from sentence_transformers import SentenceTransformer
import faiss
from pathlib import Path

BASE = Path(__file__).resolve().parent
INDEX = BASE / "rag" / "vector.index"

index = faiss.read_index(str(INDEX))
print("FAISS dim:", index.d)

model = SentenceTransformer("intfloat/multilingual-e5-large-instruct")
emb = model.encode(["test"], convert_to_numpy=True)
print("Embedder dim:", emb.shape[1])
