import os
from typing import List
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.schema import Document

class RAGEngine:
    def __init__(self, data_dir: str = "data", db_dir: str = "chroma_db"):
        self.data_dir = data_dir
        self.db_dir = db_dir
        self.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
        self.vector_store = None
        self._initialize_vector_store()

    def _initialize_vector_store(self):
        if not os.path.exists(self.db_dir):
            os.makedirs(self.db_dir)
            self._process_documents()
        else:
            self.vector_store = Chroma(
                persist_directory=self.db_dir,
                embedding_function=self.embeddings
            )

    def _process_documents(self):
        documents = []
        for file in os.listdir(self.data_dir):
            if file.endswith(".pdf"):
                file_path = os.path.join(self.data_dir, file)
                loader = PyPDFLoader(file_path)
                documents.extend(loader.load())

        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
        chunks = text_splitter.split_documents(documents)

        self.vector_store = Chroma.from_documents(
            documents=chunks,
            embedding=self.embeddings,
            persist_directory=self.db_dir
        )
        self.vector_store.persist()

    def query(self, text: str, k: int = 3) -> List[Document]:
        if not self.vector_store:
            return []
        return self.vector_store.similarity_search(text, k=k)

rag_engine = RAGEngine()