LangcChain4J - Java必备技能

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> <packaging>pom</packaging> <name>langchain4j-study父工程</name> <properties> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding> <java.version>17</java.version> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <!-- Spring Boot --> <spring-boot.version>3.5.0</spring-boot.version> <!-- Spring AI --> <spring-ai.version>1.0.0</spring-ai.version> <!-- Spring AI Alibaba --> <spring-ai-alibaba.version>1.0.0-M6.1</spring-ai-alibaba.version> <!-- langchain4j --> <langchain4j.version>1.0.1</langchain4j.version> <!--langchain4j-community 引入阿里云百炼平台依赖管理清单--> <langchain4j-community.version>1.0.1-beta6</langchain4j-community.version> <!-- maven plugin --> <maven-deploy-plugin.version>3.1.1</maven-deploy-plugin.version> <flatten-maven-plugin.version>1.3.0</flatten-maven-plugin.version> <maven-compiler-plugin.version>3.8.1</maven-compiler-plugin.version> </properties> <dependencyManagement> <dependencies> <!-- Spring Boot --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-dependencies</artifactId> <version>${spring-boot.version}</version> <type>pom</type> <scope>import</scope> </dependency> <!-- Spring AI --> <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-bom</artifactId> <version>${spring-ai.version}</version> <type>pom</type> <scope>import</scope> </dependency> <!-- Spring AI Alibaba --> <dependency> <groupId>com.alibaba.cloud.ai</groupId> <artifactId>spring-ai-alibaba-starter</artifactId> <version>${spring-ai-alibaba.version}</version> </dependency> <!--langchain4j的依赖清单，加载BOM后所有langchain4j版本号可以被统一管理起来 https://docs.langchain4j.dev/get-started --> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-bom</artifactId> <version>${langchain4j.version}</version> <type>pom</type> <scope>import</scope> </dependency> <!--引入阿里云百炼平台依赖管理清单 https://docs.langchain4j.dev/integrations/language-models/dashscope --> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-community-bom</artifactId> <version>${langchain4j-community.version}</version> <type>pom</type> <scope>import</scope> </dependency> </dependencies> </dependencyManagement> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> <version>${spring-boot.version}</version> </plugin> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-deploy-plugin</artifactId> <version>${maven-deploy-plugin.version}</version> <configuration> <skip>true</skip> </configuration> </plugin> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-compiler-plugin</artifactId> <version>${maven-compiler-plugin.version}</version> <configuration> <release>${java.version}</release> <compilerArgs> <compilerArg>-parameters</compilerArg> </compilerArgs> </configuration> </plugin> <plugin> <groupId>org.codehaus.mojo</groupId> <artifactId>flatten-maven-plugin</artifactId> <version>${flatten-maven-plugin.version}</version> <inherited>true</inherited> <executions> <execution> <id>flatten</id> <phase>process-resources</phase> <goals> <goal>flatten</goal> </goals> <configuration> <updatePomFile>true</updatePomFile> <flattenMode>ossrh</flattenMode> <pomElements> <distributionManagement>remove</distributionManagement> <dependencyManagement>remove</dependencyManagement> <repositories>remove</repositories> <scm>keep</scm> <url>keep</url> <organization>resolve</organization> </pomElements> </configuration> </execution> <execution> <id>flatten.clean</id> <phase>clean</phase> <goals> <goal>clean</goal> </goals> </execution> </executions> </plugin> </plugins> </build> <repositories> <repository> <id>spring-milestones</id> <name>Spring Milestones</name> <url>https://repo.spring.io/milestone</url> <snapshots> <enabled>false</enabled> </snapshots> </repository> <repository> <id>spring-snapshots</id> <name>Spring Snapshots</name> <url>https://repo.spring.io/snapshot</url> <releases> <enabled>false</enabled> </releases> </repository> <repository> <id>aliyunmaven</id> <name>aliyun</name> <url>https://maven.aliyun.com/repository/public</url> </repository> </repositories> <pluginRepositories> <pluginRepository> <id>public</id> <name>aliyun nexus</name> <url>https://maven.aliyun.com/repository/public</url> <releases> <enabled>true</enabled> </releases> <snapshots> <enabled>false</enabled> </snapshots> </pluginRepository> </pluginRepositories> </project>

（4）建立第一个案例模块

1）新建模块

2）修改pom文件

文档出处https://docs.langchain4j.dev/get-started

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> </parent> <artifactId>langcChain4J-01study</artifactId> <properties> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <!--所有调用均基于 OpenAI 协议标准，实现一致的接口设计与规范LangChain4j 提供与许多 LLM 提供商的集成 从最简单的开始方式是从 OpenAI 集成开始https://docs.langchain4j.dev/get-started --> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> </dependency> <!--langchain4j 高阶--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j</artifactId> </dependency> <!--lombok--> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <optional>true</optional> </dependency> <!--test--> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project>

3）添加配置文件

server.port=8001 spring.application.name=langchain4j-01study

4）添加配置类

@Configuration public class LLMConfig { @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .modelName("qwen-plus") .build(); } }

5）编写业务类

@RestController public class Study01Controller { @Resource private ChatModel chatModel; @GetMapping("/chat") public String chat(@RequestParam(name = "msg", defaultValue = "你是谁") String msg) { return chatModel.chat(msg); } }

6）测试

（5）多模型共存(deepseek)

官网地址https://platform.deepseek.com/usage

1）获取Key

2）获取模型名称

3）获取baseUrl

https://api.deepseek.com/v1

（6）修改工程

1）修改配置类

@Configuration public class LLMConfig { @Bean("qwen") public ChatModel qwenChatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .modelName("qwen-plus") .build(); } @Bean("deepseek") public ChatModel deepseekChatModel() { return OpenAiChatModel.builder() .apiKey("sk-4af********") .baseUrl("https://api.deepseek.com/v1") .modelName("deepseek-chat") .build(); } }

