backend/semantic_search.py

from qdrant_haystack import QdrantDocumentStore
from haystack.nodes import EmbeddingRetriever
from pathlib import Path

proj_dir = Path(__file__).parents[1]
qd_document_store = QdrantDocumentStore(path=str(proj_dir/'Qdrant'), index='RAGDemo')
qd_retriever = EmbeddingRetriever(document_store=qd_document_store,
                                  embedding_model="BAAI/bge-base-en-v1.5",
                                  model_format="sentence_transformers",
                                  use_gpu=True)