List Vector Stores

GET/v5/vector-stores

List all vector stores in your account with pagination.

Returns vector stores sorted by creation date (newest first). Each store includes its configuration, embedding model, dimensions, indexed fields, and timestamps.

Query ParametersExpand Collapse

ending_before: optional string

limit: optional number

maximum10000

minimum1

sort_by: optional string

sort_order: optional SortOrder

One of the following:

"asc"

"desc"

starting_after: optional string

ReturnsExpand Collapse

has_more: boolean

Whether there are more items left to be fetched.

items: array of VectorStore { id, created_at, embedding_dimensions, 4 more }

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"

total: number

The total of items that match the query. This is greater than or equal to the number of items returned.

limit: optional number

The maximum number of items to return.

object: optional "list"

List Vector Stores

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

{
  "has_more": true,
  "items": [
    {
      "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"
      }
    }
  ],
  "total": 0,
  "limit": 0,
  "object": "list"
}

Returns Examples

{
  "has_more": true,
  "items": [
    {
      "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"
      }
    }
  ],
  "total": 0,
  "limit": 0,
  "object": "list"
}