2）修改业务类

@RestController public class Study01Controller { @Resource(name = "qwen") private ChatModel qwenChatModel; @Resource(name = "deepseek") private ChatModel deepseekChatModel; @GetMapping("/chat/v1") public String chatV1(@RequestParam(name = "msg", defaultValue = "你是谁") String msg) { return qwenChatModel.chat(msg); } @GetMapping("/chat/v2") public String chatV2(@RequestParam(name = "msg", defaultValue = "你是谁") String msg) { return deepseekChatModel.chat(msg); } }

3）测试

3、与springBoot整合

（1）了解

官网地址https://docs.langchain4j.dev/tutorials/spring-boot-integration

（2）案例实现

1）新建模块

2）修改pom文件

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> </parent> <artifactId>langChain4j-02boot</artifactId> <properties> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <!--langchain4j原生 基础--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> </dependency> <!--langchain4j原生 高阶--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j</artifactId> </dependency> <!--1 LangChain4j 整合boot底层支持--> <!-- https://docs.langchain4j.dev/tutorials/spring-boot-integration --> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId> </dependency> <!--2 LangChain4j 整合boot高阶支持--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-spring-boot-starter</artifactId> </dependency> <!--lombok--> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <optional>true</optional> </dependency> <!--test--> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project>

3）添加配置文件

server.port=8002 spring.application.name=langchain4j-02boot # https://docs.langchain4j.dev/tutorials/spring-boot-integration langchain4j.open-ai.chat-model.api-key=${aliQwen-api} langchain4j.open-ai.chat-model.model-name=qwen-plus langchain4j.open-ai.chat-model.base-url=https://dashscope.aliyuncs.com/compatible-mode/v1

4）低阶Api业务类

@RestController public class PopularIntegrationsController { @Resource private ChatModel chatModel; @GetMapping("/chatv1") public String chat(String msg) { return chatModel.chat(msg); } }

5）自定义接口类

@AiService public interface ChatAssistant { String chat(String prompt); }

6）高阶Api业务类

@RestController public class DeclarativeAIServiceController { @Resource private ChatAssistant chatAssistant; @GetMapping("/chatv2") public String chat(String msg) { return chatAssistant.chat(msg); } }

7）测试

4、低阶Api和高阶Api

（1）介绍

官网地址https://docs.langchain4j.dev/intro/#2-levels-of-abstraction

低阶api 有最多的选择，可以使用所有底层组件，比如ChatModel等。这些是基于LLM的应用中的“primitives”。可以完全控制组合它们，但需要写更多的代码。UserMessage AiMessage

EmbeddingStore Embedding

高阶api 在程序员自己定义接口，通过AiServices类里面的方法实现，优点是Api封装度较高，减少代码的复杂度，但是仍可以进行微调

（2）案例实现

1）新建模块

2）修改pom文件

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> </parent> <artifactId>langChain4j-03lowhigh</artifactId> <properties> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <!--langchain4j-open-ai 基础--> <!--所有调用均基于 OpenAI 协议标准，实现一致的接口设计与规范LangChain4j 提供与许多 LLM 提供商的集成 从最简单的开始方式是从 OpenAI 集成开始https://docs.langchain4j.dev/get-started --> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> </dependency> <!--langchain4j 高阶--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j</artifactId> </dependency> <!--lombok--> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <optional>true</optional> </dependency> <!--test--> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project>

3）添加配置文件

server.port=8003 spring.application.name=langChain4j-03lowhigh

4）添加配置类

@Configuration public class LLMConfig { @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-plus") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .build(); } }

5）低阶api调用业务类

@RestController public class LowApiController { @Resource private ChatModel chatModel; @GetMapping("/chat1") public String chat1(@RequestParam(name = "msg", defaultValue = "你是谁") String msg) { return chatModel.chat(msg); } }

6）测试低阶api调用

7）修改业务类获取Token调用

@RestController public class LowApiController { @Resource private ChatModel chatModel; @GetMapping("/chat1") public String chat1(@RequestParam(name = "msg", defaultValue = "你是谁") String msg) { return chatModel.chat(msg); } @GetMapping("/chat2") public String chat2(@RequestParam(name = "msg", defaultValue = "你是谁") String msg) { ChatResponse chatResponse = chatModel.chat(UserMessage.from(msg)); String text = chatResponse.aiMessage().text(); TokenUsage tokenUsage = chatResponse.tokenUsage(); System.out.println("本次token消耗：" + tokenUsage); return text; } }

8）高阶api调用

官网地址https://docs.langchain4j.dev/tutorials/ai-services#simplest-ai-service

1、定义AiServices接口

public interface ChatAssistant { String chat(String prompt); }

2、修改配置类

AiService接口实现

package com.hk.study.config; import com.hk.study.service.ChatAssistant; import dev.langchain4j.model.chat.ChatModel; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.service.AiServices; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; @Configuration public class LLMConfig { @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-plus") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .build(); } @Bean public ChatAssistant chatAssistant(ChatModel chatModel) { return AiServices.create(ChatAssistant.class, chatModel); } }

3、业务类

@RestController public class HighApiController { @Resource private ChatAssistant chatAssistant; @GetMapping("chatv2") public String chatv2 (String msg) { return chatAssistant.chat(msg); } }

9）测试高阶api

5、模型的参数配置

在03模块的基础上配置

官网地址https://docs.langchain4j.dev/tutorials/model-parameters

（1）日志配置

官网地址https://docs.langchain4j.dev/tutorials/logging

1）修改配置类

 @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-plus") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") // 开启日志, 日志级别设置为DEBUG才会打印日志 .logRequests(true) .logResponses(true) .build(); }

