## Get Vector Store

`vector_stores.retrieve(strvector_store_name)  -> VectorStore`

**get** `/v5/vector-stores/{vector_store_name}`

Retrieve detailed configuration and metadata for a specific vector store.

Returns the store's embedding model, dimensions, indexed metadata field definitions,
creation timestamp, and last update timestamp. Use this to verify store settings before
performing operations or to display store information in your application.

### Parameters

- `vector_store_name: str`

  The name of the vector store

### 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
)
vector_store = client.vector_stores.retrieve(
    "vector_store_name",
)
print(vector_store.id)
```

#### Response

```json
{
  "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"
  }
}
```