"""Sentence-transformer embedder for the archival tier.

Lazy singleton wrapping ``SentenceTransformer``. Vectors are L2-normalized
at encode time so downstream FAISS ``IndexFlatIP`` search returns cosine
similarity directly (Step 16).
"""

from __future__ import annotations

import numpy as np
from sentence_transformers import SentenceTransformer

from src.config import settings

_MODEL: SentenceTransformer | None = None


def _get_model() -> SentenceTransformer:
    """Load the model on first call; return the cached instance thereafter.

    Cache directory is set explicitly via ``cache_folder`` (not HF_HOME) so
    the setting scopes to just this model. On HF Spaces, the configured
    path lives under the persistent data dir, surviving rebuilds.
    """
    global _MODEL
    if _MODEL is None:
        _MODEL = SentenceTransformer(
            settings.embeddings.model_name,
            cache_folder=settings.storage.embeddings_cache,
        )
    return _MODEL


def embed(text: str) -> np.ndarray:
    """Encode one string. Returns a normalized 1D float32 array of shape (dim,)."""
    model = _get_model()
    return model.encode(text, normalize_embeddings=True)


def embed_batch(texts: list[str]) -> np.ndarray:
    """Encode a list of strings. Returns a normalized 2D float32 array of shape (N, dim)."""
    model = _get_model()
    return model.encode(texts, normalize_embeddings=True)