app/embedding.py

from functools import lru_cache

from sentence_transformers import SentenceTransformer

from app.config import get_settings


@lru_cache
def get_model() -> SentenceTransformer:
    return SentenceTransformer(get_settings().model_name)


def embed_text(text: str) -> list[float]:
    vector = get_model().encode(text, normalize_embeddings=True)
    return vector.tolist()


def embed_texts(texts: list[str]) -> list[list[float]]:
    vectors = get_model().encode(texts, normalize_embeddings=True)
    return vectors.tolist()