Package Methods (0.4.0)

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 = True, **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]