2）修改配置文件

server.port=8003 spring.application.name=langChain4j-03lowhigh # 开启langchain4j的日志, 日志级别设置为DEBUG才会打印日志 logging.level.dev.langchain4j=DEBUG

3）测试

（2）监控配置

官网地址https://docs.langchain4j.dev/tutorials/observability

1）编写监听类

public class TestChatModelListener implements ChatModelListener { @Override public void onRequest(ChatModelRequestContext requestContext) { // onRequest配置的k:v键值对，在onResponse阶段可以获得，上下文传递参数好用 String uuidValue = IdUtil.simpleUUID(); requestContext.attributes().put("TraceID",uuidValue); System.out.println("配置请求参数：TraceID == "+ uuidValue); } @Override public void onResponse(ChatModelResponseContext responseContext) { Object object = responseContext.attributes().get("TraceID"); System.out.println("获取请求参数：TraceID == "+ object); } @Override public void onError(ChatModelErrorContext errorContext) { System.out.println("出错了..."); } }

2）修改配置类

 @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-plus") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .listeners(List.of(new TestChatModelListener())) .build(); }

3）测试

（3）重试机制

1）修改配置类

 @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-plus") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .logRequests(true) .logResponses(true) .maxRetries(2) .build(); }

2）测试

断开网络，再次访问

（4）超时机制

1）修改配置类

 @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-plus") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .logRequests(true) .maxRetries(2) .timeout(Duration.ofSeconds(1)) .build(); }

2）测试

6、多模态视觉

（1）出处

官网地址https://docs.langchain4j.dev/tutorials/chat-and-language-models#multimodalityUserMessage不仅可以包含文本，还可以包含其他类型的内容。包含一个。是一个接口，具有以下实现方式：UserMessageList<Content> contentsContent

TextContent
ImageContent
AudioContent
VideoContent
PdfFileContent

本次选择的模型：qwen-vl-max

阿里地址https://help.aliyun.com/zh/model-studio/models#850732b1aabs0

（2）图形理解案例实现

1）创建模块

2）修改pom

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> </parent> <artifactId>langChain4j-04chatimage</artifactId> <properties> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <!--langchain4j-open-ai 基础--> <!--所有调用均基于 OpenAI 协议标准，实现一致的接口设计与规范LangChain4j 提供与许多 LLM 提供商的集成 从最简单的开始方式是从 OpenAI 集成开始https://docs.langchain4j.dev/get-started --> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> </dependency> <!--langchain4j 高阶--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j</artifactId> </dependency> <!--lombok--> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <optional>true</optional> </dependency> <!--hutool--> <dependency> <groupId>cn.hutool</groupId> <artifactId>hutool-all</artifactId> <version>5.8.22</version> </dependency> <!--test--> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project>

3）添加配置文件

server.port=8004 spring.application.name=langchain4j-04chatimage

4）添加配置类

@Configuration public class LLMConfig { @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-vl-max") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .build(); } }

5）编写业务类

@RestController public class ImageModelController { @Resource private ChatModel chatModel; @Value("classpath:mi.jpg") private org.springframework.core.io.Resource imageResource; @GetMapping("/image") public String image() throws IOException { // 1、将图片通过Base64编码转为字符串 byte[] imageBytes = imageResource.getContentAsByteArray(); String encodeToString = Base64.getEncoder().encodeToString(imageBytes); // 2、构建提示词userMessage UserMessage userMessage = UserMessage.from( TextContent.from("从以下图片中获取来源网站名称，股价走势和5月30号股价"), ImageContent.from(encodeToString, "image/jpg") ); // 3、调用模型获取回答 ChatResponse chatResponse = chatModel.chat(userMessage); String response = chatResponse.aiMessage().text(); return response; } }

6）测试

（3）引入第三方案例实现

结合阿里巴巴的通义万象进行图像理解，所有出处均来自官网，模型使用阿里的wanx2.1-t2i-turbo

官网文档地址https://docs.langchain4j.dev/integrations/language-models/dashscope

0）修改父工程pom文件

<properties>

        ......

        
        <langchain4j-community.version>1.0.1-beta6</langchain4j-community.version>

........

</properties>

<dependencyManagement>

        ......

        
            <dependency>
                <groupId>dev.langchain4j</groupId>
                <artifactId>langchain4j-community-bom</artifactId>
                <version>${langchain4j-community.version}</version>
                <type>pom</type>
                <scope>import</scope>
            </dependency>

......

