"""ko-sroberta embeddings for template-fit content similarity.

Model: jhgan/ko-sroberta-multitask (HuggingFace)
용도: MDX summary ↔ frame description 코사인 유사도 계산
32 frame 규모 — 벡터 DB 없이 numpy 만으로 충분
"""
import numpy as np

_model = None


def _get_model():
    global _model
    if _model is None:
        from sentence_transformers import SentenceTransformer
        _model = SentenceTransformer('jhgan/ko-sroberta-multitask')
    return _model


def embed_texts(texts):
    """Batch embed. Returns numpy (N, 768)."""
    model = _get_model()
    return model.encode(list(texts), show_progress_bar=False, convert_to_numpy=True)


def cosine(a, b):
    """Single-vector cosine similarity."""
    an = a / (np.linalg.norm(a) + 1e-12)
    bn = b / (np.linalg.norm(b) + 1e-12)
    return float(np.dot(an, bn))


def embed_map(id_to_text):
    """{id: text} → {id: vec(768,)}"""
    ids = list(id_to_text.keys())
    vecs = embed_texts([id_to_text[i] for i in ids])
    return {ids[i]: vecs[i] for i in range(len(ids))}