diff --git a/README.md b/README.md
index 7cb4061..b2dc103 100644
--- a/README.md
+++ b/README.md
@@ -30,11 +30,9 @@ p = pipe.input('vec')  \
     .map('rows', ('id', 'score'), lambda x: (x[0], x[1], x[2]))  \
     .output('id', 'score')
 
-p('cat')
+p(<your-vector>)
 ```
 
-<img src="./result.png" height="100px"/>
-
 <br />
 
 
diff --git a/faiss.py b/faiss.py
index b222a25..d14b038 100644
--- a/faiss.py
+++ b/faiss.py
@@ -2,12 +2,13 @@ import numpy as np
 from pathlib import Path
 import faiss
 from towhee import register
+from towhee.operator import PyOperator, SharedType
 from towhee.utils.thirdparty.faiss_utils import KVStorage
 # from towhee.functional.entity import Entity
 
 
 @register(output_schema=['result'])
-class Faiss:
+class Faiss(PyOperator):
     """
     Search for embedding vectors in Faiss. Note that the index has data before searching,
     refer to DataCollection Mixin `to_faiss`.
@@ -59,3 +60,7 @@ class Faiss:
             result.extend([k, scores[0][i]])
             # result.append(Entity(**{'key': k, 'score': scores[0][i]}))
         return result
+    
+    @property
+    def shared_type(self):
+        return SharedType.NotShareable