Get Vector Store

client.VectorStores.Get(ctx, vectorStoreName) (*VectorStore, error)

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

vectorStoreName string

The name of the vector store

ReturnsExpand Collapse

type VectorStore struct{…}

Response model for vector store operations.

ID string

The unique identifier of the vector store

CreatedAt Time

Timestamp of creation

formatdate-time

EmbeddingDimensions int64

Dimensionality of the embedding vectors

Name string

The name of the vector store

UpdatedAt Time

Timestamp of last update

formatdate-time

EmbeddingConfig EmbeddingConfigUnionOptional

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

One of the following:

type EmbeddingConfigModelsAPI struct{…}

ModelDeploymentID string

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

Type ModelsAPI

The type of the embedding configuration.

type EmbeddingConfigBase struct{…}

EmbeddingModel EmbeddingModelName

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:

type EmbeddingModelName string

One of the following:

const EmbeddingModelNameSentenceTransformersAllMiniLmL12V2 EmbeddingModelName = "sentence-transformers/all-MiniLM-L12-v2"

const EmbeddingModelNameSentenceTransformersMultiQaDistilbertCosV1 EmbeddingModelName = "sentence-transformers/multi-qa-distilbert-cos-v1"

const EmbeddingModelNameOpenAITextEmbeddingAda002 EmbeddingModelName = "openai/text-embedding-ada-002"

const EmbeddingModelNameOpenAITextEmbedding3Small EmbeddingModelName = "openai/text-embedding-3-small"

const EmbeddingModelNameOpenAITextEmbedding3Large EmbeddingModelName = "openai/text-embedding-3-large"

const EmbeddingModelNameEmbedEnglishV3_0 EmbeddingModelName = "embed-english-v3.0"

const EmbeddingModelNameEmbedEnglishLightV3_0 EmbeddingModelName = "embed-english-light-v3.0"

const EmbeddingModelNameEmbedMultilingualV3_0 EmbeddingModelName = "embed-multilingual-v3.0"

const EmbeddingModelNameGeminiTextEmbedding005 EmbeddingModelName = "gemini/text-embedding-005"

const EmbeddingModelNameGeminiTextMultilingualEmbedding002 EmbeddingModelName = "gemini/text-multilingual-embedding-002"

const EmbeddingModelNameGeminiGeminiEmbedding001 EmbeddingModelName = "gemini/gemini-embedding-001"

string

Type EmbeddingConfigBaseTypeOptional

The type of the embedding configuration.

IndexedMetadataFields map[string, string]Optional

Dictionary mapping metadata field names to their types

One of the following:

const VectorStoreIndexedMetadataFieldString VectorStoreIndexedMetadataField = "string"

const VectorStoreIndexedMetadataFieldNumber VectorStoreIndexedMetadataField = "number"

const VectorStoreIndexedMetadataFieldBoolean VectorStoreIndexedMetadataField = "boolean"

Get Vector Store

package main

import (
  "context"
  "fmt"

  "github.com/scaleapi/sgp-dev-go"
  "github.com/scaleapi/sgp-dev-go/option"
)

func main() {
  client := sgpdev.NewClient(
    option.WithAPIKey("My API Key"),
    option.WithAccountID("My Account ID"),
  )
  vectorStore, err := client.VectorStores.Get(context.TODO(), "vector_store_name")
  if err != nil {
    panic(err.Error())
  }
  fmt.Printf("%+v\n", vectorStore.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"
  }
}