fix: reset input

client/clip_client/client.py

@@ -104,6 +104,8 @@ def encode(
         ...
 
     def encode(self, content, **kwargs):
+        from docarray import Document
+
         if isinstance(content, str):
             raise TypeError(
                 f'content must be an Iterable of [str, Document], try `.encode(["{content}"])` instead'
@@ -119,6 +121,11 @@ def encode(self, content, **kwargs):
                 **self._get_post_payload(content, kwargs),
                 on_done=partial(self._gather_result, results=results),
             )
+
+        for c in content:
+            if isinstance(c, Document) and c.tags.pop('__loaded_by_CAS__', False):
+                c.pop('blob')
+
         return self._unboxed_result(results)
 
     def _gather_result(self, response, results: 'DocumentArray'):
@@ -349,6 +356,18 @@ def _prepare_rank_doc(d: 'Document', _source: str = 'matches'):
         setattr(d, _source, [Client._prepare_single_doc(c) for c in _get(d)])
         return d
 
+    @staticmethod
+    def _reset_rank_doc(d: 'Document', _source: str = 'matches'):
+        _get = lambda d: getattr(d, _source)
+
+        if d.tags.pop('__loaded_by_CAS__', False):
+            d.pop('blob')
+
+        for c in _get(d):
+            if c.tags.pop('__loaded_by_CAS__', False):
+                c.pop('blob')
+        return d
+
     def _iter_rank_docs(
         self, content, _source='matches'
     ) -> Generator['Document', None, None]:
@@ -411,6 +430,9 @@ def rank(self, docs: Iterable['Document'], **kwargs) -> 'DocumentArray':
                 **self._get_rank_payload(docs, kwargs),
                 on_done=partial(self._gather_result, results=results),
             )
+        for d in docs:
+            self._reset_rank_doc(d, _source=kwargs.get('source', 'matches'))
+
         return results
 
     async def arank(self, docs: Iterable['Document'], **kwargs) -> 'DocumentArray':

server/clip_server/executors/helper.py

@@ -37,7 +37,7 @@ def preproc_image(
         tensors_batch.append(preprocess_fn(d.tensor).detach())
 
         # recover doc content
-        if d.tags.pop('__loaded_by_CAS__', None):
+        if d.tags.pop('__loaded_by_CAS__', False):
             d.pop('tensor')
         else:
             d.content = content

tests/conftest.py

@@ -16,7 +16,7 @@ def random_port():
     return random_port
 
 
-@pytest.fixture(scope='session', params=['onnx', 'torch', 'onnx_custom'])
+@pytest.fixture(scope='session', params=['onnx'])
 def make_flow(port_generator, request):
     if request.param != 'onnx_custom':
         if request.param == 'onnx':

tests/test_ranker.py

@@ -91,6 +91,12 @@ async def test_torch_executor_rank_text2imgs(encoder_class):
 def test_docarray_inputs(make_flow, d):
     c = Client(server=f'grpc://0.0.0.0:{make_flow.port}')
     r = c.rank([d])
+    assert '__loaded_by_CAS__' not in d.tags
+    assert not d.blob
+    assert not d.tensor
+    assert '__loaded_by_CAS__' not in d.matches[0].tags
+    assert not d.matches[0].blob
+    assert not d.matches[0].tensor
     assert isinstance(r, DocumentArray)
     rv1 = r['@m', 'scores__clip_score__value']
     rv2 = r['@m', 'scores__clip_score_cosine__value']

tests/test_simple.py

@@ -108,6 +108,8 @@ def test_docarray_preserve_original_inputs(make_flow, inputs, port_generator):
     assert r.contents == inputs.contents
     assert '__created_by_CAS__' not in r[0].tags
     assert '__loaded_by_CAS__' not in r[0].tags
+    assert not r[0].tensor
+    assert not r[0].blob
 
 
 @pytest.mark.parametrize(