Claude
ac76ef5181
None of local-ai-setup.sh's tier-selected models (CHAT_MODEL/CODE_MODEL/
EMBED_MODEL) can read an image — there was no way to get vision support out
of this stack at all before now. Added a numbered pick-list to the
generated pull-models.sh, right after the existing DeepSeek-R1 optional
pull, matching that same read -rp pattern:
1) moondream ~1.7 GB by Moondream AI — tiny, built for
CPU-only or weak/old-GPU hardware
2) llava:7b ~4.7 GB general-purpose vision
3) qwen2.5vl:7b ~6 GB stronger accuracy, more RAM/VRAM
4) llama3.2-vision:11b ~7.9 GB heaviest of the four
moondream is the recommended default — sized for exactly the "6 vCPU, 8GB
RAM, no GPU" case this was asked for, unlike the other three which assume
real GPU/RAM headroom.
Verified by actually running the heredoc that generates pull-models.sh
(with EMBED_MODEL/CHAT_MODEL/CODE_MODEL stood in) and syntax-checking the
resulting output script, not just the source — the outer heredoc is
unquoted so $-escaping mistakes wouldn't show up as a bash -n failure on
local-ai-setup.sh itself, only on what it generates.
services/ai-stack.md gets a matching "Vision models" section (sizes, the
manual pull command, and how to point an app's OPENAI_MODEL at one).
laptop_full_setup.sh's separate, non-interactive pull-models.sh generator
is untouched — it's not invoked anywhere in this repo's own install flow
(only local-ai-setup.sh is, from install_ai-stack()), so it's out of
scope here.
1008 lines
40 KiB
Bash
Executable File
1008 lines
40 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Local AI Stack — single script, new install or update
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# Usage: ./local-ai-setup.sh [--force] [--no-pull]
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set -euo pipefail
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RED='\033[0;31m'; GREEN='\033[0;32m'; YELLOW='\033[1;33m'
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CYAN='\033[0;36m'; BOLD='\033[1m'; NC='\033[0m'
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info() { echo -e "${CYAN}[..]${NC} $*"; }
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ok() { echo -e "${GREEN}[OK]${NC} $*"; }
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warn() { echo -e "${YELLOW}[!!]${NC} $*"; }
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section() { echo -e "\n${BOLD}━━━ $* ━━━${NC}"; }
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FORCE=false; NO_PULL=false
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for a in "$@"; do [[ "$a" == "--force" ]] && FORCE=true; [[ "$a" == "--no-pull" ]] && NO_PULL=true; done
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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BASE="$SCRIPT_DIR"
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LOCAL_IP=$(ip route get 1.1.1.1 2>/dev/null | grep -oP 'src \K\S+' || hostname -I | awk '{print $1}')
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[[ -z "$LOCAL_IP" ]] && read -rp "Enter LAN IP: " LOCAL_IP
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IS_UPDATE=false; [[ -f "$BASE/docker-compose.yml" ]] && IS_UPDATE=true
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# ── detect VRAM and set models accordingly ────────────────────────────────────
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VRAM_GB=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits 2>/dev/null \
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| head -1 | awk '{printf "%d", $1/1024}' 2>/dev/null || echo "0")
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GPU_COUNT=$(nvidia-smi --query-gpu=name --format=csv,noheader 2>/dev/null | wc -l || echo "0")
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TOTAL_VRAM=$((VRAM_GB * GPU_COUNT))
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# Ollama optimization flags (stacked — see docs/gpu-setup-research.md)
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OLLAMA_KV_CACHE="q8_0" # halves KV cache VRAM (q4_0 for aggressive)
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OLLAMA_FLASH="1" # flash attention: less VRAM, no quality loss
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if [[ "$TOTAL_VRAM" -ge 40 ]]; then
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CHAT_MODEL="qwen3.5:27b"; CODE_MODEL="qwen3.5:27b"
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CTX=131072; TIER="${TOTAL_VRAM}GB — 27B dense, 128K context"
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elif [[ "$TOTAL_VRAM" -ge 28 ]]; then
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CHAT_MODEL="qwen3.5-35b-a3b"; CODE_MODEL="qwen3.5-35b-a3b"
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CTX=131072; TIER="${TOTAL_VRAM}GB — 35B MoE, 128K context"
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elif [[ "$TOTAL_VRAM" -ge 14 ]]; then
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CHAT_MODEL="qwen3.5-35b-a3b"; CODE_MODEL="qwen3.5-35b-a3b"
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CTX=65536; TIER="${TOTAL_VRAM}GB — 35B MoE + KV quant, 64K context"
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elif [[ "$TOTAL_VRAM" -ge 8 ]]; then
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CHAT_MODEL="qwen3.5:9b"; CODE_MODEL="qwen3.5:9b"
