Agent-Skills.md

Agent Skills: Milvus Integration Skill

Milvus distributed vector database configuration for large-scale RAG applications

UncategorizedID: a5c-ai/babysitter/milvus-integration

Repository

a5c-ai/babysitter
a5c-aiLicense: MIT
244

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plugins/babysitter/skills/babysit/process/specializations/ai-agents-conversational/skills/milvus-integration/SKILL.md

Skill Metadata

Name
milvus-integration
Description
Milvus distributed vector database configuration for large-scale RAG applications

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