fix: 修复xinference向量模型添加失败的缺陷

apps/setting/models_provider/impl/xinference_model_provider/credential/embedding.py

@@ -15,7 +15,8 @@ def is_valid(self, model_type: str, model_name, model_credential: Dict[str, obje
         if not any(list(filter(lambda mt: mt.get('value') == model_type, model_type_list))):
             raise AppApiException(ValidCode.valid_error.value, f'{model_type} 模型类型不支持')
         try:
-            model_list = provider.get_base_model_list(model_credential.get('api_base'), 'embedding')
+            model_list = provider.get_base_model_list(model_credential.get('api_base'), model_credential.get('api_key'),
+                                                      'embedding')
         except Exception as e:
             raise AppApiException(ValidCode.valid_error.value, "API 域名无效")
         exist = provider.get_model_info_by_name(model_list, model_name)
@@ -36,3 +37,4 @@ def build_model(self, model_info: Dict[str, object]):
         return self
 
     api_base = forms.TextInputField('API 域名', required=True)
+    api_key = forms.PasswordInputField('API Key', required=True)

apps/setting/models_provider/impl/xinference_model_provider/model/embedding.py

@@ -1,18 +1,26 @@
 # coding=utf-8
 import threading
-from typing import Dict
+from typing import Dict, Optional, List, Any
 
 from langchain_community.embeddings import XinferenceEmbeddings
+from langchain_core.embeddings import Embeddings
 
 from setting.models_provider.base_model_provider import MaxKBBaseModel
 
 
-class XinferenceEmbedding(MaxKBBaseModel, XinferenceEmbeddings):
+class XinferenceEmbedding(MaxKBBaseModel, Embeddings):
+    client: Any
+    server_url: Optional[str]
+    """URL of the xinference server"""
+    model_uid: Optional[str]
+    """UID of the launched model"""
+
     @staticmethod
     def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
         return XinferenceEmbedding(
             model_uid=model_name,
             server_url=model_credential.get('api_base'),
+            api_key=model_credential.get('api_key'),
         )
 
     def down_model(self):
@@ -22,3 +30,63 @@ def start_down_model_thread(self):
         thread = threading.Thread(target=self.down_model)
         thread.daemon = True
         thread.start()
+
+    def __init__(
+            self, server_url: Optional[str] = None, model_uid: Optional[str] = None,
+            api_key: Optional[str] = None
+    ):
+        try:
+            from xinference.client import RESTfulClient
+        except ImportError:
+            try:
+                from xinference_client import RESTfulClient
+            except ImportError as e:
+                raise ImportError(
+                    "Could not import RESTfulClient from xinference. Please install it"
+                    " with `pip install xinference` or `pip install xinference_client`."
+                ) from e
+
+        if server_url is None:
+            raise ValueError("Please provide server URL")
+
+        if model_uid is None:
+            raise ValueError("Please provide the model UID")
+
+        self.server_url = server_url
+
+        self.model_uid = model_uid
+
+        self.api_key = api_key
+
+        self.client = RESTfulClient(server_url, api_key)
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of documents using Xinference.
+        Args:
+            texts: The list of texts to embed.
+        Returns:
+            List of embeddings, one for each text.
+        """
+
+        model = self.client.get_model(self.model_uid)
+
+        embeddings = [
+            model.create_embedding(text)["data"][0]["embedding"] for text in texts
+        ]
+        return [list(map(float, e)) for e in embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a query of documents using Xinference.
+        Args:
+            text: The text to embed.
+        Returns:
+            Embeddings for the text.
+        """
+
+        model = self.client.get_model(self.model_uid)
+
+        embedding_res = model.create_embedding(text)
+
+        embedding = embedding_res["data"][0]["embedding"]
+
+        return list(map(float, embedding))