理解对话上下文

1、pom依赖

<properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <maven.compiler.source>11</maven.compiler.source>
    <maven.compiler.target>11</maven.compiler.target>
    <langchain4j.version>0.35.0</langchain4j.version>
</properties>

<dependencies>
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j</artifactId>
        <version>${langchain4j.version}</version>
    </dependency>
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j-open-ai</artifactId>
        <version>${langchain4j.version}</version>
    </dependency>
    <dependency>
        <groupId>org.tinylog</groupId>
        <artifactId>tinylog-impl</artifactId>
        <version>2.6.2</version>
    </dependency>
    <dependency>
        <groupId>org.tinylog</groupId>
        <artifactId>slf4j-tinylog</artifactId>
        <version>2.6.2</version>
    </dependency>

    <dependency>
      <groupId>dev.langchain4j</groupId>
      <artifactId>langchain4j-dashscope</artifactId>
      <version>${langchain4j.version}</version>
      <exclusions>
        <exclusion>
            <artifactId>slf4j-simple</artifactId>
            <groupId>org.slf4j</groupId>
        </exclusion>
      </exclusions>
    </dependency>
</dependencies>

2、代码

import dev.langchain4j.data.message.AiMessage;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.dashscope.QwenChatModel;
import dev.langchain4j.model.output.Response;

public class Qwen {

    public static void main(String [] args) {
        ChatLanguageModel model = QwenChatModel.builder()
                .apiKey("sk-3bfe0dfa79b94aa9b2d2da0a9286b9b1")
                .modelName("qwen-plus")
                .build();
        UserMessage userMessage1 = UserMessage.userMessage("你好，你是谁？");
        Response<AiMessage> response1 = model.generate(userMessage1);
        AiMessage aiMessage1 = response1.content();
        System.out.println(aiMessage1.text());
        System.out.println("===============================================");
        UserMessage userMessage2 = UserMessage.userMessage("请再重复⼀次");
        Response<AiMessage> response2 = model.generate(userMessage1, aiMessage1, userMessage2);
        System.out.println(response2.content().text());
    }
}

3、测试结果

你好！我是Qwen，是阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我可以帮助你回答问题、创作文字，比如写故事、写公文、写邮件、写剧本、逻辑推理、编程等等，还能表达观点，玩游戏等。如果你有任何问题或需要帮助，欢迎随时告诉我！
===============================================
你好！我是Qwen，是阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我可以帮助你回答问题、创作文字，比如写故事、写公文、写邮件、写剧本、逻辑推理、编程等等，还能表达观点，玩游戏等。如果你有任何问题或需要帮助，欢迎随时告诉我！