feat(api): support wenxin bge-large and tao embedding model. (#7393)

api/core/model_runtime/model_providers/wenxin/_common.py

@@ -118,6 +118,9 @@ class _CommonWenxin:
         'ernie-4.0-turbo-8k-preview': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-8k-preview',
         'yi_34b_chat': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/yi_34b_chat',
         'embedding-v1': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/embedding-v1',
+        'bge-large-en': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/bge_large_en',
+        'bge-large-zh': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/bge_large_zh',
+        'tao-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/tao_8k',
     }
 
     function_calling_supports = [

api/core/model_runtime/model_providers/wenxin/text_embedding/bge-large-en.yaml

@@ -0,0 +1,9 @@
+model: bge-large-en
+model_type: text-embedding
+model_properties:
+  context_size: 512
+  max_chunks: 16
+pricing:
+  input: '0.0005'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/wenxin/text_embedding/bge-large-zh.yaml

@@ -0,0 +1,9 @@
+model: bge-large-zh
+model_type: text-embedding
+model_properties:
+  context_size: 512
+  max_chunks: 16
+pricing:
+  input: '0.0005'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/wenxin/text_embedding/tao-8k.yaml

@@ -0,0 +1,9 @@
+model: tao-8k
+model_type: text-embedding
+model_properties:
+  context_size: 8192
+  max_chunks: 1
+pricing:
+  input: '0.0005'
+  unit: '0.001'
+  currency: RMB

api/tests/integration_tests/model_runtime/wenxin/test_embedding.py

@@ -5,7 +5,7 @@ from core.model_runtime.entities.text_embedding_entities import TextEmbeddingRes
 from core.model_runtime.model_providers.wenxin.text_embedding.text_embedding import WenxinTextEmbeddingModel
 
 
-def test_invoke_embedding_model():
+def test_invoke_embedding_v1():
     sleep(3)
     model = WenxinTextEmbeddingModel()
 
@@ -22,3 +22,60 @@ def test_invoke_embedding_model():
     assert isinstance(response, TextEmbeddingResult)
     assert len(response.embeddings) == 3
     assert isinstance(response.embeddings[0], list)
+
+
+def test_invoke_embedding_bge_large_en():
+    sleep(3)
+    model = WenxinTextEmbeddingModel()
+
+    response = model.invoke(
+        model='bge-large-en',
+        credentials={
+            'api_key': os.environ.get('WENXIN_API_KEY'),
+            'secret_key': os.environ.get('WENXIN_SECRET_KEY')
+        },
+        texts=['hello', '你好', 'xxxxx'],
+        user="abc-123"
+    )
+
+    assert isinstance(response, TextEmbeddingResult)
+    assert len(response.embeddings) == 3
+    assert isinstance(response.embeddings[0], list)
+
+
+def test_invoke_embedding_bge_large_zh():
+    sleep(3)
+    model = WenxinTextEmbeddingModel()
+
+    response = model.invoke(
+        model='bge-large-zh',
+        credentials={
+            'api_key': os.environ.get('WENXIN_API_KEY'),
+            'secret_key': os.environ.get('WENXIN_SECRET_KEY')
+        },
+        texts=['hello', '你好', 'xxxxx'],
+        user="abc-123"
+    )
+
+    assert isinstance(response, TextEmbeddingResult)
+    assert len(response.embeddings) == 3
+    assert isinstance(response.embeddings[0], list)
+
+
+def test_invoke_embedding_tao_8k():
+    sleep(3)
+    model = WenxinTextEmbeddingModel()
+
+    response = model.invoke(
+        model='tao-8k',
+        credentials={
+            'api_key': os.environ.get('WENXIN_API_KEY'),
+            'secret_key': os.environ.get('WENXIN_SECRET_KEY')
+        },
+        texts=['hello', '你好', 'xxxxx'],
+        user="abc-123"
+    )
+
+    assert isinstance(response, TextEmbeddingResult)
+    assert len(response.embeddings) == 3
+    assert isinstance(response.embeddings[0], list)