Prompt
Specify a webhook-driven task for OpenClaw
Describe event inputs, validation, deduplication, and intended actions before integration work.
OpenClawDevelopment & Engineering
Dave AI
Milvus Integration Skill
Milvus distributed vector database configuration for large-scale RAG applications
instructions.md
--- name: milvus-integration-a730b605cc3e description: Milvus distributed vector database configuration for large-scale RAG applications allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:retrieval-augmented-generation, skill-area:search-indexing] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # 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--- name: milvus-integration-a730b605cc3e description: Milvus distributed vector database configuration for large-scale RAG applications allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:retrieval-augmented-generation, skill-area:search-indexing] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # 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
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.