</dependencyManagement>

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> <packaging>pom</packaging> <name>langchain4j-study父工程</name> <modules> <module>langChain4J-01study</module> <module>langChain4J-02boot</module> <module>langChain4j-03lowhigh</module> <module>langChain4j-04chatimage</module> </modules> <properties> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding> <java.version>17</java.version> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <!-- Spring Boot --> <spring-boot.version>3.5.0</spring-boot.version> <!-- Spring AI --> <spring-ai.version>1.0.0</spring-ai.version> <!-- Spring AI Alibaba --> <spring-ai-alibaba.version>1.0.0-M6.1</spring-ai-alibaba.version> <!-- langchain4j --> <langchain4j.version>1.0.1</langchain4j.version> <!--langchain4j-community 引入阿里云百炼平台依赖管理清单--> <langchain4j-community.version>1.0.1-beta6</langchain4j-community.version> <!-- maven plugin --> <maven-deploy-plugin.version>3.1.1</maven-deploy-plugin.version> <flatten-maven-plugin.version>1.3.0</flatten-maven-plugin.version> <maven-compiler-plugin.version>3.8.1</maven-compiler-plugin.version> </properties> <dependencyManagement> <dependencies> <!-- Spring Boot --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-dependencies</artifactId> <version>${spring-boot.version}</version> <type>pom</type> <scope>import</scope> </dependency> <!-- Spring AI --> <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-bom</artifactId> <version>${spring-ai.version}</version> <type>pom</type> <scope>import</scope> </dependency> <!-- Spring AI Alibaba --> <dependency> <groupId>com.alibaba.cloud.ai</groupId> <artifactId>spring-ai-alibaba-starter</artifactId> <version>${spring-ai-alibaba.version}</version> </dependency> <!--langchain4j的依赖清单，加载BOM后所有langchain4j版本号可以被统一管理起来 https://docs.langchain4j.dev/get-started --> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-bom</artifactId> <version>${langchain4j.version}</version> <type>pom</type> <scope>import</scope> </dependency> <!--引入阿里云百炼平台依赖管理清单 https://docs.langchain4j.dev/integrations/language-models/dashscope --> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-community-bom</artifactId> <version>${langchain4j-community.version}</version> <type>pom</type> <scope>import</scope> </dependency> </dependencies> </dependencyManagement> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> <version>${spring-boot.version}</version> </plugin> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-deploy-plugin</artifactId> <version>${maven-deploy-plugin.version}</version> <configuration> <skip>true</skip> </configuration> </plugin> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-compiler-plugin</artifactId> <version>${maven-compiler-plugin.version}</version> <configuration> <release>${java.version}</release> <compilerArgs> <compilerArg>-parameters</compilerArg> </compilerArgs> </configuration> </plugin> <plugin> <groupId>org.codehaus.mojo</groupId> <artifactId>flatten-maven-plugin</artifactId> <version>${flatten-maven-plugin.version}</version> <inherited>true</inherited> <executions> <execution> <id>flatten</id> <phase>process-resources</phase> <goals> <goal>flatten</goal> </goals> <configuration> <updatePomFile>true</updatePomFile> <flattenMode>ossrh</flattenMode> <pomElements> <distributionManagement>remove</distributionManagement> <dependencyManagement>remove</dependencyManagement> <repositories>remove</repositories> <scm>keep</scm> <url>keep</url> <organization>resolve</organization> </pomElements> </configuration> </execution> <execution> <id>flatten.clean</id> <phase>clean</phase> <goals> <goal>clean</goal> </goals> </execution> </executions> </plugin> </plugins> </build> <repositories> <repository> <id>spring-milestones</id> <name>Spring Milestones</name> <url>https://repo.spring.io/milestone</url> <snapshots> <enabled>false</enabled> </snapshots> </repository> <repository> <id>spring-snapshots</id> <name>Spring Snapshots</name> <url>https://repo.spring.io/snapshot</url> <releases> <enabled>false</enabled> </releases> </repository> <repository> <id>aliyunmaven</id> <name>aliyun</name> <url>https://maven.aliyun.com/repository/public</url> </repository> </repositories> <pluginRepositories> <pluginRepository> <id>public</id> <name>aliyun nexus</name> <url>https://maven.aliyun.com/repository/public</url> <releases> <enabled>true</enabled> </releases> <snapshots> <enabled>false</enabled> </snapshots> </pluginRepository> </pluginRepositories> </project>

1）创建模块

2）修改pom

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> </parent> <artifactId>langChain4j-05chatimage</artifactId> <properties> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <!--langchain4j-open-ai 基础--> <!--所有调用均基于 OpenAI 协议标准，实现一致的接口设计与规范LangChain4j 提供与许多 LLM 提供商的集成 从最简单的开始方式是从 OpenAI 集成开始https://docs.langchain4j.dev/get-started --> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> </dependency> <!--langchain4j 高阶--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j</artifactId> </dependency> <!--DashScope (Qwen)接入阿里云百炼平台 https://docs.langchain4j.dev/integrations/language-models/dashscope --> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId> </dependency> <!--lombok--> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <optional>true</optional> </dependency> <!--hutool--> <dependency> <groupId>cn.hutool</groupId> <artifactId>hutool-all</artifactId> <version>5.8.22</version> </dependency> <!--test--> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project>

3）添加配置文件

server.port=8005 spring.application.name=langchain4j-05chatimage

4）添加配置类

@Configuration public class LLMConfig { @Bean public WanxImageModel wanxImageModel() { return WanxImageModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("wanx2.1-t2i-turbo") .build(); } }

5）编写业务类

@RestController public class ImageModelController { @Resource private WanxImageModel wanxImageModel; @GetMapping("/chat/image-1") public String image1() { Response<Image> generate = wanxImageModel.generate("一张图，描述：一个人在沙滩上"); return generate.content().url().toString(); } @GetMapping("/chat/image-2") public String image2() throws NoApiKeyException { String prompt = "近景镜头，18岁的中国女孩，古代服饰，圆脸，正面看着镜头，" + "民族优雅的服装，商业摄影，室外，电影级光照，半身特写，精致的淡妆，锐利的边缘。"; ImageSynthesisParam build = ImageSynthesisParam.builder() .apiKey(System.getenv("aliQwen-api")) .model("wanx2.1-t2i-turbo") .prompt(prompt) .n(2) .size("1024*1024") .build(); ImageSynthesis imageSynthesis = new ImageSynthesis(); ImageSynthesisResult result = imageSynthesis.call(build); return JsonUtils.toJson(result); } }

6）测试

7、流式输出

（1）介绍

流式输出(StreamingOutput)是一种逐步返回大模型生成结果的技术，允许服务器将响应内容分批次实时传输给客户端，而不是等待全部内容生成完毕后再一次性返回。这种机制能显著提升用户体验，尤其适用于大模型响应较慢的场景（如生成长文本或复杂推理结果）。

