【代码新增】AI:适配 提供 OllamaChatModelTests,用于本地 ollma 模型的调用
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package cn.iocoder.yudao.framework.ai.chat;
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import org.junit.jupiter.api.Disabled;
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import org.junit.jupiter.api.Test;
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import org.springframework.ai.chat.messages.Message;
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import org.springframework.ai.chat.messages.SystemMessage;
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import org.springframework.ai.chat.messages.UserMessage;
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import org.springframework.ai.chat.model.ChatResponse;
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import org.springframework.ai.chat.prompt.Prompt;
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import org.springframework.ai.ollama.OllamaChatModel;
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import org.springframework.ai.ollama.api.OllamaApi;
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import org.springframework.ai.ollama.api.OllamaOptions;
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import reactor.core.publisher.Flux;
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import java.util.ArrayList;
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import java.util.List;
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/**
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* {@link OllamaChatModel} 集成测试
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*
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* @author 芋道源码
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*/
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public class OllamaChatModelTests {
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private final OllamaChatModel chatModel = OllamaChatModel.builder()
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.ollamaApi(new OllamaApi("http://127.0.0.1:11434")) // Ollama 服务地址
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.defaultOptions(OllamaOptions.builder()
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// .model("qwen") // 模型(https://ollama.com/library/qwen)
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.model("deepseek-r1") // 模型(https://ollama.com/library/deepseek-r1)
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.build())
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.build();
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@Test
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@Disabled
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public void testCall() {
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// 准备参数
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List<Message> messages = new ArrayList<>();
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messages.add(new SystemMessage("你是一个优质的文言文作者,用文言文描述着各城市的人文风景。"));
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messages.add(new UserMessage("1 + 1 = ?"));
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// 调用
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ChatResponse response = chatModel.call(new Prompt(messages));
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// 打印结果
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System.out.println(response);
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System.out.println(response.getResult().getOutput());
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}
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@Test
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@Disabled
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public void testStream() {
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// 准备参数
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List<Message> messages = new ArrayList<>();
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messages.add(new SystemMessage("你是一个优质的文言文作者,用文言文描述着各城市的人文风景。"));
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messages.add(new UserMessage("1 + 1 = ?"));
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// 调用
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Flux<ChatResponse> flux = chatModel.stream(new Prompt(messages));
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// 打印结果
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flux.doOnNext(response -> {
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// System.out.println(response);
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System.out.println(response.getResult().getOutput());
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}).then().block();
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}
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}
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