Lock the index before updating it via API or Scheduler

- There are 3 paths to updating/setting the index (stored in state.model)
  - App start
  - API
  - Scheduler

- Put all updates to the index behind a lock. As multiple updates path
that could (potentially) run at the same time (via API or Scheduler)

src/configure.py

@@ -34,7 +34,9 @@ def configure_server(args, required=False):
         state.config = args.config
 
     # Initialize the search model from Config
+    state.search_index_lock.acquire()
     state.model = configure_search(state.model, state.config, args.regenerate)
+    state.search_index_lock.release()
 
     # Initialize Processor from Config
     state.processor_config = configure_processor(args.config.processor)
@@ -42,7 +44,9 @@ def configure_server(args, required=False):
 
 @schedule.repeat(schedule.every(1).hour)
 def update_search_index():
+    state.search_index_lock.acquire()
     state.model = configure_search(state.model, state.config, regenerate=False)
+    state.search_index_lock.release()
     logger.info("Search Index updated via Scheduler")
 
 

src/routers/api.py

@@ -125,7 +125,9 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
 
 @api.get('/update')
 def update(t: Optional[SearchType] = None, force: Optional[bool] = False):
+    state.search_index_lock.acquire()
     state.model = configure_search(state.model, state.config, regenerate=force, t=t)
+    state.search_index_lock.release()
     logger.info("Search Index updated via API call")
 
     return {'status': 'ok', 'message': 'index updated'}

src/utils/state.py

@@ -1,4 +1,5 @@
 # Standard Packages
+import threading
 from packaging import version
 
 # External Packages
@@ -20,6 +21,7 @@ host: str = None
 port: int = None
 cli_args: list[str] = None
 query_cache = LRU()
+search_index_lock = threading.Lock()
 
 if torch.cuda.is_available():
     # Use CUDA GPU