低阶出处https://docs.langchain4j.dev/tutorials/response-streaming 高阶出处https://docs.langchain4j.dev/tutorials/ai-services#flux

（2）案例实现

1）创建模块

2）修改pom

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> </parent> <artifactId>langChain4j-06chatstream</artifactId> <properties> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <!--langchain4j-open-ai + langchain4j + langchain4j-reactor--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> </dependency> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j</artifactId> </dependency> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-reactor</artifactId> </dependency> <!--lombok--> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <optional>true</optional> </dependency> <!--hutool--> <dependency> <groupId>cn.hutool</groupId> <artifactId>hutool-all</artifactId> <version>5.8.22</version> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project>

3）添加配置文件

server.port=8006 spring.application.name=langchain4j-06chat-stream # 设置响应的字符编码，避免流式返回输出乱码 server.servlet.encoding.charset=utf-8 server.servlet.encoding.enabled=true server.servlet.encoding.force=true

4）自定义接口

public interface ChatAssistant { Flux<String> chat(String prompt); }

5）编写配置类

@Configuration public class LLMConfig { @Bean public StreamingChatModel streamingChatModel() { return OpenAiStreamingChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-plus") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .build(); } @Bean public ChatAssistant chatAssistant(StreamingChatModel streamingChatModel){ return AiServices.create(ChatAssistant.class, streamingChatModel); } }

6）编写业务类

@RestController public class StreamingChatModelController { @Resource private StreamingChatModel streamingChatModel; @Resource private ChatAssistant chatAssistant; // 方式一：使用 Flux.create 手动创建 Flux @GetMapping("/chat1") public Flux<String> chat1(String msg) { return Flux.create(fluxSink -> { streamingChatModel.chat(msg, new StreamingChatResponseHandler() { @Override public void onPartialResponse(String s) { System.out.println(s); fluxSink.next(s); } @Override public void onCompleteResponse(ChatResponse chatResponse) { System.out.println("---response over: "+chatResponse); fluxSink.complete(); } @Override public void onError(Throwable throwable) { fluxSink.error(throwable); } }); }); } // 方式二 @GetMapping("/chat2") public Flux<String> chat2(String msg) { return chatAssistant.chat(msg); } }

7）测试

8、记忆存储

（1）介绍

记忆缓存是聊天系统中的一个重要组件，用于存储和管理对话的上下文信息。它的主要作用是让AI助手能够"记住"之前的对话内容，从而提供连贯和个性化的回复

官网地址https://docs.langchain4j.dev/tutorials/chat-memoryLangChain4j 提供两种开箱即用实现：

滑动窗口：只保留最新的N条消息 MessageWindowChatMemory
限制Token数量：它同样作为滑动窗口，保留最新的令牌TokenWindowChatMemory，需要结合TokenCountEstimator来计算Token的数量

（2）案例实现

1）新建模块

2）修改pom文件

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> </parent> <artifactId>langChain4j-07chatmemory</artifactId> <properties> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <!--langchain4j-open-ai + langchain4j + langchain4j-reactor--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> </dependency> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j</artifactId> </dependency> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-reactor</artifactId> </dependency> <!--lombok--> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <optional>true</optional> </dependency> <!--hutool--> <dependency> <groupId>cn.hutool</groupId> <artifactId>hutool-all</artifactId> <version>5.8.22</version> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project>

3）添加配置文件

server.port=8007 spring.application.name=langchain4j-07chat-memory # 设置响应的字符编码 server.servlet.encoding.charset=utf-8 server.servlet.encoding.enabled=true server.servlet.encoding.force=true

4）自定义接口

public interface ChatMemoryAssistant { /** * 聊天带记忆缓存功能 * * @param userId 用户 ID * @param prompt 消息 * @return String */ String chatWithChatMemory(@MemoryId Long userId, @UserMessage String prompt); }

5）编写配置类

@Configuration public class LLMConfig { @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-long") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .build(); } @Bean("chatMessageWindowChatMemory") public ChatMemoryAssistant chatMessageWindowChatMemory(ChatModel chatModel) { return AiServices.builder(ChatMemoryAssistant.class) .chatModel(chatModel) // 每个memoryId对应创建一个ChatMemory .chatMemoryProvider(memoryId -> MessageWindowChatMemory.withMaxMessages(20)) .build(); } @Bean public ChatMemoryAssistant chatTokenWindowChatMemory(ChatModel chatModel) { // TokenCountEstimator默认的token分词器，需要结合Tokenizer计算ChatMessage的token数量 OpenAiTokenCountEstimator openAiTokenCountEstimator = new OpenAiTokenCountEstimator("gpt-4"); return AiServices.builder(ChatMemoryAssistant.class) .chatModel(chatModel) // 每个memoryId对应创建一个ChatMemory .chatMemoryProvider(memoryId -> TokenWindowChatMemory.withMaxTokens(100, openAiTokenCountEstimator)) .build(); } }

