Qdrant Vector Database Connection Guide
Connection Details
- Host: localhost
- Port: 17000
- Protocol: HTTP
- Health Check:
curl http://localhost:17000/collections
Authentication
- No authentication required (localhost binding only)
- All operations are local to the server
Python Client Example
from qdrant_client import QdrantClient
client = QdrantClient(host="localhost", port=17000)
# Check health
collections = client.get_collections()
print(f"Available collections: {collections}")
# Create collection (if needed)
client.create_collection(
collection_name="nova_memory",
vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
)
REST API Examples
# List collections
curl http://localhost:17000/collections
# Get collection info
curl http://localhost:17000/collections/nova_memory
# Search vectors
curl -X POST http://localhost:17000/collections/nova_memory/points/search \
-H "Content-Type: application/json" \
-d '{"vector": [0.1, 0.2, ...], "limit": 10}'
Configuration Notes
- Data directory:
/data/qdrant/storage/ - Max memory: 50GB (configurable)
- No external network exposure
- Backup location:
/data/adaptai/backups/qdrant/
Security
- ❗ Localhost binding only
- ❗ No authentication mechanism
- ❗ Regular backups recommended
- ❗ Monitor disk usage on /data partition
Last Updated: September 4, 2025