a5c-ai Qdrant Integration Skill
Capabilities
- Set up Qdrant (local, cloud, self-hosted)
- Create collections with configuration
- Implement advanced filtering with payloads
- Configure quantization for efficiency
- Set up sparse vectors for hybrid search
- Implement batch operations and optimization
Target Processes
- vector-database-setup
- rag-pipeline-implementation
Implementation Details
Deployment Modes
- Local Memory: For testing
- Local Disk: Persistent local storage
- Qdrant Cloud: Managed service
- Self-Hosted: Docker/Kubernetes deployment
Core Operations
- Collection management with parameters
- Point upsert with vectors and payloads
- Search with filters (must, should, must_not)
- Scroll for pagination
- Batch operations
Configuration Options
- Vector parameters (size, distance)
- Quantization (scalar, product)
- Sparse vector configuration
- Payload indexes
- Replication and sharding
Best Practices
- Use quantization for large collections
- Design payload indexes for filters
- Implement proper batch sizes
- Configure appropriate distance metrics
Dependencies
- qdrant-client
- langchain-qdrant