Package Methods (0.5.0)

add_message(message: langchain_core.messages.base.BaseMessage) -> None

clear() -> None

lazy_load() -> typing.Iterator[langchain_core.documents.base.Document]

load() -> typing.List[langchain_core.documents.base.Document]

FirestoreSaver(collection: Optional[str] = None, client: Optional[Client] = None)

delete_documents(
    documents: typing.List[langchain_core.documents.base.Document],
    document_ids: typing.Optional[typing.List[str]] = None,
) -> None

upsert_documents(
    documents: typing.List[langchain_core.documents.base.Document],
    merge: typing.Optional[bool] = False,
    document_ids: typing.Optional[typing.List[str]] = None,
) -> None

FirestoreVectorStore(
    collection: google.cloud.firestore_v1.collection.CollectionReference | str,
    embedding_service: langchain_core.embeddings.embeddings.Embeddings,
    client: typing.Optional[google.cloud.firestore_v1.client.Client] = None,
    content_field: str = "content",
    metadata_field: str = "metadata",
    embedding_field: str = "embedding",
    distance_strategy: typing.Optional[
        google.cloud.firestore_v1.base_vector_query.DistanceMeasure
    ] = DistanceMeasure.COSINE,
    filters: typing.Optional[google.cloud.firestore_v1.base_query.BaseFilter] = None,
)

_encode_image(uri: str) -> str

add_images(
    uris: typing.Iterable[str],
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[typing.List[str]] = None,
    store_encodings: bool = False,
    **kwargs: typing.Any
) -> typing.List[str]

add_texts(
    texts: typing.Iterable[str],
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[typing.List[str]] = None,
    **kwargs: typing.Any
) -> typing.List[str]

delete(ids: typing.Optional[typing.List[str]] = None, **kwargs: typing.Any) -> None

from_texts(
    texts: typing.List[str],
    embedding: langchain_core.embeddings.embeddings.Embeddings,
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[typing.List[str]] = None,
    collection: typing.Optional[
        typing.Union[str, google.cloud.firestore_v1.collection.CollectionReference]
    ] = None,
    **kwargs: typing.Any
) -> langchain_google_firestore.vectorstores.FirestoreVectorStore

max_marginal_relevance_search(
    query: str,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    filters: typing.Optional[google.cloud.firestore_v1.base_query.BaseFilter] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

max_marginal_relevance_search_by_vector(
    embedding: typing.List[float],
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    filters: typing.Optional[google.cloud.firestore_v1.base_query.BaseFilter] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

similarity_search(
    query: str,
    k: int = 4,
    filters: typing.Optional[google.cloud.firestore_v1.base_query.BaseFilter] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

similarity_search_by_vector(
    embedding: typing.List[float],
    k: int = 4,
    filters: typing.Optional[google.cloud.firestore_v1.base_query.BaseFilter] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

similarity_search_image(
    image_uri: str,
    k: int = 4,
    filters: typing.Optional[google.cloud.firestore_v1.base_query.BaseFilter] = None,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]