"""Embedding-Wrapper für lokale Modelle."""

import hashlib
import logging
from pathlib import Path
from typing import List

logger = logging.getLogger(__name__)


class LocalEmbedder:
    """Wrapper um sentence-transformers mit Caching."""

    def __init__(self, model_name: str = "all-MiniLM-L6-v2", cache_dir: Path = None):
        self.model_name = model_name
        self.cache_dir = cache_dir or Path.home() / ".cache" / "hermes_memory" / "embeddings"
        self.cache_dir.mkdir(parents=True, exist_ok=True)
        self._model = None
        self._dim = 384

    def _load_model(self):
        if self._model is None:
            try:
                from sentence_transformers import SentenceTransformer
                self._model = SentenceTransformer(self.model_name)
                self._dim = self._model.get_sentence_embedding_dimension()
            except ImportError:
                logger.error("sentence-transformers nicht installiert.")
                raise
        return self._model

    def _cache_key(self, text: str) -> str:
        return hashlib.sha256(text.encode("utf-8")).hexdigest() + ".npy"

    def embed(self, texts: List[str]) -> List[List[float]]:
        model = self._load_model()
        return model.encode(texts, convert_to_numpy=True).tolist()

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

    @property
    def dim(self) -> int:
        return self._dim