"""
Lokaler Embedder fuer Memory-Texte.

Nutzt sentence-transformers (paraphrase-multilingual-MiniLM-L12-v2):
- Deutsch + Englisch
- 384-dimensionale Vektoren
- Laeuft auf CPU, ~30ms pro kurzer Text
- Modell wird beim ersten Aufruf in /data/_models gecached
"""

from __future__ import annotations

import logging
from typing import List

logger = logging.getLogger(__name__)

MODEL_NAME = "paraphrase-multilingual-MiniLM-L12-v2"
VECTOR_DIM = 384


class Embedder:
    def __init__(self, model_name: str = MODEL_NAME):
        self.model_name = model_name
        self._model = None

    def _load(self):
        if self._model is None:
            logger.info("Lade Embedding-Modell %s ...", self.model_name)
            from sentence_transformers import SentenceTransformer
            self._model = SentenceTransformer(self.model_name)
            logger.info("Embedding-Modell geladen.")

    def embed(self, text: str) -> List[float]:
        self._load()
        vec = self._model.encode(text, convert_to_numpy=True, normalize_embeddings=True)
        return vec.tolist()

    def embed_batch(self, texts: List[str]) -> List[List[float]]:
        self._load()
        vecs = self._model.encode(texts, convert_to_numpy=True, normalize_embeddings=True)
        return vecs.tolist()