## Create Vectorize Index

`vectorize.indexes.create(IndexCreateParams**kwargs)  -> CreateIndex`

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

Creates and returns a new Vectorize Index.

### Parameters

- `account_id: str`

  Identifier

- `config: Config`

  Specifies the type of configuration to use for the index.

  - `class IndexDimensionConfiguration: …`

    - `dimensions: int`

      Specifies the number of dimensions for the index

    - `metric: Literal["cosine", "euclidean", "dot-product"]`

      Specifies the type of metric to use calculating distance.

      - `"cosine"`

      - `"euclidean"`

      - `"dot-product"`

  - `class ConfigVectorizeIndexPresetConfiguration: …`

    - `preset: Literal["@cf/baai/bge-small-en-v1.5", "@cf/baai/bge-base-en-v1.5", "@cf/baai/bge-large-en-v1.5", 2 more]`

      Specifies the preset to use for the index.

      - `"@cf/baai/bge-small-en-v1.5"`

      - `"@cf/baai/bge-base-en-v1.5"`

      - `"@cf/baai/bge-large-en-v1.5"`

      - `"openai/text-embedding-ada-002"`

      - `"cohere/embed-multilingual-v2.0"`

- `name: str`

- `description: Optional[str]`

  Specifies the description of the index.

### Returns

- `class CreateIndex: …`

  - `config: Optional[IndexDimensionConfiguration]`

    - `dimensions: int`

      Specifies the number of dimensions for the index

    - `metric: Literal["cosine", "euclidean", "dot-product"]`

      Specifies the type of metric to use calculating distance.

      - `"cosine"`

      - `"euclidean"`

      - `"dot-product"`

  - `created_on: Optional[datetime]`

    Specifies the timestamp the resource was created as an ISO8601 string.

  - `description: Optional[str]`

    Specifies the description of the index.

  - `modified_on: Optional[datetime]`

    Specifies the timestamp the resource was modified as an ISO8601 string.

  - `name: Optional[str]`

### 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
)
create_index = client.vectorize.indexes.create(
    account_id="023e105f4ecef8ad9ca31a8372d0c353",
    config={
        "dimensions": 768,
        "metric": "cosine",
    },
    name="example-index",
)
print(create_index.config)
```

#### 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": {
    "config": {
      "dimensions": 768,
      "metric": "cosine"
    },
    "created_on": "2022-11-15T18:25:44.442097Z",
    "description": "This is my example index.",
    "modified_on": "2022-11-15T18:25:44.442097Z",
    "name": "example-index"
  },
  "success": true
}
```