Veculo Documentation

Veculo is a managed graph + vector database built for AI workloads. Store vertices and edges, attach vector embeddings, and query with combined graph traversal and similarity search — all with cell-level security and per-tenant isolation.

Quickstart

Create a cluster, get an API key, and run your first query in under 5 minutes.

Core Concepts

Understand vertices, edges, vector embeddings, cell-level security, and Veculo Units.

API Reference

Complete reference for the Veculo REST API with request/response examples.

Security & ABAC

Cell-level security, visibility expressions, and per-tenant isolation.

Architecture

How Veculo works under the hood -- Accumulo, GCS, ZooKeeper, and tenant isolation.

Guides

Step-by-step guides for RAG pipelines, knowledge graphs, and hybrid queries.

Quick example

Add a vertex with a vector embedding and query for similar items — two API calls:

# Add a document with its embedding
curl -X POST https://api.veculo.com/v1/cls_abc123/vertices/embedding \
  -H "Authorization: Bearer vk_live_abc123" \
  -H "Content-Type: application/json" \
  -d '{
    "id": "doc:arxiv-2401.001",
    "label": "document",
    "properties": {
      "title": "Attention Is All You Need",
      "authors": "Vaswani et al.",
      "year": 2017
    },
    "embedding": [0.023, -0.114, 0.891, ...],
    "visibility": "public"
  }'

# Find similar documents
curl -X POST https://api.veculo.com/v1/cls_abc123/query/vector \
  -H "Authorization: Bearer vk_live_abc123" \
  -H "Content-Type: application/json" \
  -d '{
    "embedding": [0.019, -0.108, 0.875, ...],
    "top_k": 5,
    "edge_type": "cites",
    "depth": 2
  }'

Quickstart