AI-Knowledge-Assistant

Open WebUI Deployment — Method Decision, Install, Verification

Deployment method decision (16 GB machine)

Method RAM overhead Verdict
Native Python (uv/pip) ~0.5–1 GB (WebUI process only) Chosen default
Docker Desktop + WSL2 +1.5–2.5 GB idle (WSL2 VM, vmmem, Docker daemon) Fallback only; if used, cap WSL2 RAM via .wslconfig (memory=3GB) and stop Docker when the stack is down

On a 16 GB laptop the Docker overhead is roughly the cost of an entire second 4B model. Native wins unless the audit shows Python cannot be installed.

Install (native, localhost-only)

scripts/04-install-openwebui.ps1 automates this. Manual equivalent:

# Requires Python 3.11 or 3.12 (audit checks this). Then:
pip install uv
# Data lives in %USERPROFILE%\open-webui\data (set DATA_DIR to control it)
$env:DATA_DIR = "$env:USERPROFILE\open-webui\data"
uvx --python 3.12 open-webui@latest serve --host 127.0.0.1 --port 8080

--host 127.0.0.1 is mandatory — the default binds 0.0.0.0 (all interfaces, LAN-visible). Ollama already defaults to 127.0.0.1:11434; leave it that way.

First launch: open http://localhost:8080, create the admin account immediately (first signup becomes admin) with a strong password, then in Admin Panel → Settings disable open signups (Default User Role: pending or disable Enable New Sign Ups).

Connect to Ollama: Admin Panel → Settings → Connections → Ollama API = http://127.0.0.1:11434 (usually auto-detected).

Verification checklist (Gate 4)

Service notes