)
[docs]    @classmethod
    def from_components(
        cls: Type["MatchingEngine"],
        project_id: str,
        region: str,
        gcs_bucket_name: str,
        index_id: str,
        endpoint_id: str,
        credentials_path: Optional[str] = None,
        embedding: Optional[Embeddings] = None,
    ) -> "MatchingEngine":
        """Takes the object creation out of the constructor.
        Args:
            project_id: The GCP project id.
            region: The default location making the API calls. It must have
            the same location as the GCS bucket and must be regional.
            gcs_bucket_name: The location where the vectors will be stored in
            order for the index to be created.
            index_id: The id of the created index.
            endpoint_id: The id of the created endpoint.
            credentials_path: (Optional) The path of the Google credentials on
            the local file system.
            embedding: The :class:`Embeddings` that will be used for
            embedding the texts.
        Returns:
            A configured MatchingEngine with the texts added to the index.
        """
        gcs_bucket_name = cls._validate_gcs_bucket(gcs_bucket_name)
        credentials = cls._create_credentials_from_file(credentials_path)
        index = cls._create_index_by_id(index_id, project_id, region, credentials)
        endpoint = cls._create_endpoint_by_id(
            endpoint_id, project_id, region, credentials
        )
        gcs_client = cls._get_gcs_client(credentials, project_id)
        cls._init_aiplatform(project_id, region, gcs_bucket_name, credentials)
        return cls(
            project_id=project_id,
            index=index,
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/matching_engine.html