6）编写业务类

@RestController public class ChatMemoryController { @Resource(name = "chatMessageWindowChatMemory") private ChatMemoryAssistant chatMessageWindowChatMemory; @Resource(name = "chatTokenWindowChatMemory") private ChatMemoryAssistant chatTokenWindowChatMemory; // MessageWindowChatMemory实现聊天功能 @GetMapping("/chatv1") public void chatv1() { chatMessageWindowChatMemory.chatWithChatMemory(1L, "你好！我的名字是Java."); String answer01 = chatMessageWindowChatMemory.chatWithChatMemory(1L, "我的名字是什么"); System.out.println("answer01返回结果："+answer01); chatMessageWindowChatMemory.chatWithChatMemory(3L, "你好！我的名字是C++"); String answer02 = chatMessageWindowChatMemory.chatWithChatMemory(3L, "我的名字是什么"); System.out.println("answer02返回结果："+answer02); } // TokenWindowChatMemory实现聊天功能 @GetMapping("/chatv2") public void chatv2() { chatTokenWindowChatMemory.chatWithChatMemory(1L, "你好！我的名字是Java."); String answer01 = chatTokenWindowChatMemory.chatWithChatMemory(1L, "我的名字是什么"); System.out.println("answer01返回结果："+answer01); chatTokenWindowChatMemory.chatWithChatMemory(3L, "你好！我的名字是C++"); String answer02 = chatTokenWindowChatMemory.chatWithChatMemory(3L, "我的名字是什么"); System.out.println("answer02返回结果："+answer02); } }

7）测试

GET http://localhost:8007/chatv1

9、提示词工程

（1）出处

官网地址https://docs.langchain4j.dev/tutorials/chat-and-language-models

五种类型的聊天的消息：

SystemMessage：系统消息，通常，作为开发人员，你应该定义这条消息的内容。通常，你会在这里写关于LLM在这次对话中的角色、应该如何表现、以什么风格回答等指令。
UserMessage：用户消息；这是用户发来的信息。用户可以是你应用的最终用户（人类）或你的应用本身
AiMessage：由AI生成的消息；
ToolExecutionResultMessage：模型结合工具
CustomMessage：这是一个可以包含任意属性的自定义消息。这种消息类型只能被支持它的实现使用（目前仅支持 Ollama）。

（2）案例实现

1）基础工程搭建

创建模块

修改pom文件

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> </parent> <artifactId>langChain4j-08chatprompt</artifactId> <properties> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <!--langchain4j-open-ai + langchain4j + langchain4j-reactor--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> </dependency> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j</artifactId> </dependency> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-reactor</artifactId> </dependency> <!--lombok--> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <optional>true</optional> </dependency> <!--hutool--> <dependency> <groupId>cn.hutool</groupId> <artifactId>hutool-all</artifactId> <version>5.8.22</version> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project>

添加配置文件

server.port=8008 spring.application.name=langchain4j-08chat-prompt # 设置响应的字符编码 server.servlet.encoding.charset=utf-8 server.servlet.encoding.enabled=true server.servlet.encoding.force=true

2）方式一

@SystemMessage + @UserMessage + @V

编写自定义接口

public interface LawAssistant { @SystemMessage("你是一位专业的中国法律顾问，只回答与中国法律相关的问题。" + "输出限制：对于其他领域的问题禁止回答，直接返回'抱歉，我只能回答中国法律相关的问题。'") @UserMessage("请回答以下法律问题：{{question}},字数控制在{{length}}以内") String chat(@V("question") String question, @V("length") int length); }

添加配置类

@Configuration public class LLMConfig { @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-long") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .build(); } @Bean public LawAssistant lawAssistant(ChatModel chatModel) { return AiServices.create(LawAssistant.class, chatModel); } }

编写业务类

@RestController public class ChatPromptController { @Resource private LawAssistant lawAssistant; @GetMapping(value = "/chatprompt/test1") public void test1() { String chat = lawAssistant.chat("什么是知识产权？",2000); System.out.println(chat); System.out.println("========================================================"); String chat2 = lawAssistant.chat("什么是java？",2000); System.out.println(chat2); System.out.println("========================================================"); String chat3 = lawAssistant.chat("介绍下西瓜和芒果",2000); System.out.println(chat3); System.out.println("========================================================"); String chat4 = lawAssistant.chat("飞机发动机原理",2000); System.out.println(chat4); } }

3）方式二

使用@structuredPrompt业务实体类

新建业务实体类

@Data @StructuredPrompt("根据中国{{legal}}法律，解答以下问题：{{question}}") public class LawPrompt { private String legal; private String question; }

修改自定义接口

public interface LawAssistant { @SystemMessage("你是一位专业的中国法律顾问，只回答与中国法律相关的问题。" + "输出限制：对于其他领域的问题禁止回答，直接返回'抱歉，我只能回答中国法律相关的问题。'") @UserMessage("请回答以下法律问题：{{question}},字数控制在{{length}}以内") String chat(@V("question") String question, @V("length") int length); @SystemMessage("你是一位专业的中国法律顾问，只回答与中国法律相关的问题。" + "输出限制：对于其他领域的问题禁止回答，直接返回'抱歉，我只能回答中国法律相关的问题。'") String chat(LawPrompt lawPrompt); }

修改业务类测试

@RestController public class ChatPromptController { @Resource private LawAssistant lawAssistant; ...... @GetMapping(value = "/chatprompt/test2") public String test2() { LawPrompt prompt = new LawPrompt(); prompt.setLegal("知识产权"); prompt.setQuestion("TRIPS协议?"); String chat = lawAssistant.chat(prompt); System.out.println(chat); return chat; } }

4）方式三

在LangChain4j中有两个对象PromptTemplate以及Prompt用来实现提示词相关功能

单个参数可以使用{it}"占位符或者"{{参数名}"，如果为其他字符，系统不能自动识别会报错。

修改业务类测试

 @Resource private ChatModel chatModel; @GetMapping(value = "/chatprompt/test3") public String test3(){ // 默认 PromptTemplate 构造使用 it 属性作为默认占位符 String role = "财务会计"; String question = "人民币大写"; //1 构造PromptTemplate模板 PromptTemplate template = PromptTemplate.from("你是一个{{it}}助手,{{question}}怎么办"); //2 由PromptTemplate生成Prompt Prompt prompt = template.apply(Map.of("it",role,"question",question)); //3 Prompt提示词变成UserMessage UserMessage userMessage = prompt.toUserMessage(); //4 调用大模型 ChatResponse chatResponse = chatModel.chat(userMessage); //4.1 后台打印 System.out.println(chatResponse.aiMessage().text()); //4.2 前台返回 return chatResponse.aiMessage().text(); }

