import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.document_loaders import DirectoryLoader
from langchain.document_loaders import PyPDFLoader
from langchain.vectorstores import Qdrant
from qdrant_client import QdrantClient

embeddings = SentenceTransformerEmbeddings(model_name="NeuML/pubmedbert-base-embeddings")

client = QdrantClient(
    url=os.getenv("QDRANT_URL", "https://QDRANT_URL.europe-west3-0.gcp.cloud.qdrant.io"),
    api_key=os.getenv("QDRANT_API_KEY"),
    prefer_grpc=False
)

loader = DirectoryLoader('data/', glob="**/*.pdf", show_progress=True, loader_cls=PyPDFLoader)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
texts = text_splitter.split_documents(documents)

qdrant = Qdrant.from_documents(
    texts,
    embeddings,
    client=client,
    collection_name="vector_db"
)

#print("Vector DB Successfully Created!")