## Count Vectors

`vector_stores.count(strvector_store_name, VectorStoreCountParams**kwargs)  -> VectorStoreCountResponse`

**post** `/v5/vector-stores/{vector_store_name}/count`

Count documents in a vector store, optionally filtered by metadata.

**Use Cases:**

- Monitor vector store size and growth over time
- Preview the number of documents matching a filter before deletion
- Validate data ingestion by comparing expected versus actual document counts
- Analyze document distribution across metadata categories

**Filtering:** Apply the same metadata filter syntax as delete and list operations. Only indexed fields
can be used for filtering. An empty filter counts all documents in the store.

### Parameters

- `vector_store_name: str`

  The name of the vector store

- `filter: Optional[Dict[str, object]]`

  Metadata filter expression

### Returns

- `class VectorStoreCountResponse: …`

  Response for count operation.

  - `count: int`

    Number of documents matching the criteria

### Example

```python
import os
from scale_gp_beta import SGPClient

client = SGPClient(
    api_key=os.environ.get("SGP_API_KEY"),  # This is the default and can be omitted
)
response = client.vector_stores.count(
    vector_store_name="vector_store_name",
)
print(response.count)
```

#### Response

```json
{
  "count": 0
}
```