10、持久化

（1）介绍

将用户和大模型的对话进行持久化存储

官网出处https://docs.langchain4j.dev/tutorials/chat-memory#persistence

（2）案例实现

1）创建工程

2）修改pom文件

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> </parent> <artifactId>langChain4j-09chatPersistence</artifactId> <properties> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <!--langchain4j-open-ai + langchain4j + langchain4j-reactor--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> </dependency> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j</artifactId> </dependency> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-reactor</artifactId> </dependency> <!--spring-boot-starter-data-redis https://docs.langchain4j.dev/tutorials/chat-memory#persistence --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-data-redis</artifactId> </dependency> <!--lombok--> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <optional>true</optional> </dependency> <!--hutool--> <dependency> <groupId>cn.hutool</groupId> <artifactId>hutool-all</artifactId> <version>5.8.22</version> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project>

3）添加配置文件

server.port=8009 spring.application.name=langchain4j-09chat-persistence # 设置响应的字符编码 server.servlet.encoding.charset=utf-8 server.servlet.encoding.enabled=true server.servlet.encoding.force=true # ==========config redis=============== spring.data.redis.host=localhost spring.data.redis.port=6379 spring.data.redis.password=12345 spring.data.redis.database=0 spring.data.redis.connect-timeout=3s spring.data.redis.timeout=2s

4）自定义接口

public interface ChatPersistenceAssistant { String chat(@MemoryId Long userId, @UserMessage String message); }

5）redis配置类

@Configuration public class RedisConfig { @Bean public RedisTemplate<String, Object> redisTemplate(RedisConnectionFactory redisConnectionFactory) { RedisTemplate<String, Object> template = new RedisTemplate<>(); template.setConnectionFactory(redisConnectionFactory); //设置key序列化方式string template.setKeySerializer(new StringRedisSerializer()); //设置value序列化方式json template.setDefaultSerializer(new GenericJackson2JsonRedisSerializer()); template.setHashKeySerializer(new StringRedisSerializer()); template.setHashValueSerializer(new GenericJackson2JsonRedisSerializer()); template.setEnableTransactionSupport(true); template.afterPropertiesSet(); return template; } }

6）实现ChatMemoryStore

@Component public class RedisChatMemoryStore implements ChatMemoryStore { private static final String CHAT_MEMORY_KEY_PREFIX = "chat_memory:"; @Resource private RedisTemplate<String, String> redisTemplate; @Override public List<ChatMessage> getMessages(Object memoryId) { String messages = redisTemplate.opsForValue().get(CHAT_MEMORY_KEY_PREFIX + memoryId); return ChatMessageDeserializer.messagesFromJson(messages); } @Override public void updateMessages(Object memoryId, List<ChatMessage> list) { redisTemplate.opsForValue().set(CHAT_MEMORY_KEY_PREFIX + memoryId, ChatMessageSerializer.messagesToJson(list)); } @Override public void deleteMessages(Object memoryId) { redisTemplate.delete(CHAT_MEMORY_KEY_PREFIX + memoryId); } }

7）模型配置类

@Configuration public class LLMConfig { @Resource private RedisChatMemoryStore redisChatMemoryStore; @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-plus") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .build(); } @Bean public ChatPersistenceAssistant chatPersistenceAssistant(ChatModel chatModel) { ChatMemoryProvider chatMemoryProvider = memoryId -> MessageWindowChatMemory.builder() .id(memoryId) .maxMessages(10) .chatMemoryStore(redisChatMemoryStore) .build(); return AiServices.builder(ChatPersistenceAssistant.class) .chatModel(chatModel) .chatMemoryProvider(chatMemoryProvider) .build(); } }

8）业务类

@RestController public class ChatPersistenceController { @Resource private ChatPersistenceAssistant chatPersistenceAssistant; @GetMapping("/chat") public void chat() { chatPersistenceAssistant.chat(1L, "你好！我的名字是redis"); chatPersistenceAssistant.chat(2L, "你好！我的名字是nacos"); String chat = chatPersistenceAssistant.chat(1L, "我的名字是什么"); System.out.println(chat); System.out.println("====================================================="); chat = chatPersistenceAssistant.chat(2L, "我的名字是什么"); System.out.println(chat); } }

9）测试

11、Tools工具

（1）说明

大模型对话可回答训练时间之前的问题，对当前时间的问题无法获取，需要给大模型配一个调用其它外部Uti工具类；大语言模型(LLMs)不仅仅是文本生成的能手，它们还能触发并调用第3方函数，比如查询微信/调用支付宝/查看顺丰快递单据号等等..，LLM本身并不执行函数,它只是指示应该调用哪个函数以及如何调用

官网文档https://docs.langchain4j.dev/tutorials/tools

（2）案例实现

1）基础搭建

创建工程

修改pom文件‘

<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>com.hk</groupId> <artifactId>langcChain4J-study</artifactId> <version>1.0-SNAPSHOT</version> </parent> <artifactId>langChain4j-10chatFunctionCalling</artifactId> <properties> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <!--langchain4j-open-ai--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> </dependency> <!--langchain4j--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j</artifactId> </dependency> <!--langchain4j-reactor--> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-reactor</artifactId> </dependency> <!--httpclient5--> <dependency> <groupId>org.apache.httpcomponents.client5</groupId> <artifactId>httpclient5</artifactId> <version>5.5</version> </dependency> <!--lombok--> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <optional>true</optional> </dependency> <!--hutool--> <dependency> <groupId>cn.hutool</groupId> <artifactId>hutool-all</artifactId> <version>5.8.22</version> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project>

