Package Methods (0.2.1)

Summary of entries of Methods for langchain-google-firestore.

langchain_google_firestore.chat_message_history.FirestoreChatMessageHistory.add_message

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

langchain_google_firestore.chat_message_history.FirestoreChatMessageHistory.clear

clear() -> None

langchain_google_firestore.document_loader.FirestoreLoader.lazy_load

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

langchain_google_firestore.document_loader.FirestoreLoader.load

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

langchain_google_firestore.document_loader.FirestoreSaver

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

Document Saver for Google Cloud Firestore.

See more: langchain_google_firestore.document_loader.FirestoreSaver

langchain_google_firestore.document_loader.FirestoreSaver.delete_documents

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

Delete documents from the Firestore database.

See more: langchain_google_firestore.document_loader.FirestoreSaver.delete_documents

langchain_google_firestore.document_loader.FirestoreSaver.upsert_documents

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

Create / merge documents into the Firestore database.

See more: langchain_google_firestore.document_loader.FirestoreSaver.upsert_documents

langchain_google_firestore.vectorstores.FirestoreVectorStore

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,
)

Constructor for FirestoreVectorStore.

See more: langchain_google_firestore.vectorstores.FirestoreVectorStore

langchain_google_firestore.vectorstores.FirestoreVectorStore.add_texts

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]

Add or update texts in the vector store.

See more: langchain_google_firestore.vectorstores.FirestoreVectorStore.add_texts

langchain_google_firestore.vectorstores.FirestoreVectorStore.delete

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

Delete documents from the vector store.

See more: langchain_google_firestore.vectorstores.FirestoreVectorStore.delete

langchain_google_firestore.vectorstores.FirestoreVectorStore.from_texts

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

Create a FirestoreVectorStore instance and add texts to it.

See more: langchain_google_firestore.vectorstores.FirestoreVectorStore.from_texts

langchain_google_firestore.vectorstores.FirestoreVectorStore.max_marginal_relevance_search

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]

Run max marginal relevance search on the results of Firestore nearest neighbor search.

See more: langchain_google_firestore.vectorstores.FirestoreVectorStore.max_marginal_relevance_search

langchain_google_firestore.vectorstores.FirestoreVectorStore.max_marginal_relevance_search_by_vector

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]

Run max marginal relevance search on the results of Firestore nearest neighbor search using a vector.

See more: langchain_google_firestore.vectorstores.FirestoreVectorStore.max_marginal_relevance_search_by_vector

langchain_google_firestore.vectorstores.FirestoreVectorStore.similarity_search

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]

langchain_google_firestore.vectorstores.FirestoreVectorStore.similarity_search_by_vector

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]

Run similarity search with Firestore using a vector.

See more: langchain_google_firestore.vectorstores.FirestoreVectorStore.similarity_search_by_vector

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Last updated 2025年10月30日 UTC.