from typing import List, Tuple
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

from .config import EMBEDDING_MODEL


class ExampleRetriever:
    """Ranks the per-example documents in RAGBench by similarity to the question."""  # noqa: E501

    def __init__(self):
        self.embedder = SentenceTransformer(EMBEDDING_MODEL)

    def _encode(self, texts: List[str]) -> np.ndarray:
        return self.embedder.encode(texts, show_progress_bar=False)

    def rank_docs(
        self,
        question: str,
        documents_sentences: List[List[Tuple[str, str]]],
        k: int = 4,
    ) -> List[int]:
        doc_texts = [
            " ".join(sent for _, sent in doc) for doc in documents_sentences
        ]
        q_emb = self._encode([question])
        d_emb = self._encode(doc_texts)

        sims = cosine_similarity(q_emb, d_emb)[0]
        topk_idx = np.argsort(sims)[::-1][:k]
        return topk_idx.tolist()