//        System.out.println(answer);
+//    }
+//
+//    @Test
+//    public void testSpringBoot() {
+//        //向模型提问
+//        String answer = openAiChatModel.chat("你好");
+//        //输出结果
+//        System.out.println(answer);
+//    }
+    @Test
+    public void testDashScopeQwen() {
+        //向模型提问
+        String answer = qwenChatModel.chat("你好");
+        //输出结果
+        System.out.println(answer);
+    }
+
+    @Test
+    public void testDashScopeWanx() {
+        WanxImageModel wanxImageModel = WanxImageModel.builder()
+                .modelName("wanx2.1-t2i-plus")
+                .apiKey(apiKey)
+                .build();
+        Response<Image> response = wanxImageModel.generate("奇幻森林精灵：在一片弥漫着轻柔薄雾的 古老森林深处，阳光透过茂密枝叶洒下金色光斑。一位身材娇小、长着透明薄翼的精灵少女站在一朵硕大的蘑菇上。她 有着海藻般的绿色长发，发间点缀着蓝色的小花，皮肤泛着珍珠般的微光。身上穿着由翠绿树叶和白色藤蔓编织而成的 连衣裙，手中捧着一颗散发着柔和光芒的水晶球，周围环绕着五彩斑斓的蝴蝶，脚下是铺满苔藓的地面，蘑菇和蕨类植物丛生，营造出神秘而梦幻的氛围。");
+        System.out.println(response.content().url());
+    }
+
+    @Test
+    public void testChat() {
+        //创建AIService
+        Assistant assistant = AiServices.create(Assistant.class, qwenChatModel);
+        //调用service的接口
+        String answer = assistant.chat("Hello");
+        System.out.println(answer);
+    }
+   //也可以在 Assistant 接口上添加 @AiService 注解
+    @Test
+    public void testAssistant() {
+        String answer = assistant.chat("Hello");
+        System.out.println(answer);
+    }
+
+   // 测试对话是否有记忆
+    @Test
+    public void testChatMemory() {
+        String answer1 = assistant.chat("我是环环");
+        System.out.println(answer1);
+        String answer2 = assistant.chat("我是谁");
+        System.out.println(answer2);
+    }
+
+
+    // 聊天记忆的简单实现
+    @Test
+    public void testChatMemory2() {
+        //第一轮对话
+        UserMessage userMessage1 = UserMessage.userMessage("我是环环");
+        ChatResponse chatResponse1 = qwenChatModel.chat(userMessage1);
+        AiMessage aiMessage1 = chatResponse1.aiMessage();
+        //输出大语言模型的回复
+        System.out.println(aiMessage1.text());
+        //第二轮对话
+        UserMessage userMessage2 = UserMessage.userMessage("你知道我是谁吗");
+        ChatResponse chatResponse2 = qwenChatModel.chat(Arrays.asList(userMessage1,
+                aiMessage1, userMessage2));
+        AiMessage aiMessage2 = chatResponse2.aiMessage();
+        //输出大语言模型的回复
+        System.out.println(aiMessage2.text());
+    }
+
+   // 使用ChatMemory实现聊天记忆
+    @Test
+    public void testChatMemory3() {
+        //创建chatMemory
+        MessageWindowChatMemory chatMemory = MessageWindowChatMemory.withMaxMessages(10);
+        //创建AIService
+        Assistant assistant = AiServices
+                .builder(Assistant.class)
+                .chatLanguageModel(qwenChatModel)
+                .chatMemory(chatMemory)
+                .build();
+        //调用service的接口
+        String answer1 = assistant.chat("我是环环");
+        System.out.println(answer1);
+        String answer2 = assistant.chat("我是谁");
+        System.out.println(answer2);
+    }
+
+
+    @Autowired
+    private MemoryChatAssistant memoryChatAssistant;
+    @Test
+    public void testChatMemory4() {
+        String answer1 = memoryChatAssistant.chat("我是环环");
+        System.out.println(answer1);
+        String answer2 = memoryChatAssistant.chat("我是谁");
+        System.out.println(answer2);
+    }
+
+    @Autowired
+    private SeparateChatAssistant separateChatAssistant;
+    @Test
+    public void testChatMemory5() {
+        String answer1 = separateChatAssistant.chat(1,"我是环环");
+        System.out.println(answer1);
+        String answer2 = separateChatAssistant.chat(1,"我是谁");
+        System.out.println(answer2);
+        String answer3 = separateChatAssistant.chat(2,"我是谁");
+        System.out.println(answer3);
+    }
+
+    //系统提示词@SystemMessage
+    @Test
+    public void testSystemMessage() {
+        String answer = separateChatAssistant.chat(3,"今天几号");
+        System.out.println(answer);
+    }
+
+    @Test
+    public void testUserMessage() {
+        String answer = memoryChatAssistant.chat("我是环环");
+        System.out.println(answer);
+    }
+    @Test
+    public void testV() {
+        String answer1 = separateChatAssistant.chat2(1, "我是环环");
+        System.out.println(answer1);
+        String answer2 = separateChatAssistant.chat2(1, "我是谁");
+        System.out.println(answer2);
+    }
+    @Test
+    public void testUserInfo() {
+        String answer = separateChatAssistant.chat3(1, "我是谁，我多大了", "翠花", 18);
+        System.out.println(answer);
+    }
+
+}
--- a/src/test/java/cn/ai/springaigu/ToolsTest.java
+++ b/src/test/java/cn/ai/springaigu/ToolsTest.java
@ -0,0 +1,19 @@
+package cn.ai.springaigu;
+
+import cn.ai.springaigu.asistant.SeparateChatAssistant;
+import org.junit.jupiter.api.Test;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.boot.test.context.SpringBootTest;
+
+@SpringBootTest
+public class ToolsTest {
+    @Autowired
+    private SeparateChatAssistant separateChatAssistant;
+    @Test
+    public void testCalculatorTools() {
+        String answer = separateChatAssistant.chat(1, "1+2等于几，475695037565的平方根是多 少？");
+        //答案：3，689706.4865
+        System.out.println(answer);
+
+    }
+}