langchain/vectorstores/sklearn.py/SKLearnVectorStore/__init__
class SKLearnVectorStore:    def __init__(
        self,
        embedding: Embeddings,
        *,
        persist_path: Optional[str] = None,
        serializer: Literal["json", "bson", "parquet"] = "json",
        metric: str = "cosine",
        **kwargs: Any,
    ) -> None:
        np = guard_import("numpy")
        sklearn_neighbors = guard_import("sklearn.neighbors", pip_name="scikit-learn")

        # non-persistent properties
        self._np = np
        self._neighbors = sklearn_neighbors.NearestNeighbors(metric=metric, **kwargs)
        self._neighbors_fitted = False
        self._embedding_function = embedding
        self._persist_path = persist_path
        self._serializer: Optional[BaseSerializer] = None
        if self._persist_path is not None:
            serializer_cls = SERIALIZER_MAP[serializer]
            self._serializer = serializer_cls(persist_path=self._persist_path)

        # 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()