Java AI 高级全能工程：用少量代码调用大模型生成文章

<dependency>
    <groupId>com.alibaba</groupId>
    <artifactId>dashscope-sdk-java</artifactId>
    <version>2.16.7</version>
</dependency>

import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import java.util.Arrays;

public class ArticleGenerator {
    public static void main(String[] args) throws ApiException {
        // 1. 设置API Key（建议从环境变量读取）
        String apiKey = System.getenv("DASHSCOPE_API_KEY");
        
        // 2. 构造对话消息
        Message userMsg = Message.builder()
                .role(Role.USER.getValue())
                .content("请写一篇关于人工智能未来发展趋势的短文，约300字。")
                .build();
        
        // 3. 配置生成参数
        GenerationParam param = GenerationParam.builder()
                .model("qwen-plus")          // 可用 qwen-max 或 qwen-turbo
                .messages(Arrays.asList(userMsg))
                .resultFormat(GenerationParam.ResultFormat.MESSAGE)
                .temperature(0.85f)
                .maxTokens(600)
                .build();
        
        // 4. 调用API并输出结果
        Generation gen = new Generation();
        GenerationResult result = gen.call(param, apiKey);
        String article = result.getOutput().getChoices().get(0).getMessage().getContent();
        System.out.println(article);
    }
}

<dependency>
    <groupId>com.theokanning.openai-gpt3-java</groupId>
    <artifactId>service</artifactId>
    <version>0.18.2</version>
</dependency>

import com.theokanning.openai.completion.chat.ChatCompletionRequest;
import com.theokanning.openai.completion.chat.ChatMessage;
import com.theokanning.openai.completion.chat.ChatMessageRole;
import com.theokanning.openai.service.OpenAiService;

import java.util.List;

public class OpenAiArticleGenerator {
    public static void main(String[] args) {
        String token = System.getenv("OPENAI_API_KEY");
        OpenAiService service = new OpenAiService(token);
        
        ChatCompletionRequest request = ChatCompletionRequest.builder()
                .model("gpt-3.5-turbo")
                .messages(List.of(
                    new ChatMessage(ChatMessageRole.USER.value(), 
                                    "请用中文写一篇关于人工智能未来发展的文章，约300字。")
                ))
                .temperature(0.85)
                .maxTokens(600)
                .build();
        
        String article = service.createChatCompletion(request)
                .getChoices().get(0).getMessage().getContent();
        System.out.println(article);
    }
}

@Component
public class AIGenerationService {
    @Value("${ai.api-key}")
    private String apiKey;
    
    public String generateArticle(String topic, int wordCount) {
        // 复用上述Generation逻辑，但将topic和maxTokens动态传入
        // 并加入try-catch重试机制
        return article;
    }
}