## List Vector Stores

`vector_stores.list(VectorStoreListParams**kwargs)  -> SyncCursorPageByName[VectorStore]`

**get** `/v5/vector-stores`

List all vector stores in your account with pagination.

Returns vector stores sorted by creation date (newest first). Each store includes its configuration,
embedding model, dimensions, indexed fields, and timestamps.

### Parameters

- `ending_before: Optional[str]`

- `limit: Optional[int]`

- `sort_by: Optional[str]`

- `sort_order: Optional[SortOrder]`

  - `"asc"`

  - `"desc"`

- `starting_after: Optional[str]`

### Returns

- `class VectorStore: …`

  Response model for vector store operations.

  - `id: str`

    The unique identifier of the vector store

  - `created_at: datetime`

    Timestamp of creation

  - `embedding_dimensions: int`

    Dimensionality of the embedding vectors

  - `name: str`

    The name of the vector store

  - `updated_at: datetime`

    Timestamp of last update

  - `embedding_config: Optional[EmbeddingConfig]`

    Embedding configuration identifying the model and its type. None for raw-embedding-only stores.

    - `class EmbeddingConfigModelsAPI: …`

      - `model_deployment_id: str`

        The ID of the deployment of the created model in the Models API V3.

      - `type: Literal["models_api"]`

        The type of the embedding configuration.

        - `"models_api"`

    - `class EmbeddingConfigBase: …`

      - `embedding_model: Union[EmbeddingModelName, str]`

        The name of the base embedding model to use. Either a known base model (EmbeddingModelName) or, in ray-serve deployments with NATIVE_OPENAI_EMBEDDING_GATEWAY enabled, any model id served by the OpenAI-compatible inference proxy (e.g. 'nomic-embed-text-v1.5'). For fully custom deployments, use type 'models_api' with a model_deployment_id.

        - `Literal["sentence-transformers/all-MiniLM-L12-v2", "sentence-transformers/multi-qa-distilbert-cos-v1", "openai/text-embedding-ada-002", 8 more]`

          - `"sentence-transformers/all-MiniLM-L12-v2"`

          - `"sentence-transformers/multi-qa-distilbert-cos-v1"`

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

          - `"openai/text-embedding-3-small"`

          - `"openai/text-embedding-3-large"`

          - `"embed-english-v3.0"`

          - `"embed-english-light-v3.0"`

          - `"embed-multilingual-v3.0"`

          - `"gemini/text-embedding-005"`

          - `"gemini/text-multilingual-embedding-002"`

          - `"gemini/gemini-embedding-001"`

        - `str`

      - `type: Optional[Literal["base"]]`

        The type of the embedding configuration.

        - `"base"`

  - `indexed_metadata_fields: Optional[Dict[str, Literal["string", "number", "boolean"]]]`

    Dictionary mapping metadata field names to their types

    - `"string"`

    - `"number"`

    - `"boolean"`

### 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
)
page = client.vector_stores.list()
page = page.items[0]
print(page.id)
```

#### Response

```json
{
  "has_more": true,
  "items": [
    {
      "id": "id",
      "created_at": "2019-12-27T18:11:19.117Z",
      "embedding_dimensions": 0,
      "name": "name",
      "updated_at": "2019-12-27T18:11:19.117Z",
      "embedding_config": {
        "model_deployment_id": "model_deployment_id",
        "type": "models_api"
      },
      "indexed_metadata_fields": {
        "foo": "string"
      }
    }
  ],
  "total": 0,
  "limit": 0,
  "object": "list"
}
```