Lint error fixed

dicee/scripts/index_serve.py

@@ -11,8 +11,9 @@
 from qdrant_client.http.models import Distance, VectorParams
 from qdrant_client.http.models import PointStruct
 
+from fastapi import FastAPI
+import uvicorn
 
-# from qdrant_client.http.models import Filter, FieldCondition, MatchValue
 
 def get_default_arguments():
     parser = argparse.ArgumentParser(add_help=False)
@@ -69,45 +70,33 @@ def index(args):
     print("Completed!")
 
 
-import argparse
-from fastapi import FastAPI
-import uvicorn
-from qdrant_client import QdrantClient
-from qdrant_client.http.models import Filter, FieldCondition, MatchValue
-import pandas as pd
-from typing import List
-
 app = FastAPI()
 # Create a neural searcher instance
 neural_searcher = None
 
 class NeuralSearcher:
     def __init__(self, args):
         self.collection_name = args.collection
-        self.collection_name = args.collection
+        assert os.path.exists(args.path + "/entity_to_idx.csv"), f"{args.path + '/entity_to_idx.csv'} does not exist!"
         self.entity_to_idx = pd.read_csv(args.path + "/entity_to_idx.csv", index_col=0)
         assert self.entity_to_idx.index.is_monotonic_increasing, "Entity Index must be monotonically increasing!{}"
         self.entity_to_idx = {name: idx for idx, name in enumerate(self.entity_to_idx["entity"].tolist())}
         # initialize Qdrant client
         self.qdrant_client = QdrantClient(host=args.vdb_host,port=args.vdb_port)
+        # semantic search
+        self.topk=5
 
-    def get(self,entity:str|List[str]=None):
+    def get(self,entity:str=None):
         if entity is None:
             return {"Input {entity} cannot be None"}
         elif self.entity_to_idx.get(entity,None) is None:
             return {f"Input {entity} not found"}
         else:
-            if isinstance(entity,str):
-                ids=[self.entity_to_idx[entity]]
-            else:
-                if isinstance(entity,list):
-                    ids = [self.entity_to_idx[ent] for ent in entity]
-                else:
-                    return {"Error":f"Input must be a string or a list of strings!Given:{entity} with type {type(entity)}"}
-            return self.qdrant_client.retrieve(collection_name=self.collection_name,ids=ids,with_vectors=True)
+            ids=[self.entity_to_idx[entity]]
+            return self.qdrant_client.retrieve(collection_name=self.collection_name,ids=ids, with_vectors=True)
 
     def search(self, entity: str):
-        return self.qdrant_client.query_points(collection_name=self.collection_name,query=self.entity_to_idx[entity],limit=5)
+        return self.qdrant_client.query_points(collection_name=self.collection_name, query=self.entity_to_idx[entity],limit=self.topk)
 
 @app.get("/")
 async def root():