max_marginal_relevance_search

max_marginal_relevance_search(
    query: str,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    filter: Optional[Dict[str, Any]] = None,
    **kwargs
) -> List[Document]

# Search with balanced relevance and diversity
results = store.max_marginal_relevance_search(
    "Python programming concepts",
    k=5,
    fetch_k=20,
    lambda_mult=0.5  # Balance relevance and diversity
)

# Maximum diversity (avoid similar documents)
diverse_results = store.max_marginal_relevance_search(
    "data structures",
    k=5,
    lambda_mult=0.0  # Maximum diversity
)

# Maximum relevance (similar to standard search)
relevant_results = store.max_marginal_relevance_search(
    "algorithms",
    k=5,
    lambda_mult=1.0  # Maximum relevance
)

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]

# 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
)