diff --git a/mindsdb_sql/planner/query_planner.py b/mindsdb_sql/planner/query_planner.py
index 8ba08910..2e5ee335 100644
--- a/mindsdb_sql/planner/query_planner.py
+++ b/mindsdb_sql/planner/query_planner.py
@@ -1446,7 +1446,50 @@ def find_embeddings_field(node, **kwargs):
 
             return self.plan_insert(query)
         else:
-            raise NotImplementedError("Not implemented insert without select")
+            if not query.columns:
+                raise PlanningException("Columns list is empty when using values")
+
+            keys = [column.name for column in query.columns]
+            is_embeddings_field_present = EMBEDDINGS_FIELD in keys
+
+            query.table = Identifier(vector_database_table)
+            # directly dispatch to the underlying storage table
+            if is_embeddings_field_present:
+                return self.plan_insert(query)
+
+            # if the embeddings field is not present in the columns list
+            # we need to wrap values in ast.Data
+            # join it with a model table
+            # modify the query using from_table
+            # and dispatch to the underlying storage table
+
+            records = []
+            _unwrap_constant_or_self = lambda node: node.value if isinstance(node, Constant) else node
+            for row in query.values:
+                records.append(
+                    dict(
+                        zip(
+                            keys,
+                            map(_unwrap_constant_or_self, row)
+                        )
+                    )
+                )
+
+            data = ast.Data(records, alias=Identifier("data"))
+            predictor_select = Select(
+                targets=[Identifier(col.name) for col in query.columns] + [Identifier(EMBEDDINGS_FIELD)],
+                from_table=Join(
+                    left=data,
+                    right=Identifier(model_name),
+                    join_type="JOIN"
+                )
+            )
+
+            query.columns += [ast.TableColumn(name=EMBEDDINGS_FIELD)]
+            query.from_select = predictor_select
+            query.values = None
+
+            return self.plan_insert(query)
 
     # method for compatibility
     def from_query(self, query=None):
diff --git a/tests/test_planner/test_knowledege_base.py b/tests/test_planner/test_knowledege_base.py
index 806cf6ef..5017a3bc 100644
--- a/tests/test_planner/test_knowledege_base.py
+++ b/tests/test_planner/test_knowledege_base.py
@@ -67,9 +67,8 @@ def test_insert_into_kb(planner_context):
     (2, 'hello world', '{"a": 1, "b": 2}'),
     (3, 'hello world', '{"a": 1, "b": 2}');
     """
-    # this will dispatch the underlying dataframes to the underlying model
-    # then it will dispatch the query to the underlying storage
-    # TODO: need to figure out what to do with this situation
+    plan = _plan_sql(sql)
+    assert len(plan.steps) > 0  # TODO: better to specify t the detail of the plan
 
     # insert into kb with select
     sql = """