openCypher: the Cypher 25 SEARCH clause is parsed and dropped, so a vector search without SCORE AS returns every node of the label #10776

// What ArcadeDB's openCypher does with Neo4j's vector syntax (2026-10-27): the db.index.vector.queryNodes procedure, the Cypher 25 SEARCH clause
// (Neo4j 2026.01+, the replacement of queryNodes since Neo4j 2026.04) and CREATE VECTOR INDEX. Prints one CHECK/INFO line per case; never exits nonzero
// on an unsupported feature (that is the finding), only on a setup failure.
import com.arcadedb.database.Database;
import com.arcadedb.database.DatabaseFactory;
import com.arcadedb.query.sql.executor.Result;
import com.arcadedb.query.sql.executor.ResultSet;
import java.util.*;

public class CypherVectorProbe {
  static List<Result> run(Database db, String q, Map<String, Object> p) {
    List<Result> out = new ArrayList<>();
    try (ResultSet rs = db.query("opencypher", q, p)) { while (rs.hasNext()) out.add(rs.next()); }
    return out;
  }

  public static void main(String[] a) {
    try (DatabaseFactory f = new DatabaseFactory(a[0])) {
      if (f.exists()) f.open().drop();
      try (Database db = f.create()) {
        final int n = 50, dim = 4; final Random r = new Random(7);
        db.command("sql", "CREATE VERTEX TYPE Doc"); db.command("sql", "CREATE PROPERTY Doc.name STRING"); db.command("sql", "CREATE PROPERTY Doc.embedding ARRAY_OF_FLOATS");
        db.transaction(() -> { for (int i = 0; i < n; i++) { float[] v = new float[dim]; for (int j = 0; j < dim; j++) v[j] = (float) r.nextGaussian();
          db.command("sql", "INSERT INTO Doc SET name = ?, embedding = ?", "d" + i, v); } });
        db.command("sql", "CREATE INDEX ON Doc (embedding) LSM_VECTOR METADATA {\"dimensions\": 4, \"similarity\": \"COSINE\"}");
        final List<Float> q = List.of(0.5f, -0.2f, 0.1f, 0.9f); final Map<String, Object> p = Map.of("q", q);
        String[][] cases = {
          {"queryNodes", "CALL db.index.vector.queryNodes('Doc[embedding]', 3, $q) YIELD node, score RETURN node.name AS name, score"},
          {"SEARCH", "MATCH (d:Doc) SEARCH d IN (VECTOR INDEX `Doc[embedding]` FOR $q LIMIT 3) SCORE AS s RETURN d.name AS name, s"},
          {"SEARCH, no SCORE", "MATCH (d:Doc) SEARCH d IN (VECTOR INDEX `Doc[embedding]` FOR $q LIMIT 3) RETURN d.name AS name"},
          {"SEARCH+WHERE", "MATCH (d:Doc) SEARCH d IN (VECTOR INDEX `Doc[embedding]` FOR $q WHERE d.name <> 'd0' LIMIT 3) SCORE AS s RETURN d.name AS name, s"},
        };
        for (String[] c : cases) {
          try { List<Result> rows = run(db, c[1], p);
            System.out.println("\n" + (rows.size() == 3 ? "CHECK ok   " : "CHECK FAIL ") + c[0] + ": " + rows.size() + " rows (expected 3 of " + n + ")" + (rows.isEmpty() ? "" : "; first " + rows.get(0).toJSON()));
          } catch (Exception e) { System.out.println("INFO unsupported " + c[0] + ": " + e.getClass().getSimpleName() + ": " + String.valueOf(e.getMessage()).split("\n")[0]); }
        }
        try { db.command("opencypher", "CREATE VECTOR INDEX v2 IF NOT EXISTS FOR (n:Doc2) ON n.embedding OPTIONS {indexConfig: {`vector.dimensions`: 4, `vector.similarity_function`: 'cosine'}}");
          System.out.println("INFO CREATE VECTOR INDEX accepted; Doc2 index exists: " + (db.getSchema().existsType("Doc2") ? db.getSchema().getType("Doc2").getAllIndexes(false).size() : 0));
        } catch (Exception e) { System.out.println("INFO unsupported CREATE VECTOR INDEX: " + e.getClass().getSimpleName() + ": " + String.valueOf(e.getMessage()).split("\n")[0]); }
      }
    }
    System.out.println("RESULT java=" + System.getProperty("java.version"));
  }
}