ad490a63c84a1701.js","7474","static/chunks/7474-49ba6ec3c1d01a28.js","8090","static/chunks/8090-30c82671ddf5b306.js","8115","static/chunks/8115-3670e99fee225168.js","2092","static/chunks/2092-3fbabbadb71f9617.js","3846","static/chunks/3846-54059dbba1733522.js","6517","static/chunks/6517-b17c38227fa0d2e6.js","766","static/chunks/766-12dbb05e2adf85ab.js","4150","static/chunks/app/courses/%5BcourseSlug%5D/lessons/%5BunitSlug%5D/page-1fc213a0f4568bd8.js"],"Image"] 3b:T486,import java.util.*; import org.apache.commons.math3.linear.*; public class BOWSearch { public static RealVector bowVectorize(String text, Map vocab) { double[] vector = new double[vocab.size()]; String[] words = text.toLowerCase().split("\\s+"); for (String word : words) { String cleanWord = word.replaceAll("[.,!?]", ""); if (vocab.containsKey(cleanWord)) { vector[vocab.get(cleanWord)] += 1; } } return new ArrayRealVector(vector); } public static List> bowSearch(String query, List docs, Map vocab) { RealVector queryVec = bowVectorize(query, vocab); List> scores = new ArrayList<>(); for (int i = 0; i < docs.size(); i++) { RealVector docVec = bowVectorize(docs.get(i), vocab); double score = queryVec.dotProduct(docVec); scores.add(new AbstractMap.SimpleEntry<>(i, score)); } scores.sort((a, b) -> Double.compare(b.getValue(), a.getValue())); return scores; } }3c:T500,import com.github.jelmerk.easybert.Bert; import com.github.jelmerk.easybert.BertModel; import com.github.jelmerk.easybert.BertSentenceEncoder; import java.util.*; public class EmbeddingSearch { public static double cosSim(float[] a, float[] b) { double dotProduct = 0.0, normA = 0.0, normB = 0.0; for (int i = 0; i < a.length; i++) { dotProduct += a[i] * b[i]; no