fixed vectorRM requiring embedding model in the example.

examples/run_storm_wiki_gpt_with_VectorRM.py

@@ -95,6 +95,7 @@ def main(args):
  'batch_size': args.embed_batch_size,
  'vector_db_mode': args.vector_db_mode,
  'collection_name': args.collection_name,
+ 'embedding_model': args.embedding_model,
  'device': args.device,
  }
  if args.vector_db_mode == 'offline':
@@ -110,7 +111,7 @@ def main(args):
  )
 
  # Setup VectorRM to retrieve information from your own data
- rm = VectorRM(collection_name=args.collection_name, device=args.device, k=engine_args.search_top_k)
+ rm = VectorRM(collection_name=args.collection_name, embedding_model=args.embedding_model, device=args.device, k=engine_args.search_top_k)
 
  # initialize the vector store, either online (store the db on Qdrant server) or offline (store the db locally):
  if args.vector_db_mode == 'offline':
@@ -146,6 +147,8 @@ def main(args):
  # provide local corpus and set up vector db
  parser.add_argument('--collection-name', type=str, default="my_documents",
  help='The collection name for vector store.')
+ parser.add_argument('--embedding_model', type=str, default="BAAI/bge-m3",
+ help='The collection name for vector store.')
  parser.add_argument('--device', type=str, default="mps",
  help='The device used to run the retrieval model (mps, cuda, cpu, etc).')
  parser.add_argument('--vector-db-mode', type=str, choices=['offline', 'online'],