def get_default_sparse_query_encoder(
        self, collection_name: str, fastembed_sparse_model: Optional[str] = None
    ) -> SparseEncoderCallable:
        """
        Get the default sparse query encoder.
        Use old format for backward compatibility if detected.
        """
        if self.use_old_sparse_encoder(collection_name):
            # Update the sparse vector name to use the old format
            self.sparse_vector_name = DEFAULT_SPARSE_VECTOR_NAME_OLD
            return default_sparse_encoder("naver/efficient-splade-VI-BT-large-query")

        if fastembed_sparse_model is not None:
            return fastembed_sparse_encoder(model_name=fastembed_sparse_model)

        return fastembed_sparse_encoder()

        Get the default sparse query encoder.
        Use old format for backward compatibility if detected.
        
llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/llama_index/vector_stores/qdrant/base.py