Qdrant is a high-performance vector similarity search engine and database. It's designed for machine learning applications requiring efficient vector search capabilities, including:
- Semantic search
- Recommendation systems
- Neural network applications
- Image/video similarity search
- Embeddings storage and retrieval
Once deployed, you'll receive a URI in the format:
- HTTP API:
http://<random-subdomain>.provider.akash.network - gRPC:
<random-subdomain>.provider.akash.network:6334
curl http://<your-uri>/Access the Qdrant dashboard at:
http://<your-uri>/dashboard
Edit the profiles.compute.qdrant.resources section:
resources:
cpu:
units: 4 # Increase for better performance
memory:
size: 8Gi # Increase for larger datasets
storage:
- size: 10Gi
- name: data
size: 100Gi # Increase for more vector data