## Configure Vector Store

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

Update the indexed metadata fields configuration for a vector store.

This replaces the current set of indexed metadata fields. Only indexed fields can be used for
filtering during query, list, and count operations; non-indexed fields are still stored and
returned, but cannot be filtered on.

**Field Types:** Only STRING, NUMBER, and BOOLEAN fields can be indexed (maximum 20 fields).
OBJECT and LIST types are stored but cannot be indexed for filtering.

**Adding Fields:** New indexed fields can be added at any time. They are indexed for documents
upserted after the change; to make existing documents filterable on a new field, re-upsert them.

**Removing Fields:** Omitting a field removes it from this configuration, so it can no longer be
filtered on. The underlying index is append-only, so removal does not reclaim storage or reduce
write overhead; the field stays in the physical index until the store is recreated. Prefer
indexing only the fields you filter on.

**Note:** The `name` and `embedding_config` are immutable after creation.

### Path Parameters

- `vector_store_name: string`

  The name of the vector store

### Body Parameters

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

  Dictionary mapping metadata field names to their types. Only STRING, NUMBER, and BOOLEAN types can be indexed.

  - `"string"`

  - `"number"`

  - `"boolean"`

### Returns

- `VectorStore object { id, created_at, embedding_dimensions, 4 more }`

  Response model for vector store operations.

  - `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"`

### Example

```http
curl https://api.egp.scale.com/v5/vector-stores/$VECTOR_STORE_NAME/configure \
    -H 'Content-Type: application/json' \
    -H "x-api-key: $SGP_API_KEY" \
    -d '{
          "indexed_metadata_fields": {
            "foo": "string"
          }
        }'
```

#### 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"
  }
}
```