from typing import List
import numpy as np
from text2vec import SentenceModel
import config.config

MODEL_PATH = config.config.MODEL_PATH
_EMBEDDING_MODEL = None


def get_embedding_model():
    global _EMBEDDING_MODEL
    if _EMBEDDING_MODEL is None:
        _EMBEDDING_MODEL = SentenceModel(MODEL_PATH, device="cpu")
    return _EMBEDDING_MODEL


def compute_embedding(text: str) -> List[float]:
    model = get_embedding_model()
    vector = model.encode(text or "")
    if hasattr(vector, "tolist"):
        return vector.tolist()
    return list(vector)


def cosine_similarity(vec_a: List[float], vec_b: List[float]) -> float:
    if not vec_a or not vec_b:
        return 0.0
    a = np.array(vec_a, dtype=float)
    b = np.array(vec_b, dtype=float)
    denom = np.linalg.norm(a) * np.linalg.norm(b)
    if denom == 0:
        return 0.0
    return float(np.dot(a, b) / denom)