How to Set Up a Vector Database with Qdrant on a VPS

Qdrant is a high-performance vector database designed for similarity search and AI applications. Running it on your Breeze gives you full control over your embeddings infrastructure.

Prerequisites

• A Breeze with at least 4 GB RAM and 2 vCPUs
• Docker installed on your server
• Basic familiarity with REST APIs

Install Qdrant with Docker

Pull and run the official Qdrant image with persistent storage:

mkdir -p /opt/qdrant/storage
docker run -d --name qdrant \
  -p 6333:6333 -p 6334:6334 \
  -v /opt/qdrant/storage:/qdrant/storage \
  --restart unless-stopped \
  qdrant/qdrant:latest

Create a Collection

Use the REST API to create a collection with a specified vector dimension:

curl -X PUT http://localhost:6333/collections/my_docs \
  -H "Content-Type: application/json" \
  -d '{
    "vectors": {
      "size": 1536,
      "distance": "Cosine"
    }
  }'

Insert Vectors

Upload points with their vector embeddings and optional payload metadata:

curl -X PUT http://localhost:6333/collections/my_docs/points \
  -H "Content-Type: application/json" \
  -d '{
    "points": [
      {"id": 1, "vector": [0.1, 0.2, ...], "payload": {"title": "Doc 1"}}
    ]
  }'

Security Recommendations

• Enable API key authentication by setting QDRANT__SERVICE__API_KEY
• Use a reverse proxy with TLS for external access
• Restrict port 6333 to trusted IPs with your firewall