from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
# from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_pinecone import PineconeVectorStore
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_core.documents import Document
from typing import List


# Extract text from PDF files
def load_pdf_files(data):
    loader = DirectoryLoader(
        data,
        glob="*.pdf",
        loader_cls=PyPDFLoader
    )

    documents = loader.load()
    return documents


# Keep only minimal metadata (source)
def filter_to_minimal_docs(docs: List[Document]) -> List[Document]:
    """
    Given a list of Document objects, return a new list
    containing only 'source' metadata and original page content.
    """

    minimal_docs: List[Document] = []

    for doc in docs:
        src = doc.metadata.get("source")

        minimal_docs.append(
            Document(
                page_content=doc.page_content,
                metadata={"source": src}
            )
        )

    return minimal_docs


# Split documents into smaller chunks
def text_split(minimal_docs):

    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=500,
        chunk_overlap=20,
    )

    texts_chunk = text_splitter.split_documents(minimal_docs)
    return texts_chunk


# Download HuggingFace embedding model
def download_hugging_face_embeddings():

    model_name = "sentence-transformers/all-MiniLM-L6-v2"

    embeddings = HuggingFaceEmbeddings(
        model_name=model_name
    )

    return embeddings