qdrant-integration

name	qdrant-integration
description	Qdrant vector database with filtering, payloads, and quantization support
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"]}

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