from pathlib import Path

import numpy as np
from sentence_transformers import SentenceTransformer
from sentence_transformers.util.quantization import quantize_embeddings


model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
corpus = [
    "Int8 embeddings use one byte per dimension.",
    "Binary embeddings use one bit per dimension.",
    "Cross-encoders rerank retrieved passages.",
    "Vector databases store embeddings for search.",
]
float_embeddings = model.encode(
    corpus,
    normalize_embeddings=True,
    show_progress_bar=False,
)