grepai-storage-qdrant

This skill covers using Qdrant as the storage backend for GrepAI, offering high-performance vector search. Qdrant is a purpose-built vector database offering: Ports: Set the environment variable: Access the web dashboard at : Qdrant loads vectors into memory for fast search: Create config/production.yaml : Mount in Docker: GrepAI creates a collection named grepai with: For very large deployments, Qdrant supports distributed mode: ❌ Problem: Connection refused ✅ Solution: Ensure Qdrant is running: ❌ Problem: gRPC connection failed ✅ Solution: Check port 6334 is exposed: ❌ Problem: Authentication failed ✅ Solution: Check API key: ❌ Problem: Out of memory ✅ Solutions: ❌ Problem: Slow initial indexing ✅ Solution: This is normal; Qdrant optimizes in background. Searches will be fast after indexing completes. Recommendation: Use Qdrant for large codebases or maximum performance. Use PostgreSQL if you need SQL integration or team is familiar with it. Qdrant storage status: - Need fastest possible search performance - Very large codebases (50K+ files) - Already using Qdrant infrastructure - Want advanced vector search features - ⚡ Extremely fast vector similarity search - 📏 Excellent scalability - 🔧 Advanced filtering capabilities - 🐳 Easy Docker deployment - Qdrant server running - Network access to Qdrant - 6333 : REST API - 6334 : gRPC API (used by GrepAI) - Sign up at...