"""Proposition embedding: dense (bge-m3 via gateway) + optional BM25 sparse (client-side)."""
from __future__ import annotations

import logging

log = logging.getLogger(__name__)


def dense_embed(sc, texts: list[str]) -> list[list[float]]:
    """Dense bge-m3 (1024-d) via the gateway /v1/embeddings (§4.3)."""
    resp = sc.embed(texts)
    data = sorted(resp["data"], key=lambda d: d.get("index", 0))
    return [d["embedding"] for d in data]


class SparseEmbedder:
    """BM25 sparse vectors via FastEmbed `Qdrant/bm25` (the operator's CRM uses this exact model,
    with the collection's `modifier: idf`). Degrades gracefully to dense-only if fastembed is absent."""

    def __init__(self, model_name: str = "Qdrant/bm25") -> None:
        self.available = False
        self._model = None
        try:
            from fastembed import SparseTextEmbedding
            self._model = SparseTextEmbedding(model_name=model_name)
            self.available = True
        except Exception as e:  # noqa: BLE001
            log.warning("fastembed sparse unavailable (%s) — upserting dense-only; add sparse later", e)

    def embed(self, texts: list[str]) -> list[dict | None]:
        if not self.available or self._model is None:
            return [None] * len(texts)
        out: list[dict | None] = []
        for emb in self._model.embed(texts):
            out.append({"indices": emb.indices.tolist(), "values": emb.values.tolist()})
        return out