among selected documents.
Examples:
>>> # Search using an embedding
>>> data = vector_store.max_marginal_relevance_search(
…        query = <query_to_search>,
…        embedding_function = <embedding_function_for_query>,
…        k = <number_of_items_to_return>,
…        exec_option = <preferred_exec_option>,
… )
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents for MMR algorithm.
lambda_mult – Value between 0 and 1. 0 corresponds
to maximum diversity and 1 to minimum.
Defaults to 0.5.
exec_option (str) – Supports 3 ways to perform searching.
- “python” - Pure-python implementation running on the client.
Can be used for data stored anywhere. WARNING: using this
option with big datasets is discouraged due to potential
memory issues.
”compute_engine” - Performant C++ implementation of the DeepLake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It cannot be
used with in-memory or local datasets.
”tensor_db” - Performant, fully-hosted Managed Tensor Database.Responsible for storage and query execution. Only available
for data stored in the Deep Lake Managed Database. To store
datasets in this database, specify
runtime = {“db_engine”: True} during dataset creation.
**kwargs – Additional keyword arguments
Returns
List of Documents selected by maximal marginal relevance.
Raises
ValueError – when MRR search is on but embedding function is
    not specified.