diff --git a/embedding_concat.py b/embedding_concat.py
index 84d7631..257673b 100644
--- a/embedding_concat.py
+++ b/embedding_concat.py
@@ -1,7 +1,7 @@
 import sys
 from typing import NamedTuple, List
 from pathlib import Path
-import numpy as np
+import numpy
 
 from towhee.operator import Operator
 
@@ -11,17 +11,23 @@ class EmbeddingConcat(Operator):
 
     """
 
-    def __init__(self) -> None:
+    def __init__(self, w1: float, w2: float) -> None:
         super().__init__()
+        if w1 is not None:
+            self.w1 = w1
+        else:
+            self.w1 = 1.0
+        if w2 is not None:
+            self.w2 = w2
+        else:
+            self.w2 = 1.0
         sys.path.append(str(Path(__file__).parent))
 
-    def __call__(self, input_array_list: List[np.ndarray], input_weight_list: List[float]) -> NamedTuple('Outputs', [('feature_vector', np.ndarray)]):
+    def __call__(self, emb1: numpy.ndarray, emb2: numpy.ndarray) -> NamedTuple('Outputs', [('feature_vector', numpy.ndarray)]):
         concatemb = []
-        if input_weight_list is None or input_weight_list == []:
-            input_weight_list = [1.0] * len(input_array_list)
-        for emb_part, w in zip(input_array_list, input_weight_list):
-            concatemb.append(w * emb_part)
-        concated_feature = np.hstack(concatemb).flatten()
-        Outputs = NamedTuple('Outputs', [('feature_vector', np.ndarray)])
+        concatemb.append(self.w1 * emb1)
+        concatemb.append(self.w2 * emb2)
+        concated_feature = numpy.hstack(concatemb).flatten()
+        Outputs = NamedTuple('Outputs', [('feature_vector', numpy.ndarray)])
         return Outputs(concated_feature)
 
diff --git a/embedding_concat.yaml b/embedding_concat.yaml
index d8f8f8d..9100591 100644
--- a/embedding_concat.yaml
+++ b/embedding_concat.yaml
@@ -5,9 +5,11 @@ labels:
   others: ''
 operator: towhee/embedding-concat
 init:
+  w1: float
+  w2: float
 call:
   input:
-    input_array_list: typing.List[numpy.ndarray]
-    input_weight_list: typing.List[float]
+    emb1: numpy.ndarray
+    emb2: numpy.ndarray
   output:
     feature_vector: numpy.ndarray