"""
Script used to create the FAISS vector store of the glossary using Mistral embeddings
"""

import os
import tqdm
import time
import pandas as pd
import warnings
from langchain_core.documents import Document
from langchain_community.vectorstores import FAISS
from langchain_mistralai.embeddings import MistralAIEmbeddings
from langchain_community.docstore.in_memory import InMemoryDocstore

# Suppress the tokenizer warning
warnings.filterwarnings("ignore", message="Could not download mistral tokenizer from Huggingface")

MISTRAL_API_KEY = os.environ.get("MISTRAL_API_KEY")

def load_glossary():
    df = pd.read_csv('glossary-terms.csv') # NOTE not adding this explicitly to public repo for security
    df.drop(columns=["Category", "Notes"], inplace=True)
    return df

def create_vector_index() -> None:
    df = load_glossary()
    documents = []

    for i in tqdm.tqdm(range(len(df)), desc="Creating documents"):
        doc = Document(
            page_content=f"Name: {df.iloc[i]['Name']}\nDescription: {df.iloc[i]['Description']}",
            metadata={"name": df.iloc[i]['Name'], "description": df.iloc[i]['Description']}
        )
        documents.append(doc)

    start_time = time.time()
    print(f"Starting FAISS vector store creation...")

    vector_store = FAISS.from_documents(
        documents=documents, 
        embedding=MistralAIEmbeddings(model="mistral-embed", mistral_api_key=MISTRAL_API_KEY), 
        docstore= InMemoryDocstore(),
        index_to_docstore_id={}
    )

    end_time = time.time()
    print(f"FAISS vector store created successfully in {end_time - start_time:.2f} seconds.")

    # Save the vector store
    vector_store.save_local("faiss_index")

if __name__ == "__main__":
    create_vector_index()