## Query Vectors

`vectorize.indexes.query(strindex_name, IndexQueryParams**kwargs)  -> IndexQueryResponse`

**post** `/accounts/{account_id}/vectorize/v2/indexes/{index_name}/query`

Finds vectors closest to a given vector in an index.

### Parameters

- `account_id: str`

  Identifier

- `index_name: str`

- `vector: Iterable[float]`

  The search vector that will be used to find the nearest neighbors.

- `filter: Optional[object]`

  A metadata filter expression used to limit nearest neighbor results.

- `return_metadata: Optional[Literal["none", "indexed", "all"]]`

  Whether to return no metadata, indexed metadata or all metadata associated with the closest vectors.

  - `"none"`

  - `"indexed"`

  - `"all"`

- `return_values: Optional[bool]`

  Whether to return the values associated with the closest vectors.

- `top_k: Optional[float]`

  The number of nearest neighbors to find.

### Returns

- `class IndexQueryResponse: …`

  - `count: Optional[int]`

    Specifies the count of vectors returned by the search

  - `matches: Optional[List[Match]]`

    Array of vectors matched by the search

    - `id: Optional[str]`

      Identifier for a Vector

    - `metadata: Optional[object]`

    - `namespace: Optional[str]`

    - `score: Optional[float]`

      The score of the vector according to the index's distance metric

    - `values: Optional[List[float]]`

### 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.vectorize.indexes.query(
    index_name="example-index",
    account_id="023e105f4ecef8ad9ca31a8372d0c353",
    vector=[0.5, 0.5, 0.5],
)
print(response.count)
```

#### Response

```json
{
  "errors": [
    {
      "code": 1000,
      "message": "message",
      "documentation_url": "documentation_url",
      "source": {
        "pointer": "pointer"
      }
    }
  ],
  "messages": [
    {
      "code": 1000,
      "message": "message",
      "documentation_url": "documentation_url",
      "source": {
        "pointer": "pointer"
      }
    }
  ],
  "result": {
    "count": 0,
    "matches": [
      {
        "id": "some-vector-id-023e105f4ecef8ad9ca31a8372d0c353",
        "metadata": {},
        "namespace": "namespace",
        "score": 0,
        "values": [
          0
        ]
      }
    ]
  },
  "success": true
}
```