## 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.

### Parameters

- `vectorStoreName string`

  The name of the vector store

### Returns

- `type VectorStore struct{…}`

  Response model for vector store operations.

  - `ID string`

    The unique identifier of the vector store

  - `CreatedAt Time`

    Timestamp of creation

  - `EmbeddingDimensions int64`

    Dimensionality of the embedding vectors

  - `Name string`

    The name of the vector store

  - `UpdatedAt Time`

    Timestamp of last update

  - `EmbeddingConfig EmbeddingConfigUnion`

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

    - `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.

        - `const ModelsAPIModelsAPI ModelsAPI = "models_api"`

    - `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.

        - `type EmbeddingModelName string`

          - `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 EmbeddingConfigBaseType`

        The type of the embedding configuration.

        - `const EmbeddingConfigBaseTypeBase EmbeddingConfigBaseType = "base"`

  - `IndexedMetadataFields map[string, string]`

    Dictionary mapping metadata field names to their types

    - `const VectorStoreIndexedMetadataFieldString VectorStoreIndexedMetadataField = "string"`

    - `const VectorStoreIndexedMetadataFieldNumber VectorStoreIndexedMetadataField = "number"`

    - `const VectorStoreIndexedMetadataFieldBoolean VectorStoreIndexedMetadataField = "boolean"`

### Example

```go
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)
}
```

#### Response

```json
{
  "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"
  }
}
```