> ## 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.

# similarity_search_by_vector

Returns documents most similar to an embedding vector.

```python theme={null}
similarity_search_by_vector(
    embedding: Union[List[float], np.ndarray],
    k: int = 4,
    filter: Optional[Dict[str, Any]] = None,
    **kwargs
) -> List[Document]
```

### Parameters

| Parameter   | Type                             | Description                                     |
| ----------- | -------------------------------- | ----------------------------------------------- |
| `embedding` | `Union[List[float], np.ndarray]` | Embedding vector to search with                 |
| `k`         | `int`                            | Number of documents to return (default: 4)      |
| `filter`    | `Optional[Dict[str, Any]]`       | *(Optional)* Metadata filters to apply          |
| `**kwargs`  | `Any`                            | Additional keyword arguments (currently unused) |

### Returns

`List[Document]`: List of most similar Document objects

### Example Usage

```python theme={null}
# Get embedding for a query
query_embedding = store.get_embeddings("data science concepts")

# Search using the embedding
results = store.similarity_search_by_vector(query_embedding, k=5)

# Search with custom embedding
custom_embedding = np.random.rand(384)  # Example 384-dim embedding
results = store.similarity_search_by_vector(custom_embedding, k=3)
```