"""
Сервис для вычисления эмбеддингов текстов
"""
from functools import lru_cache
from typing import Iterable
import numpy as np
from sentence_transformers import SentenceTransformer


class EmbeddingService:

    def __init__(self, model_name: str | None = None):
        self.model_name = model_name or "intfloat/multilingual-e5-base"
        self._model = None

    @property
    def model(self) -> SentenceTransformer:
        if self._model is None:
            self._model = SentenceTransformer(self.model_name)
        return self._model

    def embed_texts(self, texts: Iterable[str]) -> list[list[float]]:
        embeddings = self.model.encode(
            list(texts),
            batch_size=8,
            show_progress_bar=False,
            normalize_embeddings=True,
        )
        return [np.array(v, dtype=np.float32).tolist() for v in embeddings]

    def embed_query(self, text: str) -> list[float]:
        return self.embed_texts([text])[0]

    @lru_cache(maxsize=1)
    def model_version(self) -> str:
        return self.model_name