qdrant-integration

# 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 1. **Local Memory**: For testing 2. **Local Disk**: Persistent local storage 3. **Qdrant Cloud**: Managed service 4. **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