"""Cosine top-k against a pre-stacked embedding matrix.

`top_k_with_matrix` mirrors `optcg_cards.search.top_k` (search.py:52-109)
but skips the per-call `_vectors_to_array` restack. The Space stacks
once at startup; doing it again on every search burns ~50 ms for no
reason.

The cards list passed here has the `embedding` key removed (it lives in
the matrix). Lookups go through the corpus index.
"""

from __future__ import annotations

from typing import Any

import numpy as np
from optcg_cards.search import SearchHit


def top_k_with_matrix(
    query_vector: np.ndarray | list[float],
    matrix: np.ndarray,
    cards: list[dict[str, Any]],
    k: int = 10,
    exclude_idx: int | None = None,
) -> list[SearchHit]:
    if matrix.shape[0] == 0 or not cards:
        return []

    query = np.asarray(query_vector, dtype=np.float32)
    if query.shape[-1] != matrix.shape[1]:
        raise ValueError(
            f"Dimension mismatch: query has {query.shape[-1]}, "
            f"matrix has {matrix.shape[1]}"
        )

    scores = matrix @ query
    order = np.argsort(-scores)

    hits: list[SearchHit] = []
    rank = 0
    for idx in order:
        idx_i = int(idx)
        if exclude_idx is not None and idx_i == exclude_idx:
            continue
        card = cards[idx_i]
        rank += 1
        hits.append(
            SearchHit(
                rank=rank,
                card_id=str(card.get("id", "")),
                name=str(card.get("name", "")),
                score=float(scores[idx_i]),
                metadata={
                    "card_type": card.get("card_type"),
                    "colors": card.get("colors"),
                    "set_code": card.get("set_code"),
                    "rarity": card.get("rarity"),
                },
            )
        )
        if rank >= k:
            break
    return hits