Create a new vector store for semantic search and RAG (Retrieval Augmented Generation).
POSThttps://api.aitronos.com/v1/organizations/{organization_id}/vector-stores
Vector stores enable semantic search across your documents. Upload files, and the AI can search and reference them in responses.
organization_id string required
The unique identifier of the organization.
name string required
Name of the vector store.
description string optional
Description of the vector store's purpose or contents.
access_mode string optional ยท Defaults to organization
Access control level:
public- Everyone can accessorganization- All organization membersdepartment- Specific departments onlyprivate- Only creator and specified users
access_departments array optional
Department IDs that can access (when access_mode is department).
access_users array optional
User IDs that can access (when access_mode is private).
Bash
- Bash
- Python
- JavaScript
curl -X POST "https://api.aitronos.com/v1/organizations/org_abc123/vector-stores" \
-H "X-API-Key: $FREDDY_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"name": "Product Documentation",
"description": "Knowledge base for product support"
}'Response:
{
"id": "vs_abc123",
"object": "vector_store",
"name": "Product Documentation",
"description": "Knowledge base for product support",
"organization_id": "org_abc123",
"created_by": "usr_alice123",
"access_mode": "organization",
"access_departments": [],
"access_users": [],
"file_count": 0,
"total_size_bytes": 0,
"status": "active",
"created_at": "2025-11-13T10:30:00Z",
"updated_at": "2025-11-13T10:30:00Z"
}