Example.java

package com.example;

import java.io.IOException;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.sql.Statement;
import java.util.ArrayList;
import java.util.List;
import ai.djl.ModelException;
import ai.djl.huggingface.translator.TextEmbeddingTranslatorFactory;
import ai.djl.inference.Predictor;
import ai.djl.repository.zoo.Criteria;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.translate.TranslateException;
import com.pgvector.PGvector;

public class Example {
    public static void main(String[] args) throws IOException, ModelException, SQLException, TranslateException {
        Connection conn = DriverManager.getConnection("jdbc:postgresql://localhost:5432/pgvector_example");

        Statement setupStmt = conn.createStatement();
        setupStmt.executeUpdate("CREATE EXTENSION IF NOT EXISTS vector");
        setupStmt.executeUpdate("DROP TABLE IF EXISTS documents");

        PGvector.addVectorType(conn);

        Statement createStmt = conn.createStatement();
        createStmt.executeUpdate("CREATE TABLE documents (id bigserial PRIMARY KEY, content text, embedding vector(384))");

        ZooModel<String, float[]> model = loadModel("sentence-transformers/all-MiniLM-L6-v2");

        String[] input = {
            "The dog is barking",
            "The cat is purring",
            "The bear is growling"
        };
        List<float[]> embeddings = generateEmbeddings(model, input);

        for (int i = 0; i < input.length; i++) {
            PreparedStatement insertStmt = conn.prepareStatement("INSERT INTO documents (content, embedding) VALUES (?, ?)");
            insertStmt.setString(1, input[i]);
            insertStmt.setObject(2, new PGvector(embeddings.get(i)));
            insertStmt.executeUpdate();
        }

        long documentId = 2;
        PreparedStatement neighborStmt = conn.prepareStatement("SELECT * FROM documents WHERE id != ? ORDER BY embedding <=> (SELECT embedding FROM documents WHERE id = ?) LIMIT 5");
        neighborStmt.setLong(1, documentId);
        neighborStmt.setLong(2, documentId);
        ResultSet rs = neighborStmt.executeQuery();
        while (rs.next()) {
            System.out.println(rs.getString("content"));
        }

        conn.close();
    }

    private static ZooModel<String, float[]> loadModel(String id) throws IOException, ModelException {
        return Criteria.builder()
            .setTypes(String.class, float[].class)
            .optModelUrls("djl://ai.djl.huggingface.pytorch/" + id)
            .optEngine("PyTorch")
            .optTranslatorFactory(new TextEmbeddingTranslatorFactory())
            .build()
            .loadModel();
    }

    private static List<float[]> generateEmbeddings(ZooModel<String, float[]> model, String[] input) throws TranslateException {
        Predictor<String, float[]> predictor = model.newPredictor();
        List<float[]> embeddings = new ArrayList<>(input.length);
        for (String text : input) {
            embeddings.add(predictor.predict(text));
        }
        return embeddings;
    }
}