import logging
import os
from functools import lru_cache

from index_schemas import SparseVector

SPARSE_MODEL_NAME = "Qdrant/bm25"
FASTEMBED_CACHE_PATH = "/models/fastembed"

logger = logging.getLogger("index-service")


@lru_cache(maxsize=1)
def get_sparse_model():
    from fastembed import SparseTextEmbedding

    logger.info("Loading sparse model %s from cache %s", SPARSE_MODEL_NAME, FASTEMBED_CACHE_PATH)
    return SparseTextEmbedding(model_name=SPARSE_MODEL_NAME)


def embed_sparse_texts(texts: list[str]) -> list[SparseVector]:
    model = get_sparse_model()
    result: list[SparseVector] = []
    for item in model.embed(texts):
        result.append(
            SparseVector(
                indices=[int(i) for i in item.indices.tolist()],
                values=[float(v) for v in item.values.tolist()],
            )
        )
    return result