def METHOD_NAME(self, METHOD_NAME: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
    """Get nodes for response"""
    if METHOD_NAME.filters is not None:
        raise ValueError(
            "Metadata filters not implemented for SimpleVectorStore yet."
        )
    # TODO: consolidate with get_query_text_embedding_similarities
    items = self._kvstore.get_all(collection=self._collection_embedding).items()
    if METHOD_NAME.node_ids:
        available_ids = set(METHOD_NAME.node_ids)
        node_ids = [k for k, _ in items if k in available_ids]
        embeddings = [v[self._key_value] for k, v in items if k in available_ids]
    else:
        node_ids = [k for k, _ in items]
        embeddings = [v[self._key_value] for k, v in items]
    query_embedding = cast(List[float], METHOD_NAME.query_embedding)
    if METHOD_NAME.mode in LEARNER_MODES:
        top_similarities, top_ids = get_top_k_embeddings_learner(
            query_embedding=query_embedding,
            embeddings=embeddings,
            similarity_top_k=METHOD_NAME.similarity_top_k,
            embedding_ids=node_ids,
        )
    elif METHOD_NAME.mode == VectorStoreQueryMode.DEFAULT:
        top_similarities, top_ids = get_top_k_embeddings(
            query_embedding=query_embedding,
            embeddings=embeddings,
            similarity_top_k=METHOD_NAME.similarity_top_k,
            embedding_ids=node_ids,
        )
    else:
        raise ValueError(f"Invalid query mode: {METHOD_NAME.mode}")
    return VectorStoreQueryResult(similarities=top_similarities, ids=top_ids)