Get Vector Store

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.

Path ParametersExpand Collapse

vector_store_name: string

The name of the vector store

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"

Get Vector Store

curl https://api.egp.scale.com/v5/vector-stores/$VECTOR_STORE_NAME \
    -H "x-api-key: $SGP_API_KEY"

{
  "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"
  }
}