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mcp-server-qdrant-enhanced

Verified Safe

by triepod-ai

Overview

Enhanced Model Context Protocol server for Qdrant vector database providing GPU-accelerated, multi-vector, and collection-specific embedding models for intelligent document storage and retrieval.

Installation

Run Command
docker run -it --rm --gpus all --network host -e QDRANT_URL="http://localhost:6333" -e COLLECTION_NAME="enhanced-collection" -e FASTEMBED_CUDA="true" ghcr.io/triepod-ai/mcp-server-qdrant-enhanced:latest

Environment Variables

  • QDRANT_URL
  • COLLECTION_NAME
  • FASTEMBED_CUDA
  • QDRANT_API_KEY
  • EMBEDDING_MODEL
  • QDRANT_AUTO_CREATE_COLLECTIONS
  • QDRANT_ENABLE_QUANTIZATION
  • QDRANT_HNSW_EF_CONSTRUCT
  • QDRANT_HNSW_M
  • COLLECTION_MODEL_MAPPINGS
  • CUSTOM_MODEL_CONFIGS
  • CUDA_VISIBLE_DEVICES

Security Notes

The server employs `TransportSecuritySettings` to configure `allowed_hosts` and `allowed_origins` for its HTTP transport, which is a good practice against DNS rebinding and CORS attacks. Sensitive credentials like `QDRANT_API_KEY` are handled via environment variables. Extensive use of `subprocess.run` with `shell=True` (e.g., `bash -c`) is present in development and testing scripts for system orchestration; while common in this context, it always warrants caution if inputs were untrusted, but here they appear internally controlled. The HTTP server binds to `0.0.0.0` by default (standard for Docker), meaning it's exposed on all network interfaces, requiring external firewall/reverse proxy considerations if deployed publicly.

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Stats

Interest Score20
Security Score8
Cost ClassMedium
Avg Tokens5000
Stars1
Forks0
Last Update2026-01-01

Tags

mcpqdrantvector-databaseembeddingaigpu-accelerationfastembedsemantic-searchproduction-readymulti-vector-supportcuda