# Qdrant Integration

> Qdrant vector database with filtering, payloads, and quantization support

- Skill: `a5c-ai/qdrant-integration` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add a5c-ai/qdrant-integration`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/qdrant-integration/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Integrations & APIs
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/qdrant-integration

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# 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