milvus-integration

# Milvus Integration Skill ## Capabilities - Set up Milvus (Lite, Standalone, Cluster) - Design collection schemas with dynamic fields - Configure index types (IVF, HNSW, etc.) - Implement partition strategies - Set up GPU acceleration - Handle large-scale data operations ## Target Processes - vector-database-setup - rag-pipeline-implementation ## Implementation Details ### Deployment Modes 1. **Milvus Lite**: Embedded for development 2. **Standalone**: Single-node deployment 3. **Cluster**: Distributed deployment with K8s ### Core Operations - Collection and schema management - Index creation and configuration - Insert/delete/query operations - Partition management - Bulk import ### Configuration Options - Index type selection (IVF_FLAT, IVF_SQ8, HNSW) - Metric type (L2, IP, COSINE) - Index parameters (nlist, nprobe, M, efConstruction) - Partition key configuration - Resource group assignment ### Best Practices - Choose index type based on scale - Use partitions for data isolation - Configure proper nprobe for recall - Monitor query latency and throughput ### Dependencies - pymilvus - langchain-milvus