Grepai Storage Qdrant

This configures Qdrant as your vector database backend for GrepAI instead of the default GOB or PostgreSQL storage. You'd want this for large codebases over 50K files where search speed matters, since Qdrant keeps vectors in memory and delivers sub-50ms searches even on hundreds of thousands of files. Setup is straightforward with Docker on port 6334, though you'll need to re-index when migrating from other backends. The tradeoff is higher memory usage (about 6GB per million vectors) and needing to run another service, but if you're already dealing with scale issues or have Qdrant infrastructure, the performance jump is significant.

skills.sh installs: 570
GitHub stars: 20
last updated: Aug 21, 2026

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Install

$ npx -y skills add yoanbernabeu/grepai-skills --skill grepai-storage-qdrant --agent claude-code

Installs into .claude/skills of the current project.

Files

GrepAI Storage with Qdrant

This skill covers using Qdrant as the storage backend for GrepAI, offering high-performance vector search.

When to Use This Skill

Need fastest possible search performance
Very large codebases (50K+ files)
Already using Qdrant infrastructure
Want advanced vector search features

What is Qdrant?

Qdrant is a purpose-built vector database offering:

⚡ Extremely fast vector similarity search
📏 Excellent scalability
🔧 Advanced filtering capabilities
🐳 Easy Docker deployment

Prerequisites

Qdrant server running
Network access to Qdrant

Advantages

Benefit	Description
⚡ Performance	Fastest vector search
📏 Scalability	Handles millions of vectors
🔍 Advanced	Filtering, payloads, sharding
🐳 Easy deploy	Docker-ready
☁️ Cloud option	Qdrant Cloud available

Setting Up Qdrant

Option 1: Docker (Recommended)

# Run Qdrant with persistent storage
docker run -d \
  --name grepai-qdrant \
  -p 6333:6333 \
  -p 6334:6334 \
  -v qdrant_storage:/qdrant/storage \
  qdrant/qdrant

SKILL.md on GitHub