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by Qubinode

Overview

Provides an AI-enhanced interface for infrastructure automation, hypervisor deployment, and management, offering interactive guidance, RAG-powered documentation search, and system diagnostics via the Model Context Protocol (MCP).

Installation

Run Command
python3 ai-assistant/mcp_server_fastmcp.py

Environment Variables

  • MCP_SERVER_ENABLED
  • MCP_SERVER_PORT
  • MCP_SERVER_HOST
  • AI_SERVICE_URL
  • MCP_API_KEY
  • AI_MODEL_TYPE
  • HUGGINGFACE_TOKEN
  • AI_USE_GPU
  • AI_GPU_LAYERS
  • AI_THREADS
  • AI_CONTEXT_LENGTH
  • AI_TEMPERATURE
  • AI_MAX_TOKENS
  • RAG_DATA_DIR
  • OPENAI_API_KEY
  • ANTHROPIC_API_KEY
  • AZURE_API_KEY
  • AZURE_API_BASE
  • AZURE_API_VERSION

Security Notes

The system runs with root privileges and performs extensive system modifications, an inherent risk for infrastructure automation. The use of 'sshpass' in certain CI/CD contexts (`rhel8-linux-hypervisor.sh`) presents a direct security risk for automated credential handling. Airflow containers are configured to run as root (`user: "0:0"`), which is not a recommended security practice. Sudoers rules may grant NOPASSWD access to created users. Input validation is present for exposed API endpoints, and sensitive keys are handled via environment variables and vault integration.

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Stats

Interest Score30
Security Score6
Cost ClassLow
Avg Tokens1000
Stars1
Forks1
Last Update2025-12-06

Tags

AIRAGInfrastructure AutomationLLMDeploymentTroubleshootingQubinodeFastAPIPythonContainer-FirstMonitoringAnsibleKVM