Local AI Stack

A fully offline, self-hosted AI environment for Ubuntu 24.04. Runs on any NVIDIA GPU (or CPU-only).

Services: Ollama · Open WebUI · RAG · MCP · ChromaDB · SearXNG · Kiwix · Gitea · InvokeAI · ComfyUI · Portainer

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Quick Start

git clone <this-repo>
cd local-ai
./laptop_full_setup.sh

That's it. The script installs Docker, NVIDIA drivers (if needed), generates all config, starts the stack, and optionally pulls models.

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Service URLs

After setup, all services are available on your LAN:

Service	URL	Purpose
Open WebUI	http://<ip>:3000	Chat interface (Ollama + RAG)
InvokeAI	http://<ip>:9090	Image generation (standalone)
ComfyUI	http://<ip>:8188	Image generation (OWUI integration)
SearXNG	http://<ip>:8888	Private web search
Kiwix	http://<ip>:8181	Offline Wikipedia / docs
Gitea	http://<ip>:3001	Self-hosted Git
RAG Health	http://<ip>:8001/health	RAG server status
MCP SSE	http://<ip>:8002/sse	MCP endpoint for Claude Code
Portainer	https://<ip>:9443	Docker management UI

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Day-to-Day Commands

All generated into ~/docker/ai-stack/ by the setup script:

bash ~/docker/ai-stack/start.sh       # pull latest images + docker compose up -d
bash ~/docker/ai-stack/stop.sh        # docker compose down
bash ~/docker/ai-stack/status.sh      # GPU / container / RAG health
bash ~/docker/ai-stack/pull-models.sh # pull Ollama models (run once after first install)

The stack also registers as a systemd service that starts on boot:

sudo systemctl start local-ai
sudo systemctl stop local-ai
sudo systemctl status local-ai

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Script Reference

Script	Lines	What it does
laptop_full_setup.sh	620	Main setup. Installs Docker + NVIDIA toolkit, creates ~/docker/ai-stack/, writes docker-compose.yml, starts stack, registers systemd service.
local-ai-setup.sh	837	Alternative setup script. Same as above but also auto-detects VRAM and selects models accordingly (14B for ≥14GB VRAM, 7B for CPU). Use this instead of laptop_full_setup.sh if you want VRAM-aware model selection.
ubuntu-post-install.sh	8,889	Full Ubuntu 24.04 post-install (dev tools, fonts, apps, tweaks). Run once on a fresh OS install. Independent of the AI stack.
configure-storage.sh	239	Storage/mount configuration helper. Run separately if you have a secondary drive for AI data.
kiwix_download.sh	198	Downloads ZIM files (Wikipedia, Stack Overflow, etc.) for offline use. Run separately — files are large.
invokeai-import-lora.sh	85	Copies a LoRA .safetensors file into InvokeAI's Docker model volume.

Which setup script should I use?

• laptop_full_setup.sh — fixed model selection (qwen2.5:14b / qwen2.5-coder:7b), simpler
• local-ai-setup.sh — detects your VRAM at runtime and picks appropriate models, also embeds server.py and mcp_server.py directly (doesn't need repo files copied separately)

Both scripts are idempotent — safe to re-run for updates. Config files are kept on re-run unless you pass --force.

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Generated File Layout

~/docker/ai-stack/
├── docker-compose.yml      # generated by setup script
├── .env                    # API tokens — edit this, never overwritten
├── server.py               # RAG server (copied from repo)
├── mcp_server.py           # MCP server (copied from repo)
├── requirements.txt        # RAG Python deps
├── mcp_requirements.txt    # MCP Python deps
├── start.sh                # start the stack
├── stop.sh                 # stop the stack
├── status.sh               # GPU + container + RAG health
├── pull-models.sh          # pull Ollama models
├── Caddyfile.example       # reverse proxy config template
├── papers/                 # drop PDFs here for RAG indexing
├── repos/                  # git repos indexed by RAG
├── workspace/              # MCP working directory
├── index/                  # ChromaDB vector store (persistent)
├── kiwix/                  # ZIM files for Kiwix
├── gitea/                  # Gitea data
├── invokeai-outputs/       # InvokeAI generated images
├── comfyui-output/         # ComfyUI generated images
├── comfyui-data/           # ComfyUI custom nodes
└── logs/

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First Run Checklist

1. Run setup:

   ./laptop_full_setup.sh
   

2. Pull models (prompted at end of setup, or run manually):

   bash ~/docker/ai-stack/pull-models.sh
   

   Downloads ~15-30GB. Takes 10-40 min depending on connection.

3. Add API tokens (optional — for Gitea/GitHub MCP tools):

   nano ~/docker/ai-stack/.env
   

4. Connect Claude Code to MCP:

   claude mcp add local http://<your-ip>:8002/sse
   

5. Download ZIMs for offline docs (optional, large):

   ./kiwix_download.sh
   

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Image Generation from Open WebUI

Open WebUI can generate images inline in chat conversations using ComfyUI as the backend. When configured, you can ask any model to "generate an image of..." and it will call ComfyUI to create the image.

How it works

Open WebUI natively supports these image generation engines:

• ComfyUI — Node-based, best Open WebUI integration, local
• AUTOMATIC1111 — Stable Diffusion WebUI, local
• OpenAI DALL-E — Cloud API
• Gemini — Cloud API

InvokeAI does NOT have a compatible API for Open WebUI integration. It works great as a standalone tool at http://<ip>:9090 but cannot be called from within Open WebUI chats. For chat-integrated image generation, use ComfyUI.

