import faiss
import pickle
from typing import List, Tuple
from pathlib import Path
from sentence_transformers import SentenceTransformer

DATA_DIR = Path("data")
INDEX_PATH = DATA_DIR / "vector_store.faiss"
META_PATH = DATA_DIR / "vector_store_meta.pkl"

_model = None

def _embedder():
    global _model
    if _model is None:
        _model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
    return _model

def retrieve_contexts(query: str, k: int=5) -> List[str]:
    if not INDEX_PATH.exists():
        return []
    index = faiss.read_index(str(INDEX_PATH))
    with open(META_PATH, "rb") as f:
        meta = pickle.load(f)
    vec = _embedder().encode([query], normalize_embeddings=True)
    D, I = index.search(vec, k)
    contexts = []
    for idx in I[0]:
        if idx == -1: 
            continue
        contexts.append(meta[idx]["text"])
    return contexts