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POST
/
v1
/
projects
/
{project_id}
/
vector-stores
Create a new vector store
curl --request POST \
  --url https://api.example.com/v1/projects/{project_id}/vector-stores \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --header 'x-selected-account-id: <api-key>' \
  --data '
{
  "name": "<string>",
  "embedding_model": "<string>",
  "engine": "sgp_knowledge_base",
  "embedding_type": "base"
}
'
{
  "id": "<string>",
  "project_id": "<string>",
  "name": "<string>",
  "engine": "sgp_knowledge_base",
  "created_at": "2023-11-07T05:31:56Z"
}

Authorizations

x-api-key
string
header
required

API key for authentication

x-selected-account-id
string
header
required

Selected Account ID

Path Parameters

project_id
string
required

Body

application/json

Base embedding configuration using standard models.

name
string
required

Name of the vector store

embedding_model
string
required

Embedding model to use for 'base' type. e.g. openai/text-embedding-3-large

engine
string
default:sgp_knowledge_base
Allowed value: "sgp_knowledge_base"
embedding_type
string
default:base

Type of embedding configuration for standard models

Allowed value: "base"

Response

Vector store created successfully

id
string
required

ID of the entity

project_id
string
required

ID of the project

name
string
required

Name of the vector store

engine
enum<string>
required

Engine used for vector store

Available options:
sgp_knowledge_base
created_at
string<date-time>
required

When the vector store was created