添加配置文件

server.port=8010 spring.application.name=langchain4j-10chat-functioncalling

2）低阶Api实现

自定义功能接口

public interface FunctionAssistant { String chat(String message); }

核心配置类

@Configuration public class LLMConfig { @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-plus") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .build(); } // 低阶工具Api @Bean public FunctionAssistant functionAssistant(ChatModel chatModel) { ToolSpecification toolSpecification = ToolSpecification.builder() .name("开局发票助手") .description("根据用户提交的开票信息，开具发票") .parameters(JsonObjectSchema.builder() .addStringProperty("companyName", "公司名称") .addStringProperty("dutyNumber", "税号序列") .addStringProperty("amount", "开票金额，保留两位有效数字") .build()) .build(); ToolExecutor toolExecutor = (toolExecutionRequest, memoryId) -> { System.out.println("toolExecutionRequest=====" + toolExecutionRequest.id()); System.out.println("toolExecutionRequest=====" + toolExecutionRequest.name()); String arguments = toolExecutionRequest.arguments(); System.out.println("toolExecutionRequest=====" + arguments); return "发票已开具"; }; return AiServices.builder(FunctionAssistant.class) .chatModel(chatModel) .tools(Map.of(toolSpecification, toolExecutor)) .build(); } }

业务类

@RestController public class ChatFunctionCallingController { @Resource private FunctionAssistant functionAssistant; @GetMapping("/chat") public void chat() { String msg = "开张发票,公司：尚硅谷教育科技有限公司 税号：zfdehtrhrt533 金额：668.12"; String chat = functionAssistant.chat(msg); System.out.println("ok:" + chat); } }

测试

3）获取【和风天气】ApiKey

【和风天气】官网https://dev.qweather.com/

注册账号

创建项目

创建凭据

api地址和参数说明

地址https://dev.qweather.com/docs/configuration/api-config/

返回结果查看

4）高阶Api实现

新建天气业务类

@Service public class WeatherService { private static final String WEATHER_API_URL = "https://nXXXX.qweatherapi.com/v7/weather/now?location=%s&key=%s"; public JsonNode getWeather(String location) throws JsonProcessingException { String url = String.format(WEATHER_API_URL, location, "XXXXXX"); // 发送HTTP请求并解析响应 CloseableHttpClient httpClient = HttpClients.createDefault(); // 这里省略具体实现，假设使用RestTemplate发送GET请求并返回JsonNode HttpComponentsClientHttpRequestFactory requestFactory = new HttpComponentsClientHttpRequestFactory(httpClient); // 配置RestTemplate使用HttpComponentsClientHttpRequestFactory RestTemplate restTemplate = new RestTemplate(requestFactory); // 发送GET请求并获取响应 String response = restTemplate.getForObject(url, String.class); // 解析响应体为JsonNode JsonNode jsonNode = new ObjectMapper().readTree(response); return jsonNode; } }

抽象层次调用

public class InvoiceHandler { @Tool("获取天气信息") public String handle(@P("天气状况") String text, @P("风向") String windDir ) throws Exception { System.out.println("天气状况：" + text + " 风向： " + windDir); //---------------------------------- // 自己的业务逻辑，调用redis/rabbitmq/kafka/mybatis/顺丰单据/医疗化验报告/支付接口等第3方 //---------------------------------- System.out.println(new WeatherService().getWeather("101010100")); return "获取成功"; } }

修改配置类

@Configuration public class LLMConfig { @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(System.getenv("aliQwen-api")) .modelName("qwen-plus") .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1") .build(); } // 低阶工具Api @Bean("lowfunction") public FunctionAssistant functionAssistant(ChatModel chatModel) { ToolSpecification toolSpecification = ToolSpecification.builder() .name("开局发票助手") .description("根据用户提交的开票信息，开具发票") .parameters(JsonObjectSchema.builder() .addStringProperty("companyName", "公司名称") .addStringProperty("dutyNumber", "税号序列") .addStringProperty("amount", "开票金额，保留两位有效数字") .build()) .build(); ToolExecutor toolExecutor = (toolExecutionRequest, memoryId) -> { System.out.println("toolExecutionRequest=====" + toolExecutionRequest.id()); System.out.println("toolExecutionRequest=====" + toolExecutionRequest.name()); String arguments = toolExecutionRequest.arguments(); System.out.println("toolExecutionRequest=====" + arguments); return "发票已开具"; }; return AiServices.builder(FunctionAssistant.class) .chatModel(chatModel) .tools(Map.of(toolSpecification, toolExecutor)) .build(); } // 高阶工具Api @Bean("highfunction") public FunctionAssistant highFunctionAssistant(ChatModel chatModel) { return AiServices.builder(FunctionAssistant.class) .chatModel(chatModel) .tools(new InvoiceHandler()) .build(); } }

修改业务类

@RestController public class ChatFunctionCallingController { // @Resource(name = "lowfunction") // private FunctionAssistant functionAssistant; @Resource(name = "highfunction") private FunctionAssistant highFunctionAssistant; // @GetMapping("/chat") // public void chat() { // String msg = "开张发票,公司：尚硅谷教育科技有限公司 税号：zfdehtrhrt533 金额：668.12"; // String chat = functionAssistant.chat(msg); // System.out.println("ok:" + chat); // } @GetMapping("/chat2") public void chat2() { String chat = highFunctionAssistant.chat("北京今天的天气"); System.out.println("ok:" + chat); } }

测试

12、向量数据库

持续更新中......

13、RAG检索增强

持续更新中......

14、MCP模型上下文切换

持续更新中......