feat: use xinference client instead of xinference (#1339)

api/core/third_party/langchain/embeddings/xinference_embedding.py

@@ -1,21 +1,54 @@
-from typing import List
+from typing import List, Optional, Any
 
 import numpy as np
-from langchain.embeddings import XinferenceEmbeddings
+from langchain.embeddings.base import Embeddings
+from xinference_client.client.restful.restful_client import Client
 
 
-class XinferenceEmbedding(XinferenceEmbeddings):
+class XinferenceEmbeddings(Embeddings):
+    client: Any
+    server_url: Optional[str]
+    """URL of the xinference server"""
+    model_uid: Optional[str]
+    """UID of the launched model"""
+
+    def __init__(
+            self, server_url: Optional[str] = None, model_uid: Optional[str] = None
+    ):
+
+        super().__init__()
+
+        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.client = Client(server_url)
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        vectors = super().embed_documents(texts)
+        model = self.client.get_model(self.model_uid)
 
+        embeddings = [
+            model.create_embedding(text)["data"][0]["embedding"] for text in texts
+        ]
+        vectors = [list(map(float, e)) for e in embeddings]
         normalized_vectors = [(vector / np.linalg.norm(vector)).tolist() for vector in vectors]
 
         return normalized_vectors
 
     def embed_query(self, text: str) -> List[float]:
-        vector = super().embed_query(text)
+        model = self.client.get_model(self.model_uid)
 
+        embedding_res = model.create_embedding(text)
+
+        embedding = embedding_res["data"][0]["embedding"]
+
+        vector = list(map(float, embedding))
         normalized_vector = (vector / np.linalg.norm(vector)).tolist()
 
         return normalized_vector