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Prerequisites

Prepare the target server before enabling Easy8 AI.

Base requirements

  • Linux server with Docker Engine installed
  • Docker Compose plugin available as docker compose
  • persistent Docker storage for service data
  • Nginx or another reverse proxy in front of Easy8
  • enough CPU, RAM, and disk space for the selected product tier

Network requirements

The server must allow:

  • browser access to Easy8 through HTTPS
  • reverse proxy access to the chain service on the host
  • internal communication between app, chain, celery, redis, and optionally chroma

If cloud AI is used, the server must also allow outbound access to the configured provider:

  • OpenAI
  • Azure OpenAI

If self-hosted AI is used, the server must allow outbound or internal access to:

  • the local LLM endpoint configured through LLM_URL
  • the local embeddings endpoint configured through EMBEDDINGS_URL when Pro products are used

Product-specific runtime requirements

Easy8 AI Basic

Required services:

  • chain
  • celery
  • LLM provider

Not required:

  • chroma
  • embeddings service

Easy8 AI Knowledge assistant PRO / Easy8 AI Helpdesk

Required services:

  • chain
  • celery
  • chroma
  • embeddings provider
  • LLM provider

Self-hosted AI requirements

If the customer hosts LLMs or embeddings locally, also prepare:

  • NVIDIA GPU server when the chosen model requires GPU acceleration
  • NVIDIA drivers
  • NVIDIA container runtime
  • enough GPU memory for the selected model

For self-hosted embeddings with BAAI/bge-m3 served through TEI:

  • only NVIDIA GPU deployment is supported
  • minimum supported GPU memory is 8 GB VRAM
  • CPU-only deployment is not supported
  • 16 GB VRAM or more is recommended for faster indexing and higher throughput

For current self-hosted infrastructure guidance, see: