diff --git a/libs/community/langchain_community/vectorstores/neo4j_vector.py b/libs/community/langchain_community/vectorstores/neo4j_vector.py
index caefda20420..e083f3ba3e0 100644
--- a/libs/community/langchain_community/vectorstores/neo4j_vector.py
+++ b/libs/community/langchain_community/vectorstores/neo4j_vector.py
@@ -854,11 +854,11 @@ class Neo4jVector(VectorStore):
             "CALL { WITH row "
             f"MERGE (c:`{self.node_label}` {{id: row.id}}) "
             "WITH c, row "
-            f"CALL db.create.setVectorProperty(c, "
+            f"CALL db.create.setNodeVectorProperty(c, "
             f"'{self.embedding_node_property}', row.embedding) "
-            "YIELD node "
             f"SET c.`{self.text_node_property}` = row.text "
-            "SET c += row.metadata } IN TRANSACTIONS OF 1000 ROWS"
+            "SET c += row.metadata "
+            "} IN TRANSACTIONS OF 1000 ROWS "
         )
 
         parameters = {
@@ -1462,9 +1462,9 @@ class Neo4jVector(VectorStore):
                 "UNWIND $data AS row "
                 f"MATCH (n:`{node_label}`) "
                 "WHERE elementId(n) = row.id "
-                f"CALL db.create.setVectorProperty(n, "
+                f"CALL db.create.setNodeVectorProperty(n, "
                 f"'{embedding_node_property}', row.embedding) "
-                "YIELD node RETURN count(*)",
+                "RETURN count(*)",
                 params=params,
             )
             # If embedding calculation should be stopped
diff --git a/libs/community/tests/integration_tests/vectorstores/test_neo4jvector.py b/libs/community/tests/integration_tests/vectorstores/test_neo4jvector.py
index 50869b985f2..761450f4a91 100644
--- a/libs/community/tests/integration_tests/vectorstores/test_neo4jvector.py
+++ b/libs/community/tests/integration_tests/vectorstores/test_neo4jvector.py
@@ -199,7 +199,10 @@ def test_neo4jvector_with_metadatas_with_scores() -> None:
         password=password,
         pre_delete_collection=True,
     )
-    output = docsearch.similarity_search_with_score("foo", k=1)
+    output = [
+        (doc, round(score, 1))
+        for doc, score in docsearch.similarity_search_with_score("foo", k=1)
+    ]
     assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)]
 
     drop_vector_indexes(docsearch)