# import
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.document_loaders import PyPDFLoader

from .embeddings import EMBEDDING_MODEL_NAME
from .vectorstore import get_vectorstore


def load_data():
    docs = parse_data()
    embedding_function = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)
    vectorstore = get_vectorstore(embedding_function)

    assert isinstance(vectorstore, Chroma)
    vectorstore.from_documents(
        docs, embedding_function, persist_directory="./chroma_db"
    )
    return vectorstore


def parse_data():
    loader = PyPDFLoader("data/daoism/tao-te-ching.pdf")
    pages = loader.load_and_split()

    # split it into chunks
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=0)
    docs = text_splitter.split_documents(pages)
    print(docs)
    for doc in docs:
        doc.metadata["name"] = parse_name(doc.metadata["source"])
        doc.metadata["domain"] = parse_domain(doc.metadata["source"])
        doc.metadata["page_number"] = doc.metadata["page"]
        doc.metadata["short_name"] = doc.metadata["name"]
    return docs


def parse_name(source: str) -> str:
    return source.split("/")[-1].split(".")[0]


def parse_domain(source: str) -> str:
    return source.split("/")[2]


if __name__ == "__main__":
    db = load_data()
    # query it
    query = (
        "He who can bear the misfortune of a nation is called the ruler of the world."
    )
    docs = db.similarity_search(query)
    print(docs)