def METHOD_NAME(self, METHOD_NAME: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
    """Query index for top k most similar nodes.
    Args:
        query_embedding (List[float]): query embedding
        similarity_top_k (int): top k most similar nodes
    """
    if METHOD_NAME.filters is not None:
        if "where" in kwargs:
            raise ValueError(
                "Cannot specify metadata filters via both query and kwargs. "
                "Use kwargs only for chroma specific items that are "
                "not supported via the generic query interface."
            )
        where = _to_chroma_filter(METHOD_NAME.filters)
    else:
        where = kwargs.pop("where", {})
    results = self._collection.METHOD_NAME(
        query_embeddings=METHOD_NAME.query_embedding,
        n_results=METHOD_NAME.similarity_top_k,
        where=where,
        **kwargs,
    )
    logger.debug(f"> Top {len(results['documents'])} nodes:")
    nodes = []
    similarities = []
    ids = []
    for node_id, text, metadata, distance in zip(
        results["ids"][0],
        results["documents"][0],
        results["metadatas"][0],
        results["distances"][0],
    ):
        try:
            node = metadata_dict_to_node(metadata)
            node.set_content(text)
        except Exception:
            # NOTE: deprecated legacy logic for backward compatibility
            metadata, node_info, relationships = legacy_metadata_dict_to_node(
                metadata
            )
            node = TextNode(
                text=text,
                id_=node_id,
                metadata=metadata,
                start_char_idx=node_info.get("start", None),
                end_char_idx=node_info.get("end", None),
                relationships=relationships,
            )
        nodes.append(node)
        similarity_score = 1.0 - math.exp(-distance)
        similarities.append(similarity_score)
        logger.debug(
            f"> [Node {node_id}] [Similarity score: {similarity_score}] "
            f"{truncate_text(str(text), 100)}"
        )
        ids.append(node_id)
    return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)