[Question]: how to use faiss.IndexIVFPQ? about llama_index HOT 1 OPEN

def _build_node_list_from_query_result(
        self, query_result: VectorStoreQueryResult
    ) -> List[NodeWithScore]:
    if query_result.nodes is None:
        if query_result.ids is None:
            raise ValueError(
                "Vector store query result should return at "
                "least one of nodes or ids."
            )
        assert isinstance(self._index.index_struct, IndexDict)
        node_ids = [
            self._index.index_struct.nodes_dict[idx] for idx in query_result.ids
        ]
        nodes = self._docstore.get_nodes(node_ids)
        query_result.nodes = nodes
    else:
        for i in range(len(query_result.nodes)):
            source_node = query_result.nodes[i].source_node
            if (not self._vector_store.stores_text) or (
                source_node is not None and source_node.node_type != ObjectType.TEXT
            ):
                node_id = query_result.nodes[i].node_id
                if self._docstore.document_exists(node_id):
                    query_result.nodes[i] = self._docstore.get_node(
                        node_id
                    )  # type: ignore[index]

    log_vector_store_query_result(query_result)

    node_with_scores: List[NodeWithScore] = []
    for ind, node in enumerate(query_result.nodes):
        score: Optional[float] = None
        if query_result.similarities is not None:
            score = query_result.similarities[ind]
        node_with_scores.append(NodeWithScore(node=node, score=score))

    return node_with_scores

node_ids = []
for idx in query_result.ids:
    if idx in self._index.index_struct.nodes_dict:
        node_ids.append(self._index.index_struct.nodes_dict[idx])
    else:
        raise KeyError(f"Key '{idx}' not found in nodes_dict")

def _add_nodes_to_index(
        self,
        index_struct: IndexDict,
        nodes: Sequence[BaseNode],
        show_progress: bool = False,
        **insert_kwargs: Any,
    ) -> None:
    if not nodes:
        return

    for nodes_batch in iter_batch(nodes, self._insert_batch_size):
        nodes_batch = self._get_node_with_embedding(nodes_batch, show_progress)
        new_ids = self._vector_store.add(nodes_batch, **insert_kwargs)

        if not self._vector_store.stores_text or self._store_nodes_override:
            for node, new_id in zip(nodes_batch, new_ids):
                node_without_embedding = node.copy()
                node_without_embedding.embedding = None

                index_struct.add_node(node_without_embedding, text_id=new_id)
                self._docstore.add_documents(
                    [node_without_embedding], allow_update=True
                )
        else:
            for node, new_id in zip(nodes_batch, new_ids):
                if isinstance(node, (ImageNode, IndexNode)):
                    node_without_embedding = node.copy()
                    node_without_embedding.embedding = None

                    index_struct.add_node(node_without_embedding, text_id=new_id)
                    self._docstore.add_documents(
                        [node_without_embedding], allow_update=True
                    )