<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-core</artifactId>
    <version>1.0.0-beta7</version>
</dependency>
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-nlp</artifactId>
    <version>1.0.0-beta7</version>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>

import org.deeplearning4j.models.word2vec.Word2Vec;
import org.deeplearning4j.text.sentenceiterator.LineSentenceIterator;
import org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor;
import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
import java.io.File;
import java.util.ArrayList;
import java.util.List;
public class DataPreprocessor {
    public static List<List<Integer>> preprocessData(String dataFilePath) {
        try {
            // 加載詞向量模型
            Word2Vec word2Vec = Word2Vec.load(new File("path/to/word2vec/model"));
            // 創(chuàng)建句子迭代器
            LineSentenceIterator iterator = new LineSentenceIterator(new File(dataFilePath));
            // 創(chuàng)建令牌工廠
            TokenizerFactory tokenizerFactory = new DefaultTokenizerFactory();
            tokenizerFactory.setTokenPreProcessor(new CommonPreprocessor());
            // 遍歷數(shù)據(jù)集，將問題和答案轉(zhuǎn)換為數(shù)字序列
            List<List<Integer>> data = new ArrayList<>();
            while (iterator.hasNext()) {
                String sentence = iterator.next();
                String[] tokens = tokenizerFactory.create(sentence).getTokens();
                List<Integer> vectorSequence = new ArrayList<>();
                for (String token : tokens) {
                    if (word2Vec.hasWord(token)) {
                        vectorSequence.add(word2Vec.indexOf(token));
                    }
                }
                data.add(vectorSequence);
            }
            return data;
        } catch (Exception e) {
            e.printStackTrace();
            return null;
        }
    }
}

import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.LSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Adam;
import java.util.List;
public class LSTMModel {
    public static MultiLayerNetwork buildModel(int inputSize, int outputSize, int hiddenSize, int numLayers) {
        MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
               .weightInit(WeightInit.XAVIER)
               .updater(new Adam())
               .list()
               .layer(0, new LSTM.Builder()
                       .nIn(inputSize)
                       .nOut(hiddenSize)
                       .activation(Activation.TANH)
                       .build())
               .layer(1, new LSTM.Builder()
                       .nIn(hiddenSize)
                       .nOut(hiddenSize)
                       .activation(Activation.TANH)
                       .build())
               .layer(2, new RnnOutputLayer.Builder()
                       .nIn(hiddenSize)
                       .nOut(outputSize)
                       .activation(Activation.SOFTMAX)
                       .build())
               .build();
        return new MultiLayerNetwork(configuration);
    }
    public static void trainModel(MultiLayerNetwork model, List<List<Integer>> data, int epochs) {
        for (int epoch = 0; epoch < epochs; epoch++) {
            for (List<Integer> inputSequence : data) {
                INDArray input = Nd4j.create(inputSequence);
                INDArray output = model.rnnTimeStep(input).get(0);
                model.fit(input, output);
            }
        }
    }
}

import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import java.util.ArrayList;
import java.util.List;
public class ChatbotService {
    private MultiLayerNetwork model;
    private int outputSize;
    public ChatbotService(MultiLayerNetwork model, int outputSize) {
        this.model = model;
        this.outputSize = outputSize;
    }
    public String answerQuestion(String question) {
        try {
            // 預(yù)處理問題，將其轉(zhuǎn)換為數(shù)字序列
            List<Integer> inputSequence = preprocessQuestion(question);
            // 將輸入序列轉(zhuǎn)換為 INDArray
            INDArray input = Nd4j.create(inputSequence);
            // 使用模型預(yù)測答案
            INDArray output = model.rnnTimeStep(input).get(0);
            // 將預(yù)測結(jié)果轉(zhuǎn)換為答案
            return convertOutputToAnswer(output);
        } catch (Exception e) {
            e.printStackTrace();
            return "抱歉，我無法回答您的問題。";
        }
    }
    private List<Integer> preprocessQuestion(String question) {
        // 與數(shù)據(jù)預(yù)處理中的方法類似，將問題轉(zhuǎn)換為數(shù)字序列
        //...
        return null;
    }
    private String convertOutputToAnswer(INDArray output) {
        // 根據(jù)輸出結(jié)果找到對應(yīng)的答案
        //...
        return null;
    }
}

import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestBody;
import org.springframework.web.bind.annotation.RestController;
@RestController
public class ChatbotController {
    private ChatbotService chatbotService;
    public ChatbotController(ChatbotService chatbotService) {
        this.chatbotService = chatbotService;
    }
    @PostMapping("/answer")
    public String answerQuestion(@RequestBody String question) {
        return chatbotService.answerQuestion(question);
    }
}

import org.junit.jupiter.api.Test;
import java.util.List;
import static org.junit.jupiter.api.Assertions.assertEquals;
public class DataPreprocessorTest {
    @Test
    public void testPreprocessData() {
        List<List<Integer>> data = DataPreprocessor.preprocessData("path/to/data/file");
        // 驗(yàn)證數(shù)據(jù)預(yù)處理的結(jié)果
        //...
    }
}

import org.junit.jupiter.api.Test;
import java.util.List;
import static org.junit.jupiter.api.Assertions.assertEquals;
public class LSTMModelTest {
    @Test
    public void testTrainModel() {
        List<List<Integer>> data = DataPreprocessor.preprocessData("path/to/data/file");
        int inputSize = 100;
        int outputSize = 10;
        int hiddenSize = 128;
        int numLayers = 2;
        MultiLayerNetwork model = LSTMModel.buildModel(inputSize, outputSize, hiddenSize, numLayers);
        LSTMModel.trainModel(model, data, 10);
        // 驗(yàn)證模型訓(xùn)練的結(jié)果
        //...
    }
}

import org.junit.jupiter.api.Test;
import static org.junit.jupiter.api.Assertions.assertEquals;
public class ChatbotServiceTest {
    @Test
    public void testAnswerQuestion() {
        int inputSize = 100;
        int outputSize = 10;
        int hiddenSize = 128;
        int numLayers = 2;
        MultiLayerNetwork model = LSTMModel.buildModel(inputSize, outputSize, hiddenSize, numLayers);
        ChatbotService chatbotService = new ChatbotService(model, outputSize);
        String question = "請問如何注冊賬號？";
        String answer = chatbotService.answerQuestion(question);
        assertEquals("您可以在我們的官網(wǎng)首頁點(diǎn)擊注冊按鈕，按照提示填寫相關(guān)信息即可注冊賬號。", answer);
    }
}