from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from typing import List
from langchain.schema import Document

# Extract Data From the PDF File
def load_pdf_file(data):
    loader = DirectoryLoader(data,
                            glob="*.pdf",
                            loader_cls=PyPDFLoader)
    documents = loader.load()
    return documents

# Filter to minimal documents
def filter_to_minimal_docs(docs: List[Document]) -> List[Document]:
    """
    Given a list of Document objects, return a new list of Document objects
    containing only 'source' in metadata and the 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 the Data into Text Chunks
def text_split(extracted_data):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
    text_chunks = text_splitter.split_documents(extracted_data)
    return text_chunks

# Download the Embeddings from HuggingFace
def download_hugging_face_embeddings():
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')  # this model returns 384 dimensions
    return embeddings