Setup: ComfyUI + Open WebUI (recommended)

If you selected ComfyUI during setup, the environment variables are already configured. You just need to install a model and set up a workflow.

Step 1: Install a Stable Diffusion model in ComfyUI

# Open ComfyUI at http://<ip>:8188
# Use the built-in Model Manager to download a model, or manually:
docker exec comfyui bash -c "cd /opt/ComfyUI/models/checkpoints && \
  wget -q 'https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors'"

Or download any .safetensors checkpoint and copy it in:

docker cp ~/Downloads/my-model.safetensors comfyui:/opt/ComfyUI/models/checkpoints/

Step 2: Create and export a workflow

1. Open ComfyUI at http://<ip>:8188
2. Build or load a workflow (the default text-to-image workflow works)
3. Click the gear icon → enable Dev Mode
4. Click Save (API Format) — this downloads workflow_api.json

Step 3: Configure Open WebUI

1. Open WebUI → Admin → Settings → Images
2. Set Engine to ComfyUI
3. Set URL to http://comfyui:8188 (container networking, already set via env vars)
4. Click Import Workflow and upload your workflow_api.json
5. Map the prompt node (usually the KSampler or CLIPTextEncode node)
6. Save settings

Step 4: Generate images in chat

In any Open WebUI chat, type something like:

• "Generate an image of a mountain landscape at sunset"
• "Create a photo of a cyberpunk city"

The model will detect the image generation request and pass it to ComfyUI.

Setup: AUTOMATIC1111 (alternative)

If you prefer AUTOMATIC1111 over ComfyUI:

1. Run AUTOMATIC1111 with the --api flag
2. In Open WebUI → Admin → Settings → Images:
   • Engine: Automatic1111
   • URL: http://host.docker.internal:7860 (or container name if in Docker)
3. Environment variables (alternative to UI config):

   ENABLE_IMAGE_GENERATION=true
   IMAGE_GENERATION_ENGINE=automatic1111
   AUTOMATIC1111_BASE_URL=http://host.docker.internal:7860
   

Environment variables reference

Variable	Default	Description
ENABLE_IMAGE_GENERATION	false	Enable image generation feature
IMAGE_GENERATION_ENGINE	—	comfyui, automatic1111, openai, or gemini
IMAGE_GENERATION_MODEL	—	Model ID for generation
IMAGE_SIZE	512x512	Default output size
COMFYUI_BASE_URL	—	ComfyUI API URL (e.g. http://comfyui:8188)
COMFYUI_API_KEY	—	ComfyUI API key (if auth enabled)
COMFYUI_WORKFLOW	—	Custom workflow JSON (API format)
AUTOMATIC1111_BASE_URL	—	AUTOMATIC1111 API URL
AUTOMATIC1111_API_AUTH	—	Auth credentials (user:pass)

Recommended Open WebUI Functions

Install these from Admin → Functions → ＋ → Import From Link or search in Discover:

• Auto Memory — Automatically stores relevant info as persistent memories across chats
• Generate Image — Adds a "Generate Image" action button to messages for quick re-generation

VRAM considerations

Image generation and LLM inference compete for GPU memory. With a single GPU:

VRAM	Recommendation
≥ 24GB	Run both LLM + image gen simultaneously
12–24GB	Use smaller LLM when generating images, or stop Ollama first
< 12GB	Run one at a time — stop Ollama before generating images

ComfyUI models typically need 4–8GB VRAM (SD 1.5: ~4GB, SDXL: ~7GB, Flux: ~12GB).

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Using LoRA Models in InvokeAI

LoRA (Low-Rank Adaptation) files let you customize image generation with fine-tuned styles or characters. If you trained a LoRA on RunPod or elsewhere, here's how to use it.

Import a LoRA file

# Copy your LoRA into the InvokeAI Docker volume:
./invokeai-import-lora.sh ~/Downloads/my-lora.safetensors

# Optionally give it a display name:
./invokeai-import-lora.sh ~/Downloads/my-lora.safetensors "My Custom Style"

Use the LoRA in InvokeAI

1. Open InvokeAI at http://<ip>:9090
2. Go to Model Manager (cube icon, left sidebar) and click Scan for Models / Sync Models
3. Your LoRA should appear in the model list
4. Switch to Text to Image tab
5. In the left panel, find the LoRA section (below the model selector)
6. Click + to add your LoRA, then adjust the weight slider (start at 0.7–0.85)

Troubleshooting greyed-out upload buttons

• No base model installed: You need a fully downloaded base model (e.g., SD 1.5) before InvokeAI enables LoRA uploads. Use Model Manager to install one first.
• Model not synced: After copying files, click Scan for Models in Model Manager.
• Architecture mismatch: A LoRA trained on SD 1.5 only works with SD 1.5 base models — not SDXL or SD 2.x.
• Use the import script instead: The greyed-out UI upload can be bypassed entirely by using invokeai-import-lora.sh to copy files directly into the model volume.

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Updating

Re-run the setup script — it detects an existing install and skips prereqs:

./laptop_full_setup.sh
# or
./laptop_full_setup.sh --force   # also overwrites config files

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GPU / Model Tiers (local-ai-setup.sh)

VRAM	Chat model	Code model	Context
≥ 14 GB	qwen2.5:14b	qwen2.5-coder:14b	32k
8–14 GB	qwen2.5:14b	qwen2.5-coder:7b	16k
4–8 GB	qwen2.5:7b	qwen2.5-coder:7b	8k
CPU	qwen2.5:7b	qwen2.5-coder:7b	4k

Embed model is always nomic-embed-text (required for RAG).