"""Cross-encoder reranking via BAAI/bge-reranker-v2-m3.

Dense retrieval is great for recall but ranks by a single vector dot-product. A
cross-encoder reads (query, passage) **together** and scores true relevance — the single
biggest precision lever for cross-lingual search. We only score the top ~80 candidates,
so it's cheap. ``normalize=True`` squashes scores to 0..1 (sigmoid) for nice display.
"""
from __future__ import annotations

from typing import List, Optional

from app.config import Config, get_config


class Reranker:
    def __init__(self, cfg: Optional[Config] = None):
        self.cfg = cfg or get_config()
        self._model = None

    @property
    def enabled(self) -> bool:
        return bool(self.cfg.reranker["enabled"])

    def _load(self):
        if self._model is not None:
            return self._model
        from FlagEmbedding import FlagReranker

        r = self.cfg.reranker
        self._model = FlagReranker(r["model"], use_fp16=bool(r["use_fp16"]))
        return self._model

    def scores(self, query: str, passages: List[str]) -> List[float]:
        """Relevance score (0..1) for each passage against the query."""
        if not passages:
            return []
        model = self._load()
        pairs = [[query, p] for p in passages]
        out = model.compute_score(
            pairs, batch_size=self.cfg.reranker["batch_size"], normalize=True
        )
        if isinstance(out, (int, float)):
            return [float(out)]
        return [float(x) for x in out]