from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS

def process_documents(docs):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    text_chunks = text_splitter.split_documents(docs)
    # print(*text_chunks, sep='\n\n')

    embeddings = HuggingFaceEmbeddings(model_name="LazarusNLP/congen-indo-e5-small", 
                                       model_kwargs={'device': 'cpu'})
    vector_store = FAISS.from_documents(text_chunks, embedding=embeddings)
    
    return vector_store