Package Methods (0.4.0)

Version 0.4.0

• 0.5.0 (latest)
• 0.4.0
• 0.3.0
• 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

Add a Message object to the store.

See more: langchain_google_firestore.chat_message_history.FirestoreChatMessageHistory.add_message

langchain_google_firestore.chat_message_history.FirestoreChatMessageHistory.clear

clear() -> None

Remove all messages from the store.

See more: langchain_google_firestore.chat_message_history.FirestoreChatMessageHistory.clear

langchain_google_firestore.document_loader.FirestoreLoader.lazy_load

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

A lazy loader for Documents.

See more: langchain_google_firestore.document_loader.FirestoreLoader.lazy_load

langchain_google_firestore.document_loader.FirestoreLoader.load

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

Load Documents.

See more: langchain_google_firestore.document_loader.FirestoreLoader.load

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._encode_image

_encode_image(uri: str) -> str

Get base64 string from a image URI.

See more: langchain_google_firestore.vectorstores.FirestoreVectorStore._encode_image

langchain_google_firestore.vectorstores.FirestoreVectorStore.add_images

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]

Adds image embeddings to Firestore vector store.

See more: langchain_google_firestore.vectorstores.FirestoreVectorStore.add_images

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]

Run similarity search with Firestore.

See more: langchain_google_firestore.vectorstores.FirestoreVectorStore.similarity_search

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

langchain_google_firestore.vectorstores.FirestoreVectorStore.similarity_search_image

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]

Run image similarity search with Firestore.

See more: langchain_google_firestore.vectorstores.FirestoreVectorStore.similarity_search_image