> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cyborg.co/llms.txt
> Use this file to discover all available pages before exploring further.

# max_marginal_relevance_search

Returns documents selected using maximal marginal relevance to balance relevance and diversity.

```python theme={null}
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]
```

### Parameters

| Parameter     | Type                       | Description                                                            |
| ------------- | -------------------------- | ---------------------------------------------------------------------- |
| `query`       | `str`                      | Query text to search for                                               |
| `k`           | `int`                      | Number of documents to return (default: 4)                             |
| `fetch_k`     | `int`                      | Number of documents to fetch before reranking (default: 20)            |
| `lambda_mult` | `float`                    | Diversity control: 0 = max diversity, 1 = min diversity (default: 0.5) |
| `filter`      | `Optional[Dict[str, Any]]` | *(Optional)* Metadata filters to apply                                 |
| `**kwargs`    | `Any`                      | Additional keyword arguments (currently unused)                        |

### Returns

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

### Example Usage

```python theme={null}
# 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
)
```