)
    return faiss
[docs]class FAISS(VectorStore):
    """Wrapper around FAISS vector database.
    To use, you should have the ``faiss`` python package installed.
    Example:
        .. code-block:: python
            from langchain import FAISS
            faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id)
    """
[docs]    def __init__(
        self,
        embedding_function: Callable,
        index: Any,
        docstore: Docstore,
        index_to_docstore_id: Dict[int, str],
        relevance_score_fn: Optional[Callable[[float], float]] = None,
        normalize_L2: bool = False,
        distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE,
    ):
        """Initialize with necessary components."""
        self.embedding_function = embedding_function
        self.index = index
        self.docstore = docstore
        self.index_to_docstore_id = index_to_docstore_id
        self.distance_strategy = distance_strategy
        self.override_relevance_score_fn = relevance_score_fn
        self._normalize_L2 = normalize_L2
        if (
            self.distance_strategy != DistanceStrategy.EUCLIDEAN_DISTANCE
            and self._normalize_L2
        ):
            warnings.warn(
                "Normalizing L2 is not applicable for metric type: {strategy}".format(
                    strategy=self.distance_strategy
                )
            )
    @property
    def embeddings(self) -> Optional[Embeddings]:
        # TODO: Accept embeddings object directly
        return None
    def __add(
        self,
        texts: Iterable[str],
        embeddings: Iterable[List[float]],
        metadatas: Optional[List[dict]] = None,