/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
• Search with filters (must, should, must_not)
• Scroll for pagination
• Batch operations

Configuration Options

• Vector parameters (size, distance)
• Quantization (scalar, product)
• Sparse vector configuration
• Payload indexes
• Replication and sharding

Best Practices

• Use quantization for large collections
• Design payload indexes for filters
• Implement proper batch sizes
• Configure appropriate distance metrics

Dependencies

• qdrant-client
• langchain-qdrant