from sentence_transformers import SentenceTransformer
import numpy as np

_MODELS = {}

class EmbeddingService:
    def __init__(self, model_name="paraphrase-multilingual-MiniLM-L12-v2"):
        if model_name not in _MODELS:
            _MODELS[model_name] = SentenceTransformer(model_name)
        self.model = _MODELS[model_name]
        self.dimension = 384

    def embed(self, texts: list[str]) -> np.ndarray:
        """Generate embeddings for a list of texts."""
        return self.model.encode(texts, convert_to_numpy=True)

    def embed_query(self, text: str) -> np.ndarray:
        """Generate embedding for a single query."""
        return self.model.encode([text], convert_to_numpy=True)[0]

    def get_dimension(self) -> int:
        """Return embedding dimension."""
        return self.dimension