feat: add optional parameters to OpenAIEmbeddingModel for enhanced embedding functionality

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

@@ -13,10 +13,27 @@
 
 from common import forms
 from common.exception.app_exception import AppApiException
-from common.forms import BaseForm
+from common.forms import BaseForm, TooltipLabel
 from models_provider.base_model_provider import BaseModelCredential, ValidCode
 from models_provider.impl.aliyun_bai_lian_model_provider.model.embedding import AliyunBaiLianEmbedding
 
+class BaiLianEmbeddingModelParams(BaseForm):
+    dimensions = forms.SingleSelect(
+        TooltipLabel(
+            _('Dimensions'),
+            _('')
+        ),
+        required=True,
+        default_value=1024,
+        value_field='value',
+        text_field='label',
+        option_list=[
+            {'label': '1024', 'value': '1024'},
+            {'label': '768', 'value': '768'},
+            {'label': '512', 'value': '512'},
+        ]
+    )
+
 
 class AliyunBaiLianEmbeddingCredential(BaseForm, BaseModelCredential):
 
@@ -71,4 +88,8 @@ def encryption_dict(self, model: Dict[str, Any]) -> Dict[str, Any]:
         api_key = model.get('dashscope_api_key', '')
         return {**model, 'dashscope_api_key': super().encryption(api_key)}
 
+
+    def get_model_params_setting_form(self, model_name):
+        return BaiLianEmbeddingModelParams()
+
     dashscope_api_key = forms.PasswordInputField('API Key', required=True)

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

@@ -6,61 +6,43 @@
     @date：2024/10/16 16:34
     @desc:
 """
-from functools import reduce
 from typing import Dict, List
 
-from langchain_community.embeddings import DashScopeEmbeddings
-from langchain_community.embeddings.dashscope import embed_with_retry
+from openai import OpenAI
 
 from models_provider.base_model_provider import MaxKBBaseModel
 
 
-def proxy_embed_documents(texts: List[str], step_size, embed_documents):
-    value = [embed_documents(texts[start_index:start_index + step_size]) for start_index in
-             range(0, len(texts), step_size)]
-    return reduce(lambda x, y: [*x, *y], value, [])
+class AliyunBaiLianEmbedding(MaxKBBaseModel):
+    model_name: str
+    optional_params: dict
 
+    def __init__(self, api_key, model_name: str, optional_params: dict):
+        self.client = OpenAI(api_key=api_key, base_url='https://dashscope.aliyuncs.com/compatible-mode/v1').embeddings
+        self.model_name = model_name
+        self.optional_params = optional_params
 
-class AliyunBaiLianEmbedding(MaxKBBaseModel, DashScopeEmbeddings):
     @staticmethod
     def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
+        optional_params = MaxKBBaseModel.filter_optional_params(model_kwargs)
         return AliyunBaiLianEmbedding(
-            model=model_name,
-            dashscope_api_key=model_credential.get('dashscope_api_key')
+            api_key=model_credential.get('dashscope_api_key'),
+            model_name=model_name,
+            optional_params=optional_params
         )
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        if self.model == 'text-embedding-v3':
-            return proxy_embed_documents(texts, 6, self._embed_documents)
-        return self._embed_documents(texts)
-
-    def _embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Call out to DashScope's embedding endpoint for embedding search docs.
-
-        Args:
-            texts: The list of texts to embed.
-            chunk_size: The chunk size of embeddings. If None, will use the chunk size
-                specified by the class.
-
-        Returns:
-            List of embeddings, one for each text.
-        """
-        embeddings = embed_with_retry(
-            self, input=texts, text_type="document", model=self.model
-        )
-        embedding_list = [item["embedding"] for item in embeddings]
-        return embedding_list
-
-    def embed_query(self, text: str) -> List[float]:
-        """Call out to DashScope's embedding endpoint for embedding query text.
-
-        Args:
-            text: The text to embed.
-
-        Returns:
-            Embedding for the text.
-        """
-        embedding = embed_with_retry(
-            self, input=[text], text_type="document", model=self.model
-        )[0]["embedding"]
-        return embedding
+    def embed_query(self, text: str):
+        res = self.embed_documents([text])
+        return res[0]
+
+    def embed_documents(
+            self, texts: List[str], chunk_size: int | None = None
+    ) -> List[List[float]]:
+        if len(self.optional_params) > 0:
+            res = self.client.create(
+                input=texts, model=self.model_name, encoding_format="float",
+                **self.optional_params
+            )
+        else:
+            res = self.client.create(input=texts, model=self.model_name, encoding_format="float")
+        return [e.embedding for e in res.data]

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

@@ -15,17 +15,21 @@
 
 class OpenAIEmbeddingModel(MaxKBBaseModel):
     model_name: str
+    optional_params: dict
 
-    def __init__(self, api_key, base_url, model_name: str):
+    def __init__(self, api_key, base_url, model_name: str, optional_params: dict):
         self.client = openai.OpenAI(api_key=api_key, base_url=base_url).embeddings
         self.model_name = model_name
+        self.optional_params = optional_params
 
     @staticmethod
     def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
+        optional_params = MaxKBBaseModel.filter_optional_params(model_kwargs)
         return OpenAIEmbeddingModel(
             api_key=model_credential.get('api_key'),
             model_name=model_name,
             base_url=model_credential.get('api_base'),
+            optional_params=optional_params
         )
 
     def embed_query(self, text: str):
@@ -35,5 +39,11 @@ def embed_query(self, text: str):
     def embed_documents(
             self, texts: List[str], chunk_size: int | None = None
     ) -> List[List[float]]:
-        res = self.client.create(input=texts, model=self.model_name, encoding_format="float")
+        if len(self.optional_params) > 0:
+            res = self.client.create(
+                input=texts, model=self.model_name, encoding_format="float",
+                **self.optional_params
+            )
+        else:
+            res = self.client.create(input=texts, model=self.model_name, encoding_format="float")
         return [e.embedding for e in res.data]

ui/src/views/model/component/CreateModelDialog.vue

@@ -140,8 +140,7 @@
         />
         <el-empty
           v-else-if="
-            base_form_data.model_type === 'RERANKER' ||
-            base_form_data.model_type === 'EMBEDDING'
+            base_form_data.model_type === 'RERANKER'
           "
           :description="$t('views.model.tip.emptyMessage2')"
         />
@@ -150,7 +149,7 @@
           <el-button
             type="text"
             @click.stop="openAddDrawer()"
-            :disabled="!['TTS', 'LLM', 'IMAGE', 'TTI', 'TTV', 'ITV','STT'].includes(base_form_data.model_type)"
+            :disabled="!['TTS', 'LLM', 'IMAGE', 'TTI', 'TTV', 'ITV','STT', 'EMBEDDING'].includes(base_form_data.model_type)"
           >
             <AppIcon iconName="app-add-outlined" class="mr-4"/> {{ $t('common.add') }}
           </el-button>

ui/src/views/model/component/ModelCard.vue

@@ -95,6 +95,7 @@
                   currentModel.model_type === 'IMAGE' ||
                   currentModel.model_type === 'TTI' ||
                   currentModel.model_type === 'ITV' ||
+                  currentModel.model_type === 'EMBEDDING' ||
                   currentModel.model_type === 'TTV') &&
                 permissionPrecise.paramSetting(model.id)
               "