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 ParametersExpand Collapse

vector_store_name: string

The name of the vector store

Body ParametersJSONExpand Collapse

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.

One of the following:

"string"

"number"

"boolean"

ReturnsExpand Collapse

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

formatdate-time

embedding_dimensions: number

Dimensionality of the embedding vectors

name: string

The name of the vector store

updated_at: string

Timestamp of last update

formatdate-time

embedding_config: optional EmbeddingConfig

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

One of the following:

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.

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.

One of the following:

EmbeddingModelName = "sentence-transformers/all-MiniLM-L12-v2" or "sentence-transformers/multi-qa-distilbert-cos-v1" or "openai/text-embedding-ada-002" or 8 more

One of the following:

"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.

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

Dictionary mapping metadata field names to their types

One of the following:

"string"

"number"

"boolean"

Configure Vector Store

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"
          }
        }'

{
  "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"
  }
}

Returns Examples

{
  "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"
  }
}