use. Defaults to DistanceStrategy.COSINE. If you select
DistanceStrategy.EUCLIDEAN_DISTANCE, Momento uses the squared
Euclidean distance.
ensure_index_exists (-) – Whether to ensure that the index
exists before adding documents to it. Defaults to True.
key (Additionally you can either pass in a client or an API) – 
client (-) – The Momento Vector Index client to use.
api_key (-) – The configuration to use to initialize
the Vector Index with. Defaults to None. If None, the configuration
is initialized from the environment variable MOMENTO_API_KEY.
Returns
Momento Vector Index vector store initialized from texts andembeddings.
Return type
VST
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.