Package Methods (0.2.0)

Summary of entries of Methods for langchain-google-memorystore-redis.

langchain_google_memorystore_redis.chat_message_history.MemorystoreChatMessageHistory

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

Initializes the chat message history for Memorystore for Redis.

See more: langchain_google_memorystore_redis.chat_message_history.MemorystoreChatMessageHistory

langchain_google_memorystore_redis.chat_message_history.MemorystoreChatMessageHistory.add_message

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

Append one message to this session.

See more: langchain_google_memorystore_redis.chat_message_history.MemorystoreChatMessageHistory.add_message

langchain_google_memorystore_redis.chat_message_history.MemorystoreChatMessageHistory.clear

clear() -> None

Clear all messages in this session.

See more: langchain_google_memorystore_redis.chat_message_history.MemorystoreChatMessageHistory.clear

langchain_google_memorystore_redis.loader.MemorystoreDocumentLoader

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

Initializes the Document Loader for Memorystore for Redis.

See more: langchain_google_memorystore_redis.loader.MemorystoreDocumentLoader

langchain_google_memorystore_redis.loader.MemorystoreDocumentLoader._construct_document

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

Construct a Document from stored value.

See more: langchain_google_memorystore_redis.loader.MemorystoreDocumentLoader._construct_document

langchain_google_memorystore_redis.loader.MemorystoreDocumentLoader._decode_if_json_parsable

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

Decode a JSON string to a dict if it is JSON.

See more: langchain_google_memorystore_redis.loader.MemorystoreDocumentLoader._decode_if_json_parsable

langchain_google_memorystore_redis.loader.MemorystoreDocumentLoader.lazy_load

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

Lazy load the Documents and yield them one by one.

See more: langchain_google_memorystore_redis.loader.MemorystoreDocumentLoader.lazy_load

langchain_google_memorystore_redis.loader.MemorystoreDocumentLoader.load

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

Load all Documents using a Redis pipeline for efficiency.

See more: langchain_google_memorystore_redis.loader.MemorystoreDocumentLoader.load

langchain_google_memorystore_redis.vectorstore.FLATConfig

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

Initializes the FLATConfig object.

See more: langchain_google_memorystore_redis.vectorstore.FLATConfig

langchain_google_memorystore_redis.vectorstore.IndexConfig

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

Initializes the IndexConfig object.

See more: langchain_google_memorystore_redis.vectorstore.IndexConfig

langchain_google_memorystore_redis.vectorstore.RedisVectorStore._similarity_search_by_vector_with_score_and_embeddings

_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]]
]

Performs a similarity search by a vector with score and embeddings, offering various customization options via keyword arguments.

See more: langchain_google_memorystore_redis.vectorstore.RedisVectorStore._similarity_search_by_vector_with_score_and_embeddings

langchain_google_memorystore_redis.vectorstore.RedisVectorStore.add_texts

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]

Adds a collection of texts along with their metadata to a vector store, generating unique keys for each entry if not provided.

See more: langchain_google_memorystore_redis.vectorstore.RedisVectorStore.add_texts

langchain_google_memorystore_redis.vectorstore.RedisVectorStore.delete

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

Delete by vector ID or other criteria.

See more: langchain_google_memorystore_redis.vectorstore.RedisVectorStore.delete

langchain_google_memorystore_redis.vectorstore.RedisVectorStore.drop_index

drop_index(client: redis.client.Redis, index_name: str, index_only: bool = True)

Drops an index from the Redis database.

See more: langchain_google_memorystore_redis.vectorstore.RedisVectorStore.drop_index

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

Creates an instance of RedisVectorStore from provided texts.

See more: langchain_google_memorystore_redis.vectorstore.RedisVectorStore.from_texts

langchain_google_memorystore_redis.vectorstore.RedisVectorStore.init_index

init_index(
    client: redis.client.Redis,
    index_config: langchain_google_memorystore_redis.vectorstore.IndexConfig,
)

Initializes a named VectorStore index in Redis with specified configurations.

See more: langchain_google_memorystore_redis.vectorstore.RedisVectorStore.init_index

langchain_google_memorystore_redis.vectorstore.RedisVectorStore.max_marginal_relevance_search

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]

Performs a search to find documents that are both relevant to the query and diverse among each other based on Maximal Marginal Relevance (MMR).

See more: langchain_google_memorystore_redis.vectorstore.RedisVectorStore.max_marginal_relevance_search

langchain_google_memorystore_redis.vectorstore.RedisVectorStore.similarity_search

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

Conducts a similarity search based on the specified query, returning a list of the top 'k' documents that are most similar to the query.

See more: langchain_google_memorystore_redis.vectorstore.RedisVectorStore.similarity_search

langchain_google_memorystore_redis.vectorstore.RedisVectorStore.similarity_search_by_vector

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

Performs a similarity search for the given embedding and returns the top k most similar Document objects, discarding their similarity scores.

See more: langchain_google_memorystore_redis.vectorstore.RedisVectorStore.similarity_search_by_vector

langchain_google_memorystore_redis.vectorstore.RedisVectorStore.similarity_search_with_score

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

Performs a similarity search using the given query, returning documents and their similarity scores.

See more: langchain_google_memorystore_redis.vectorstore.RedisVectorStore.similarity_search_with_score