List Vector Stores

client.VectorStores.List(ctx, query) (*CursorPageByName[VectorStore], error)

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.

ParametersExpand Collapse

query VectorStoreListParams

EndingBefore param.Field[string]Optional

Limit param.Field[int64]Optional

maximum10000

minimum1

SortBy param.Field[string]Optional

SortOrder param.Field[SortOrder]Optional

StartingAfter param.Field[string]Optional

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"

List Vector Stores

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"),
  )
  page, err := client.VectorStores.List(context.TODO(), sgpdev.VectorStoreListParams{

  })
  if err != nil {
    panic(err.Error())
  }
  fmt.Printf("%+v\n", page)
}

{
  "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"
}