# maximal_marginal_relevance

> **Function** in `langchain_milvus`

📖 [View in docs](https://reference.langchain.com/python/langchain-milvus/vectorstores/milvus/maximal_marginal_relevance)

Calculate maximal marginal relevance.

## Signature

```python
maximal_marginal_relevance(
    query_embedding: np.ndarray,
    embedding_list: list,
    lambda_mult: float = 0.5,
    k: int = 4,
) -> List[int]
```

## Parameters

| Name | Type | Required | Description |
|------|------|----------|-------------|
| `query_embedding` | `np.ndarray` | Yes | The query embedding. |
| `embedding_list` | `list` | Yes | The list of embeddings. |
| `lambda_mult` | `float` | No | The lambda multiplier. Defaults to 0.5. (default: `0.5`) |
| `k` | `int` | No | The number of results to return. Defaults to 4. (default: `4`) |

## Returns

`List[int]`

List[int]: The list of indices.

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[View source on GitHub](https://github.com/langchain-ai/langchain-milvus/blob/fe9d63e6dcae52e4c2e2ef4dafd5a6f7ee9b254c/libs/milvus/langchain_milvus/vectorstores/milvus.py#L103)