import argparse
[docs]
def get_default_arguments():
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument("--path_model", type=str, required=True,
help="The path of a directory containing pre-trained model")
parser.add_argument("--collection_name", type=str, required=True,
help="Named of the vector database collection")
parser.add_argument("--location", type=str, required=True,
help="location")
return parser.parse_args()
[docs]
def main():
args = get_default_arguments()
# docker pull qdrant/qdrant
# docker run -p 6333:6333 -p 6334:6334 -v $(pwd)/qdrant_storage:/qdrant/storage:z qdrant/qdrant
# pip install qdrant-client
from dicee.knowledge_graph_embeddings import KGE
# Train a model on Countries dataset
KGE(path=args.path_model).create_vector_database(collection_name=args.collection_name,
location=args.location,
distance="cosine")
return "Completed!"
if __name__ == '__main__':
main()