import os
from langchain.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.docstore.document import Document
from langchain.chains import RetrievalQA
from langchain_community.llms import HuggingFaceHub
from langchain.embeddings.base import Embeddings

# Set safe caching directories to avoid permission denied errors
os.environ["TRANSFORMERS_CACHE"] = "/app/cache"
os.environ["HF_HOME"] = "/app/cache"
os.makedirs("/app/cache", exist_ok=True)

# Constants
DATA_PATH = "/app/data"
VECTORSTORE_PATH = "/app/vectorstore"
DOCS_FILENAME = "context.txt"
EMBEDDING_MODEL_NAME = "sentence-transformers/paraphrase-MiniLM-L6-v2"


def load_embedding_model() -> Embeddings:
    """Initialize and return the HuggingFace embedding model."""
    return HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)


def load_documents() -> list[Document]:
    """Load and split documents into chunks."""
    loader = TextLoader(os.path.join(DATA_PATH, DOCS_FILENAME))
    raw_docs = loader.load()
    splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    docs = splitter.split_documents(raw_docs)
    return docs


def load_vectorstore() -> FAISS:
    """Load or create FAISS vectorstore from documents."""
    vectorstore_file = os.path.join(VECTORSTORE_PATH, "faiss_index")

    embedding_model = load_embedding_model()

    if os.path.exists(vectorstore_file):
        return FAISS.load_local(vectorstore_file, embedding_model, allow_dangerous_deserialization=True)

    docs = load_documents()
    vectorstore = FAISS.from_documents(docs, embedding_model)
    vectorstore.save_local(vectorstore_file)
    return vectorstore


def ask_question(query: str) -> str:
    """Query the vectorstore and return the answer using the language model."""
    vectorstore = load_vectorstore()

    llm = HuggingFaceHub(
        repo_id="mistralai/Mistral-7B-Instruct-v0.1",
        model_kwargs={"temperature": 0.5, "max_tokens": 256},
    )

    qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever())
    return qa.run(query)