Skip to main content

Qdrant

Vector database for AI embeddings

Qdrant provides vector storage and similarity search for AI applications and document embeddings.

Overview​

  • Port: 6333 (HTTP), 6334 (gRPC)
  • Image: qdrant/qdrant:latest
  • Purpose: Vector embeddings and similarity search
  • Status: Enabled by default

Configuration​

Default Settings​

ENABLE_QDRANT=yes

Access​

Usage​

Services Using Qdrant​

  • Flowise: Document embeddings for RAG
  • ElizaOS: Memory and context search
  • Custom AI: Vector similarity search

Basic Operations​

# Check collections
curl http://localhost:6333/collections

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

Troubleshooting​

Connection Issues​

# Check service status
curl http://localhost:6333/health

# View logs
docker logs seiling-qdrant --tail=50

Storage Issues​

# Check collection info
curl http://localhost:6333/collections/documents

# Delete collection if needed
curl -X DELETE http://localhost:6333/collections/documents

Qdrant enables semantic search and AI memory. Essential for RAG applications.