> ## 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_by_vector

Returns documents selected using maximal marginal relevance with an embedding vector.

```python theme={null}
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

| Parameter     | Type                             | Description                                                            |
| ------------- | -------------------------------- | ---------------------------------------------------------------------- |
| `embedding`   | `Union[List[float], np.ndarray]` | Query embedding vector                                                 |
| `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}
# 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
)
```