## Upsert Vectors

`vector_stores.upsert(strvector_store_name, VectorStoreUpsertParams**kwargs)  -> VectorStoreUpsertResponse`

**post** `/v5/vector-stores/{vector_store_name}/upsert`

Insert new documents or update existing documents in a vector store.

**Upsert Behavior:** If a document ID already exists, it will be completely replaced with the new content
and metadata. The previous document's text, embedding, and all metadata fields are discarded. If the ID
does not exist, a new document is created.

**Document Content:** Each document supports several modes:

- `content` only: text is automatically embedded using the store's configured model.
- `embedding` only: pre-computed embedding vector is used directly. Dimension must match the store's configuration.
- Both `content` and `embedding`: the pre-computed embedding is stored and text is kept for retrieval/search.
- Neither (metadata-only): only metadata is updated on an existing document without re-embedding.
  If the document does not exist, it will appear as a failure in the batch response.

A store created without an embedding model (dimensions-only) only accepts documents with pre-computed `embedding`.

**Batch Operations:** This endpoint supports batch operations with partial success handling and mixed
document types (some with raw embeddings, some with content) in the same call.

**Metadata:** Supports nested metadata with string, number, boolean, object, and array types. Null values
are not permitted—omit the field or use an empty string instead.

### Parameters

- `vector_store_name: str`

  The name of the vector store

- `vectors: Iterable[Vector]`

  Array of documents to upsert

  - `id: str`

    Unique document ID

  - `content: Optional[TextContentParam]`

    Text content for documents.

    - `text: str`

      Text content to be embedded

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

      Content type identifier

      - `"text"`

  - `embedding: Optional[Iterable[float]]`

    Pre-computed embedding vector

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

    Key-value metadata

### Returns

- `class VectorStoreUpsertResponse: …`

  Response for batch insert/upsert operations.

  - `failure_count: int`

    Number of failed documents

  - `success_count: int`

    Number of successfully processed documents

  - `failed: Optional[List[Failed]]`

    Failed documents with their error messages

    - `id: str`

      Document ID

    - `error: str`

      Error message describing why the document failed

  - `succeeded: Optional[List[str]]`

    IDs of successfully processed documents

### 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
)
response = client.vector_stores.upsert(
    vector_store_name="vector_store_name",
    vectors=[{
        "id": "id"
    }],
)
print(response.failure_count)
```

#### Response

```json
{
  "failure_count": 0,
  "success_count": 0,
  "failed": [
    {
      "id": "id",
      "error": "error"
    }
  ],
  "succeeded": [
    "string"
  ]
}
```