# embedder_nomic.py
from transformers import AutoTokenizer, AutoModel
import torch

class NomicEmbedder:
    _instance = None

    def __init__(self, model_path: str = ".model/nomic-embed-text-v1.5"):
        # Load model only once
        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
        self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True)

    @classmethod
    def get_instance(cls) -> "NomicEmbedder":
        if cls._instance is None:
            cls._instance = NomicEmbedder()
        return cls._instance

    def embed(self, text: str) -> list[float]:
        """Generate embedding from text"""
        inputs = self.tokenizer(
            [text], return_tensors="pt", padding=True, truncation=True, max_length=8192
        )
        with torch.no_grad():
            embedding = self.model(**inputs).last_hidden_state.mean(dim=1).squeeze()
        return embedding.tolist()