# data properties
        self._embeddings: List[List[float]] = []
        self._texts: List[str] = []
        self._metadatas: List[dict] = []
        self._ids: List[str] = []
        # cache properties
        self._embeddings_np: Any = np.asarray([])
        if self._persist_path is not None and os.path.isfile(self._persist_path):
            self._load()
[docs]    def persist(self) -> None:
        if self._serializer is None:
            raise SKLearnVectorStoreException(
                "You must specify a persist_path on creation to persist the "
                "collection."
            )
        data = {
            "ids": self._ids,
            "texts": self._texts,
            "metadatas": self._metadatas,
            "embeddings": self._embeddings,
        }
        self._serializer.save(data)
    def _load(self) -> None:
        if self._serializer is None:
            raise SKLearnVectorStoreException(
                "You must specify a persist_path on creation to load the " "collection."
            )
        data = self._serializer.load()
        self._embeddings = data["embeddings"]
        self._texts = data["texts"]
        self._metadatas = data["metadatas"]
        self._ids = data["ids"]
        self._update_neighbors()
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[str]:
        _texts = list(texts)
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/sklearn.html