milvus-integration ยท diff
git:20260507.44b06c0 to git:20260601.da7723a
70 added, 70 removed. Audit A to A.
- ---
- name: milvus-integration
- 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
+ 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