refactor: Service initiation vectorization task

backend/common/utils/embedding_threads.py

@@ -1,33 +1,43 @@
 from concurrent.futures import ThreadPoolExecutor
 from typing import List
 
-from sqlalchemy import create_engine
-from sqlalchemy.orm import sessionmaker
-
-from common.core.config import settings
+from sqlalchemy.orm import sessionmaker, scoped_session
 
 executor = ThreadPoolExecutor(max_workers=200)
 
-engine = create_engine(str(settings.SQLALCHEMY_DATABASE_URI))
-session_maker = sessionmaker(bind=engine)
-session = session_maker()
+from common.core.db import engine
+
+session_maker = scoped_session(sessionmaker(bind=engine))
+
+
+# session = session_maker()
 
 
 def run_save_terminology_embeddings(ids: List[int]):
     from apps.terminology.curd.terminology import save_embeddings
-    executor.submit(save_embeddings, session, ids)
+    executor.submit(save_embeddings, session_maker, ids)
 
 
 def fill_empty_terminology_embeddings():
     from apps.terminology.curd.terminology import run_fill_empty_embeddings
-    executor.submit(run_fill_empty_embeddings, session)
+    executor.submit(run_fill_empty_embeddings, session_maker)
 
 
 def run_save_data_training_embeddings(ids: List[int]):
     from apps.data_training.curd.data_training import save_embeddings
-    executor.submit(save_embeddings, session, ids)
+    executor.submit(save_embeddings, session_maker, ids)
 
 
 def fill_empty_data_training_embeddings():
     from apps.data_training.curd.data_training import run_fill_empty_embeddings
-    executor.submit(run_fill_empty_embeddings, session)
+    executor.submit(run_fill_empty_embeddings, session_maker)
+
+
+def run_save_table_embeddings(ids: List[int]):
+    from apps.datasource.crud.table import save_table_embedding
+    executor.submit(save_table_embedding, session_maker, ids)
+
+
+def fill_empty_table_embeddings():
+    from apps.datasource.crud.table import run_fill_empty_table_embedding
+    executor.submit(run_fill_empty_table_embedding, session_maker)