## List Vector Stores

**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.

### Query Parameters

- `ending_before: optional string`

- `limit: optional number`

- `sort_by: optional string`

- `sort_order: optional SortOrder`

  - `"asc"`

  - `"desc"`

- `starting_after: optional string`

### Returns

- `has_more: boolean`

  Whether there are more items left to be fetched.

- `items: array of VectorStore`

  - `id: string`

    The unique identifier of the vector store

  - `created_at: string`

    Timestamp of creation

  - `embedding_dimensions: number`

    Dimensionality of the embedding vectors

  - `name: string`

    The name of the vector store

  - `updated_at: string`

    Timestamp of last update

  - `embedding_config: optional EmbeddingConfig`

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

    - `EmbeddingConfigModelsAPI object { model_deployment_id, type }`

      - `model_deployment_id: string`

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

      - `type: "models_api"`

        The type of the embedding configuration.

        - `"models_api"`

    - `EmbeddingConfigBase object { embedding_model, type }`

      - `embedding_model: EmbeddingModelName or string`

        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.

        - `EmbeddingModelName = "sentence-transformers/all-MiniLM-L12-v2" or "sentence-transformers/multi-qa-distilbert-cos-v1" or "openai/text-embedding-ada-002" or 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"`

        - `string`

      - `type: optional "base"`

        The type of the embedding configuration.

        - `"base"`

  - `indexed_metadata_fields: optional map["string" or "number" or "boolean"]`

    Dictionary mapping metadata field names to their types

    - `"string"`

    - `"number"`

    - `"boolean"`

- `total: number`

  The total of items that match the query. This is greater than or equal to the number of items returned.

- `limit: optional number`

  The maximum number of items to return.

- `object: optional "list"`

  - `"list"`

### Example

```http
curl https://api.egp.scale.com/v5/vector-stores \
    -H "x-api-key: $SGP_API_KEY"
```

#### 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"
}
```