Create Vector Store

Create a new vector store for storing and querying document embeddings.

The vector store name must be unique within your account and follow naming conventions (3-63 characters, alphanumeric with hyphens/underscores). Once created, the embedding configuration and dimensions are immutable and cannot be changed. To use a different model, you must create a new vector store.

Embedding Configuration: Provide embedding_config (for base or custom model deployments), embedding_model (shorthand for a base model), or dimensions only (raw embeddings).

• With embedding_config or embedding_model: dimensions are auto-derived, and documents can be upserted with text content (auto-embedded) or with pre-computed embeddings.
• With dimensions only: the store accepts only pre-computed embeddings. Semantic/hybrid queries are not supported (lexical search only).

Indexed Fields: Optionally specify metadata fields to index at creation time. Only indexed fields can be used for filtering — indexing is required, not just a performance optimization. Additional indexed fields can be added later using the configure endpoint, but cannot be removed once added. Keep in mind that each indexed field increases write latency and storage overhead, so only index fields you actively filter on.

POST /v5/vector-stores/create

Create Vector Store

cURL

curl --request POST \
  --url https://api.egp.scale.com/v5/vector-stores/create \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '
{
  "name": "<string>",
  "embedding_config": {
    "type": "<string>",
    "model_deployment_id": "<string>"
  },
  "dimensions": 123,
  "indexed_metadata_fields": {}
}
'

200

{
  "name": "<string>",
  "embedding_dimensions": 123,
  "created_at": "2023-11-07T05:31:56Z",
  "updated_at": "2023-11-07T05:31:56Z",
  "embedding_config": {
    "type": "<string>",
    "model_deployment_id": "<string>"
  },
  "indexed_metadata_fields": {}
}

Authorizations

x-api-key · string · header · required

Headers

x-selected-account-id · string | null

Body

application/json

Request to create a vector store.

name · string · required

A unique name for the vector store within the account

embedding_config · EmbeddingConfigModelsAPI · object

The embedding configuration. Either 'base' type with an embedding_model, or 'models_api' type with a model_deployment_id for custom models.

EmbeddingConfigModelsAPI

EmbeddingConfigBase

Show child attributes

embedding_model · enum<string>

The base embedding model to use. Shorthand for embedding_config with type 'base'. Provide either embedding_config or embedding_model, not both.

Available options: sentence-transformers/all-MiniLM-L12-v2, sentence-transformers/all-mpnet-base-v2, sentence-transformers/multi-qa-distilbert-cos-v1, sentence-transformers/paraphrase-multilingual-mpnet-base-v2, 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

dimensions · integer

Dimension size of embedding vectors. Required when neither 'embedding_config' nor 'embedding_model' is set. Automatically derived when an embedding model is provided.

indexed_metadata_fields · Indexed Metadata Fields · object

Dictionary mapping metadata field names to their types for efficient filtering. Only STRING, NUMBER, and BOOLEAN types can be indexed.

Show child attributes

Response

200

application/json

Successful Response

Response model for vector store operations.

name · string · required

The name of the vector store

embedding_dimensions · integer · required

Dimensionality of the embedding vectors

created_at · string<date-time> · required

Timestamp of creation

updated_at · string<date-time> · required

Timestamp of last update

embedding_config · EmbeddingConfigModelsAPI · object

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

EmbeddingConfigModelsAPI

EmbeddingConfigBase

Show child attributes

indexed_metadata_fields · Indexed Metadata Fields · object

Dictionary mapping metadata field names to their types

Show child attributes