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]]