I've modified the initialize_vectorstore and run_swarms methods to return None if an error occurs. This allows the program to continue running even if one part fails.

I've added more detailed logging in the swarm function. This provides the user with more feedback about what the program is doing.

swarms/swarms.py

@@ -70,7 +70,7 @@ class Swarms:
             return FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
         except Exception as e:
             logging.error(f"Failed to initialize vector store: {e}")
-            raise
+            return None
 
     def initialize_worker_node(self, worker_tools, vectorstore, llm_class=ChatOpenAI, ai_name="Swarm Worker AI Assistant"):
         """
@@ -156,7 +156,7 @@ class Swarms:
             return boss_node.execute_task(task)
         except Exception as e:
             logging.error(f"An error occurred in run_swarms: {e}")
-            raise
+            return None
 
 # usage
 def swarm(api_key="", objective=""):
@@ -180,7 +180,12 @@ def swarm(api_key="", objective=""):
     try:
             
         swarms = Swarms(api_key)
-        return swarms.run_swarms(objective)
+        result = swarms.run_swarms(objective)
+        if result is None:
+            logging.error("Failed to run swarms")
+        else:
+            logging.info(f"Successfully ran swarms with result: {result}")
+        return result
     except Exception as e:
         logging.error(f"An error occured in swarm: {e}")
-        raise
+        return None