import os
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_community.document_loaders import UnstructuredFileLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Qdrant

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


loader = DirectoryLoader('data/', glob="**/*.pdf", show_progress=True, loader_cls=UnstructuredFileLoader)
documents = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=700, chunk_overlap=70)
texts = text_splitter.split_documents(documents)

# url = "http://127.0.0.1:6333" # This is the same URL that must match Step 4d
qdrant_api_key = 'ic_WPSW7zUEOYzJIbHAYKVUxTf7xVXxFfJgTN6UsnvcuXGwkRPGx3g'
qdrant_url = 'https://ea51a65a-6fad-48ce-b571-846d3b496882.us-east4-0.gcp.cloud.qdrant.io'


qdrant = Qdrant.from_documents(
    texts,
    embeddings,
    url=qdrant_url,
    port=6333,
    api_key=qdrant_api_key, 
    prefer_grpc=False,
    collection_name="vector_db"
)

print("Vector DB Successfully Created!")