Delete auditqa/engine/vectorstore.py

auditqa/engine/vectorstore.py

@@ -1,40 +0,0 @@
-from langchain_community.embeddings import HuggingFaceEmbeddings, HuggingFaceInferenceAPIEmbeddings
-from langchain_community.vectorstores import Chroma, Qdrant
-from qdrant_client import QdrantClient
-from qdrant_client.models import Distance, VectorParams
-from dotenv import load_dotenv
-import os
-
-provider_retrieval_model = "HF"
-embeddingmodel = "sentence-transformers/all-MiniLM-l6-v2"
-load_dotenv()
-HF_Token = os.environ.get("HF_TOKEN")
-client_path = "./vectorstore/"
-collection_name = "collection"
-provider_retrieval_model = "HF"
-
-def create_vectorstore(docs):
-    
-    if provider_retrieval_model == "HF":
-      qdrantClient = QdrantClient(path=client_path, prefer_grpc=True)
-    
-      embeddings = HuggingFaceInferenceAPIEmbeddings(
-          api_key=HF_Token, model_name=embeddingmodel
-      )
-    
-      dim = 384
-    
-
-    
-    qdrantClient.create_collection(
-        collection_name=collection_name,
-        vectors_config=VectorParams(size=dim, distance=Distance.COSINE),
-    )
-    
-    vectorstore = Qdrant(
-        client=qdrantClient,
-        collection_name=collection_name,
-        embeddings=embeddings,
-    )
-    
-    vectorstore.add_documents(docs)