Package Methods (0.3.0)

MemorystoreChatMessageHistory(
    client: typing.Union[redis.client.Redis, redis.cluster.RedisCluster],
    session_id: str,
    ttl: typing.Optional[int] = None,
)

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

clear() -> None

MemorystoreDocumentLoader(
    client: typing.Union[redis.client.Redis, redis.cluster.RedisCluster],
    key_prefix: str,
    content_fields: typing.Set[str],
    metadata_fields: typing.Optional[typing.Set[str]] = None,
    batch_size: int = 100,
)

_construct_document(stored_value) -> langchain_core.documents.base.Document

_decode_if_json_parsable(s: str) -> typing.Union[str, dict]

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

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

FLATConfig(
    name: str,
    field_name: typing.Optional[str] = None,
    vector_size: int = 128,
    distance_strategy: langchain_community.vectorstores.utils.DistanceStrategy = DistanceStrategy.COSINE,
)

IndexConfig(name: str, field_name: str, type: str)

_similarity_search_by_vector_with_score_and_embeddings(
    query_embedding: typing.List[float], k: int = 4, **kwargs: typing.Any
) -> typing.List[
    typing.Tuple[langchain_core.documents.base.Document, float, typing.List[float]]
]

add_texts(
    texts: typing.Iterable[str],
    metadatas: typing.Optional[typing.List[dict]] = None,
    ids: typing.Optional[typing.List[str]] = None,
    batch_size: typing.Optional[int] = 1000,
    **kwargs: typing.Any
) -> typing.List[str]

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

drop_index(
    client: typing.Union[redis.client.Redis, redis.cluster.RedisCluster],
    index_name: str,
    index_only: bool = True,
)

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,
    client: typing.Optional[
        typing.Union[redis.client.Redis, redis.cluster.RedisCluster]
    ] = None,
    index_name: typing.Optional[str] = None,
    **kwargs: typing.Any
) -> langchain_google_memorystore_redis.vectorstore.RedisVectorStore

init_index(
    client: typing.Union[redis.client.Redis, redis.cluster.RedisCluster],
    index_config: langchain_google_memorystore_redis.vectorstore.IndexConfig,
)

max_marginal_relevance_search(
    query: str,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

similarity_search(
    query: str, k: int = 4, **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

similarity_search_by_vector(
    embedding: typing.List[float], k: int = 4, **kwargs: typing.Any
) -> typing.List[langchain_core.documents.base.Document]

similarity_search_with_score(
    query: str, k: int = 4, **kwargs: typing.Any
) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]