Returns documents selected using maximal marginal relevance with an embedding vector. 
max_marginal_relevance_search_by_vector(
    embedding: Union[List[float], np.ndarray],
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    filter: Optional[Dict[str, Any]] = None,
    **kwargs
) -> List[Document]

​
Parameters

ParameterTypeDescription
embeddingUnion[List[float], np.ndarray]Query embedding vector
kintNumber of documents to return (default: 4)
fetch_kintNumber of documents to fetch before reranking (default: 20)
lambda_multfloatDiversity control: 0 = max diversity, 1 = min diversity (default: 0.5)
filterOptional[Dict[str, Any]](Optional) Metadata filters to apply
**kwargsAnyAdditional keyword arguments (currently unused)

​
Returns

List[Document]: List of diverse, relevant Document objects

​
Example Usage

# Get query embedding
query_embedding = store.get_embeddings("machine learning algorithms")

# MMR search with embedding
results = store.max_marginal_relevance_search_by_vector(
    query_embedding,
    k=5,
    fetch_k=30,
    lambda_mult=0.3  # Favor diversity
)

# With metadata filter
filtered_results = store.max_marginal_relevance_search_by_vector(
    query_embedding,
    k=4,
    filter={"category": "tutorial"},
    lambda_mult=0.7  # Favor relevance
)