# vectorstore.py

import os
from langchain_community.document_loaders import PyPDFLoader
from langchain_experimental.text_splitter import SemanticChunker
from langchain_community.vectorstores import FAISS

def load_or_build_vectorstore(local_file: str, index_folder: str, embeddings):
    """
    Loads a local FAISS index if it exists; otherwise,
    builds a new index from the specified PDF file.
    """
    if os.path.exists(index_folder):
        print("Loading existing FAISS index from disk...")
        vectorstore = FAISS.load_local(index_folder, embeddings, allow_dangerous_deserialization=True)
    else:
        print("Building a new FAISS index...")
        loader = PyPDFLoader(local_file)
        documents = loader.load()

        text_splitter = SemanticChunker(
            embeddings=embeddings,
            breakpoint_threshold_type='percentile',
            breakpoint_threshold_amount=90
        )
        chunked_docs = text_splitter.split_documents(documents)
        print(f"Document split into {len(chunked_docs)} chunks.")

        vectorstore = FAISS.from_documents(chunked_docs, embeddings)
        vectorstore.save_local(index_folder)

    return vectorstore