diff --git a/libs/community/langchain_community/vectorstores/hanavector.py b/libs/community/langchain_community/vectorstores/hanavector.py
index 4862a71385c..cc8822c888e 100644
--- a/libs/community/langchain_community/vectorstores/hanavector.py
+++ b/libs/community/langchain_community/vectorstores/hanavector.py
@@ -92,10 +92,10 @@ class HanaDB(VectorStore):
         # Check if the table exists, and eventually create it
         if not self._table_exists(self.table_name):
             sql_str = (
-                f"CREATE TABLE {self.table_name}("
-                f"{self.content_column} NCLOB, "
-                f"{self.metadata_column} NCLOB, "
-                f"{self.vector_column} REAL_VECTOR "
+                f'CREATE TABLE "{self.table_name}"('
+                f'"{self.content_column}" NCLOB, '
+                f'"{self.metadata_column}" NCLOB, '
+                f'"{self.vector_column}" REAL_VECTOR '
             )
             if self.vector_column_length == -1:
                 sql_str += ");"
@@ -228,8 +228,8 @@ class HanaDB(VectorStore):
                     else self.embedding.embed_documents([text])[0]
                 )
                 sql_str = (
-                    f"INSERT INTO {self.table_name} ({self.content_column}, "
-                    f"{self.metadata_column}, {self.vector_column}) "
+                    f'INSERT INTO "{self.table_name}" ("{self.content_column}", '
+                    f'"{self.metadata_column}", "{self.vector_column}") '
                     f"VALUES (?, ?, TO_REAL_VECTOR (?));"
                 )
                 cur.execute(
@@ -340,12 +340,12 @@ class HanaDB(VectorStore):
         embedding_as_str = ",".join(map(str, embedding))
         sql_str = (
             f"SELECT TOP {k}"
-            f"  {self.content_column}, "  # row[0]
-            f"  {self.metadata_column}, "  # row[1]
-            f"  TO_NVARCHAR({self.vector_column}), "  # row[2]
-            f"  {distance_func_name}({self.vector_column}, TO_REAL_VECTOR "
+            f'  "{self.content_column}", '  # row[0]
+            f'  "{self.metadata_column}", '  # row[1]
+            f'  TO_NVARCHAR("{self.vector_column}"), '  # row[2]
+            f'  {distance_func_name}("{self.vector_column}", TO_REAL_VECTOR '
             f"     (ARRAY({embedding_as_str}))) AS CS "  # row[3]
-            f"FROM {self.table_name}"
+            f'FROM "{self.table_name}"'
         )
         order_str = f" order by CS {HANA_DISTANCE_FUNCTION[self.distance_strategy][1]}"
         where_str, query_tuple = self._create_where_by_filter(filter)
@@ -451,7 +451,7 @@ class HanaDB(VectorStore):
             raise ValueError("Parameter 'filter' is required when calling 'delete'")
 
         where_str, query_tuple = self._create_where_by_filter(filter)
-        sql_str = f"DELETE FROM {self.table_name} {where_str}"
+        sql_str = f'DELETE FROM "{self.table_name}" {where_str}'
 
         try:
             cur = self.connection.cursor()
diff --git a/libs/community/tests/integration_tests/vectorstores/test_hanavector.py b/libs/community/tests/integration_tests/vectorstores/test_hanavector.py
index fb9ab6ec194..c725c534a9e 100644
--- a/libs/community/tests/integration_tests/vectorstores/test_hanavector.py
+++ b/libs/community/tests/integration_tests/vectorstores/test_hanavector.py
@@ -890,3 +890,37 @@ def test_invalid_metadata_keys(texts: List[str], metadatas: List[dict]) -> None:
     except ValueError:
         exception_occured = True
     assert exception_occured
+
+
+@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed")
+def test_hanavector_table_mixed_case_names(texts: List[str]) -> None:
+    table_name = "MyTableName"
+    content_column = "TextColumn"
+    metadata_column = "MetaColumn"
+    vector_column = "VectorColumn"
+
+    vectordb = HanaDB(
+        connection=test_setup.conn,
+        embedding=embedding,
+        distance_strategy=DistanceStrategy.COSINE,
+        table_name=table_name,
+        content_column=content_column,
+        metadata_column=metadata_column,
+        vector_column=vector_column,
+    )
+
+    vectordb.add_texts(texts=texts)
+
+    # check that embeddings have been created in the table
+    number_of_texts = len(texts)
+    number_of_rows = -1
+    sql_str = f'SELECT COUNT(*) FROM "{table_name}"'
+    cur = test_setup.conn.cursor()
+    cur.execute(sql_str)
+    if cur.has_result_set():
+        rows = cur.fetchall()
+        number_of_rows = rows[0][0]
+    assert number_of_rows == number_of_texts
+
+    # check results of similarity search
+    assert texts[0] == vectordb.similarity_search(texts[0], 1)[0].page_content