import pandas as pd
import numpy as np
import os
from langchain_core.documents import Document
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma

def create_Doc(data):
    documents = []
    for num, i in data.iterrows():
        documents.append(Document(
            page_content=i.lyric,
            metadata={"name": i.title, "id": num}
        ))

    return documents

def load_embedding(model_name='sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'):
    embeddings = HuggingFaceEmbeddings(
        model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
    )

    return embeddings

def load_vectorstore(documents, embeddings):
    vectorstore = Chroma.from_documents(
        documents,
        embedding=embeddings,
    )

    return vectorstore

def process(list_text, vectorstore, search_type = 'mmr'):
    vectorstore.as_retriever(search_type= search_type)
    retrieves = []
    for i in list_text:
        retrieves.append(vectorstore.invoke(i))
    return retrieves