## Stats

`aisearch.instances.stats(strid, InstanceStatsParams**kwargs)  -> InstanceStatsResponse`

**get** `/accounts/{account_id}/ai-search/instances/{id}/stats`

Retrieves usage statistics for AI Search instances.

### Parameters

- `account_id: str`

- `id: str`

  AI Search instance ID. Lowercase alphanumeric, hyphens, and underscores.

### Returns

- `class InstanceStatsResponse: …`

  - `completed: Optional[int]`

  - `engine: Optional[Engine]`

    Engine-specific metadata. Present only for managed (v3) instances.

    - `r2: Optional[EngineR2]`

      R2 bucket storage usage in bytes.

      - `metadata_size_bytes: int`

      - `object_count: int`

      - `payload_size_bytes: int`

    - `vectorize: Optional[EngineVectorize]`

      Vectorize index metadata (dimensions, vector count).

      - `dimensions: int`

      - `vectors_count: int`

  - `error: Optional[int]`

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

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

  - `last_activity: Optional[datetime]`

  - `outdated: Optional[int]`

  - `queued: Optional[int]`

  - `running: Optional[int]`

  - `skipped: Optional[int]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.aisearch.instances.stats(
    id="my-ai-search",
    account_id="c3dc5f0b34a14ff8e1b3ec04895e1b22",
)
print(response.completed)
```

#### Response

```json
{
  "result": {
    "completed": 0,
    "engine": {
      "r2": {
        "metadataSizeBytes": 0,
        "objectCount": 0,
        "payloadSizeBytes": 0
      },
      "vectorize": {
        "dimensions": 0,
        "vectorsCount": 0
      }
    },
    "error": 0,
    "file_embed_errors": {
      "foo": "bar"
    },
    "index_source_errors": {
      "foo": "bar"
    },
    "last_activity": "2019-12-27T18:11:19.117Z",
    "outdated": 0,
    "queued": 0,
    "running": 0,
    "skipped": 0
  },
  "success": true
}
```