feat: add support for v2 API version in embedding models and update validation logic

apps/models_provider/impl/openai_model_provider/model/llm.py

@@ -9,7 +9,6 @@
 from typing import List, Dict
 
 from langchain_core.messages import BaseMessage, get_buffer_string
-from langchain_openai.chat_models import ChatOpenAI
 
 from common.config.tokenizer_manage_config import TokenizerManage
 from models_provider.base_model_provider import MaxKBBaseModel

apps/models_provider/impl/wenxin_model_provider/credential/embedding.py

@@ -21,11 +21,27 @@ class QianfanEmbeddingCredential(BaseForm, BaseModelCredential):
 
     def is_valid(self, model_type: str, model_name, model_credential: Dict[str, object], model_params, provider,
                  raise_exception=False):
-        model_type_list = provider.get_model_type_list()
+        api_version = model_credential.get('api_version', 'v1')
-        if not any(list(filter(lambda mt: mt.get('value') == model_type, model_type_list))):
+        model = provider.get_model(model_type, model_name, model_credential, **model_params)
-            raise AppApiException(ValidCode.valid_error.value,
+        if api_version == 'v1':
-                                  _('{model_type} Model type is not supported').format(model_type=model_type))
+            model_type_list = provider.get_model_type_list()
-        self.valid_form(model_credential)
+            if not any(list(filter(lambda mt: mt.get('value') == model_type, model_type_list))):
+                raise AppApiException(ValidCode.valid_error.value,
+                                      _('{model_type} Model type is not supported').format(model_type=model_type))
+            model_info = [model.lower() for model in model.client.models()]
+            if not model_info.__contains__(model_name.lower()):
+                raise AppApiException(ValidCode.valid_error.value,
+                                      _('{model_name} The model does not support').format(model_name=model_name))
+        required_keys = ['qianfan_ak', 'qianfan_sk']
+        if api_version == 'v2':
+            required_keys = ['api_base', 'qianfan_ak']
+
+        for key in required_keys:
+            if key not in model_credential:
+                if raise_exception:
+                    raise AppApiException(ValidCode.valid_error.value, _('{key}  is required').format(key=key))
+                else:
+                    return False
         try:
             model = provider.get_model(model_type, model_name, model_credential)
             model.embed_query(_('Hello'))
@@ -42,8 +58,25 @@ class QianfanEmbeddingCredential(BaseForm, BaseModelCredential):
         return True
 
     def encryption_dict(self, model: Dict[str, object]):
-        return {**model, 'qianfan_sk': super().encryption(model.get('qianfan_sk', ''))}
+        api_version = model.get('api_version', 'v1')
+        if api_version == 'v1':
+            return {**model, 'qianfan_sk': super().encryption(model.get('qianfan_sk', ''))}
+        else:  # v2
+            return {**model, 'qianfan_ak': super().encryption(model.get('qianfan_ak', ''))}
 
+    api_version = forms.Radio('API Version', required=True, text_field='label', value_field='value',
+                              option_list=[
+                                  {'label': 'v1', 'value': 'v1'},
+                                  {'label': 'v2', 'value': 'v2'}
+                              ],
+                              default_value='v1',
+                              provider='',
+                              method='', )
+
+    # v2版本字段
+    api_base = forms.TextInputField("API URL", required=True, relation_show_field_dict={"api_version": ["v2"]})
+
+    # v1版本字段
     qianfan_ak = forms.PasswordInputField('API Key', required=True)
-
+    qianfan_sk = forms.PasswordInputField("Secret Key", required=True,
-    qianfan_sk = forms.PasswordInputField("Secret Key", required=True)
+                                          relation_show_field_dict={"api_version": ["v1"]})

apps/models_provider/impl/wenxin_model_provider/model/embedding.py

@@ -6,18 +6,60 @@
     @date：2024/10/17 16:48
     @desc:
 """
-from typing import Dict
+from typing import Dict, List
-
 from langchain_community.embeddings import QianfanEmbeddingsEndpoint
-
+import openai
 from models_provider.base_model_provider import MaxKBBaseModel
 
 
-class QianfanEmbeddings(MaxKBBaseModel, QianfanEmbeddingsEndpoint):
+class QianfanV1Embeddings(MaxKBBaseModel, QianfanEmbeddingsEndpoint):
     @staticmethod
     def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
-        return QianfanEmbeddings(
+        return QianfanV1Embeddings(
             model=model_name,
             qianfan_ak=model_credential.get('qianfan_ak'),
             qianfan_sk=model_credential.get('qianfan_sk'),
         )
+
+
+class QianfanV2EmbeddingModel(MaxKBBaseModel):
+    model_name: str
+
+    @staticmethod
+    def is_cache_model():
+        return False
+
+    def __init__(self, api_key, base_url, model_name: str):
+        self.client = openai.OpenAI(api_key=api_key, base_url=base_url).embeddings
+        self.model_name = model_name
+
+    @staticmethod
+    def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
+        return QianfanV2EmbeddingModel(
+            api_key=model_credential.get('qianfan_ak'),
+            model_name=model_name,
+            base_url=model_credential.get('api_base'),
+        )
+
+    def embed_query(self, text: str):
+        res = self.embed_documents([text])
+        return res[0]
+
+    def embed_documents(
+            self, texts: List[ str],
+    ) -> List[List[float]]:
+        res = self.client.create(input=texts, model=self.model_name, encoding_format="float")
+        return [e.embedding for e in res.data]
+
+
+class QianfanEmbeddings(MaxKBBaseModel):
+
+
+    @staticmethod
+    def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
+        api_version = model_credential.get('api_version', 'v1')
+
+        if api_version == "v1":
+            return QianfanV1Embeddings.new_instance(model_type, model_name, model_credential, **model_kwargs)
+        elif api_version == "v2":
+            return QianfanV2EmbeddingModel.new_instance(model_type, model_name, model_credential, **model_kwargs)