diff --git a/__init__.py b/__init__.py
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diff --git a/taiyi.py b/taiyi.py
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+# Copyright 2021 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+from transformers import BertForSequenceClassification, BertConfig, BertTokenizer
+from transformers import CLIPProcessor, CLIPModel
+
+@register(output_schema=['vec'])
+class Taiyi(NNOperator): 
+    """
+    Taiyi multi-modal embedding operator
+    """
+    def __init__(self, model_name: str, modality: str):
+        self.modality = modality
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.text_tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese")
+        self.text_encoder = BertForSequenceClassification.from_pretrained("IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese").eval()
+        self.clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+        self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+
+    def inference_single_data(self, data):
+        if self.modality == 'image':
+            vec = self._inference_from_image(data)
+        elif self.modality == 'text': 
+            vec = self._inference_from_text(data)
+        else:
+            raise ValueError("modality[{}] not implemented.".format(self._modality))
+        return vec.detach().cpu().numpy().flatten()
+
+    def __call__(self, data):
+        if not isinstance(data, list):
+            data = [data]
+        else:
+            data = data
+        results = []
+        for single_data in data:
+            result = self.inference_single_data(single_data)
+            results.append(result)
+        if len(data) == 1:        
+            return results[0]
+        else:
+            return results
+
+    def _inference_from_text(self, text):
+        self.text = self.text_tokenizer(text, return_tensors='pt', padding=True)['input_ids'].to(self.device)
+        text_features = text_encoder(text).logits
+        return text_features
+
+    @arg(1, to_image_color('RGB'))
+    def _inference_from_image(self, img):
+        image = to_pil(image)
+        image = self.processor(images=image.raw), return_tensors="pt")
+        image_features = clip_model.get_image_features(**image)
+        return image_features
+