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API Reference
Vector Stores

Get Vector Store

vector_stores.retrieve(strvector_store_name) -> VectorStore
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.

ParametersExpand Collapse
vector_store_name: str

The name of the vector store

ReturnsExpand Collapse
class VectorStore: …

Response model for vector store operations.

id: str

The unique identifier of the vector store

created_at: datetime

Timestamp of creation

formatdate-time
embedding_dimensions: int

Dimensionality of the embedding vectors

name: str

The name of the vector store

updated_at: datetime

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:
class EmbeddingConfigModelsAPI: …
model_deployment_id: str

The ID of the deployment of the created model in the Models API V3.

type: Literal["models_api"]

The type of the embedding configuration.

class EmbeddingConfigBase: …
embedding_model: Union[EmbeddingModelName, str]

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:
Literal["sentence-transformers/all-MiniLM-L12-v2", "sentence-transformers/multi-qa-distilbert-cos-v1", "openai/text-embedding-ada-002", 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"
str
type: Optional[Literal["base"]]

The type of the embedding configuration.

indexed_metadata_fields: Optional[Dict[str, Literal["string", "number", "boolean"]]]

Dictionary mapping metadata field names to their types

One of the following:
"string"
"number"
"boolean"

Get Vector Store

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
)
vector_store = client.vector_stores.retrieve(
    "vector_store_name",
)
print(vector_store.id)
{
  "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"
  }
}