## Get Vector

`vector_stores.vectors.retrieve(strvector_id, VectorRetrieveParams**kwargs)  -> VectorDocument`

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

Retrieve a single document by its unique ID.

Returns the document's full content, metadata, and optionally its embedding vector. Use this endpoint
for direct lookups when the exact document ID is known. For content similarity search,
use the query endpoint.

### Parameters

- `vector_store_name: str`

  The name of the vector store

- `vector_id: str`

  The ID of the vector to retrieve

- `include_vectors: Optional[bool]`

  Include embedding vectors

### Returns

- `class VectorDocument: …`

  A document returned from direct lookups (get/list operations).

  - `id: str`

    Document ID

  - `content: Optional[TextContent]`

    Text content for documents.

    - `text: str`

      Text content to be embedded

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

      Content type identifier

      - `"text"`

  - `metadata: Optional[Dict[str, object]]`

    Key-value metadata

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

    Embedding vector (if requested)

### 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_document = client.vector_stores.vectors.retrieve(
    vector_id="vector_id",
    vector_store_name="vector_store_name",
)
print(vector_document.id)
```

#### Response

```json
{
  "id": "id",
  "content": {
    "text": "text",
    "type": "text"
  },
  "metadata": {
    "foo": "bar"
  },
  "vector": [
    0
  ]
}
```