🔨 In Development — This section is still being developed and may change.
Create a new vector store for semantic search and RAG (Retrieval Augmented Generation).
POSThttps://api.freddy.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.
accessMode string optional · Defaults to organization
Access control level:
public- Everyone can accessorganization- All organization membersdepartment- Specific departments onlyprivate- Only creator and specified users
accessDepartments array optional
Department IDs that can access (when accessMode is department).
accessUsers array optional
User IDs that can access (when accessMode is private).
Bash
- Bash
- Python
- Python
curl -X POST "https://api.freddy.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": "Technical docs for all products",
"accessMode": "organization"
}'{
"id": "vs_abc123",
"name": "Product Documentation",
"description": "Technical docs for all products",
"organizationId": "org_abc123",
"isActive": true,
"createdAt": "2025-01-20T15:45:00Z",
"updatedAt": "2025-01-20T15:45:00Z",
"createdBy": "uid_user123",
"accessMode": "organization",
"accessDepartments": null,
"accessUsers": null,
"fileCount": 0,
"dataSize": 0
}