feat(model): Support deploy rerank model (#1522)

dbgpt/model/cluster/embedding/remote_embedding.py

@@ -1,6 +1,6 @@
 from typing import List
 
-from dbgpt.core import Embeddings
+from dbgpt.core import Embeddings, RerankEmbeddings
 from dbgpt.model.cluster.manager_base import WorkerManager
 
 
@@ -26,3 +26,30 @@ class RemoteEmbeddings(Embeddings):
     async def aembed_query(self, text: str) -> List[float]:
         """Asynchronous Embed query text."""
         return await self.aembed_documents([text])[0]
+
+
+class RemoteRerankEmbeddings(RerankEmbeddings):
+    def __init__(self, model_name: str, worker_manager: WorkerManager) -> None:
+        self.model_name = model_name
+        self.worker_manager = worker_manager
+
+    def predict(self, query: str, candidates: List[str]) -> List[float]:
+        """Predict the scores of the candidates."""
+        params = {
+            "model": self.model_name,
+            "input": candidates,
+            "query": query,
+        }
+        return self.worker_manager.sync_embeddings(params)[0]
+
+    async def apredict(self, query: str, candidates: List[str]) -> List[float]:
+        """Asynchronously predict the scores of the candidates."""
+        params = {
+            "model": self.model_name,
+            "input": candidates,
+            "query": query,
+        }
+        # Use embeddings interface to get scores of ranker
+        scores = await self.worker_manager.embeddings(params)
+        # The first element is the scores of the query
+        return scores[0]