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CTX=32768; TIER="${TOTAL_VRAM}GB — 9B dense, 32K context"
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elif [[ "$TOTAL_VRAM" -ge 6 ]]; then
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CHAT_MODEL="qwen3.5:4b"; CODE_MODEL="qwen3.5:4b"
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CTX=32768; TIER="${TOTAL_VRAM}GB — 4B + KV quant, 32K context"
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elif [[ "$TOTAL_VRAM" -ge 4 ]]; then
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CHAT_MODEL="qwen3.5:4b"; CODE_MODEL="qwen3.5:4b"
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CTX=16384; TIER="${TOTAL_VRAM}GB — 4B models, 16K context"
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else
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CHAT_MODEL="qwen3.5:4b"; CODE_MODEL="qwen3.5:4b"
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CTX=4096; OLLAMA_KV_CACHE="q4_0"; TIER="CPU-only — 4B models, 4K context"
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fi
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EMBED_MODEL="nomic-embed-text"
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# ── Image generation model tiers (VRAM-aware) ────────────────────────────────
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# These vars are used by setup-image-models.sh and printed in status output.
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# Image gen shares GPU with Ollama — Ollama unloads after KEEP_ALIVE timeout,
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# so image gen gets full VRAM when Ollama is idle.
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if [[ "$TOTAL_VRAM" -ge 24 ]]; then
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IMG_MODELS="SD 1.5, SDXL, SDXL Turbo, Flux.1-dev, Flux.1-schnell"
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IMG_TIER="all models including Flux"
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IMG_DEFAULT="SDXL"
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elif [[ "$TOTAL_VRAM" -ge 12 ]]; then
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IMG_MODELS="SD 1.5, SDXL, SDXL Turbo, Flux.1-schnell (tight)"
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IMG_TIER="SDXL + Flux-schnell"
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IMG_DEFAULT="SDXL"
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elif [[ "$TOTAL_VRAM" -ge 8 ]]; then
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IMG_MODELS="SD 1.5, SDXL (tight at 512px), SDXL Turbo"
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IMG_TIER="SD 1.5 comfortable, SDXL possible"
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IMG_DEFAULT="SD 1.5"
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elif [[ "$TOTAL_VRAM" -ge 4 ]]; then
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IMG_MODELS="SD 1.5 (float16)"
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IMG_TIER="SD 1.5 only"
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IMG_DEFAULT="SD 1.5"
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else
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IMG_MODELS="none (CPU generation extremely slow)"
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IMG_TIER="CPU only — not recommended"
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IMG_DEFAULT=""
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fi
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# ── InvokeAI precision (GPU-aware) ───────────────────────────────────────────
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GPU_COMPUTE=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader 2>/dev/null \
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| head -1 | tr -d '.' || echo "0")
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if [[ "$GPU_COMPUTE" -ge 80 ]]; then
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INVOKEAI_PRECISION="bfloat16" # Ampere+ (RTX 30xx/40xx/50xx, A-series)
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elif [[ "$GPU_COMPUTE" -ge 60 ]]; then
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INVOKEAI_PRECISION="float16" # Pascal+ (GTX 10xx, RTX 20xx, Tesla P40/V100)
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else
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INVOKEAI_PRECISION="auto" # Let InvokeAI decide
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fi
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section "Local AI Stack — $($IS_UPDATE && echo UPDATE || echo NEW INSTALL)"
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info "Base : $BASE"
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info "IP : $LOCAL_IP"
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info "GPU : ${VRAM_GB}GB VRAM → $TIER"
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info "Image : $IMG_TIER ($IMG_MODELS)"
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write_if_new() {
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local dest="$1"; local body; body=$(cat)
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if [[ ! -f "$dest" ]] || $FORCE; then
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printf '%s\n' "$body" > "$dest"; ok "Wrote $(basename "$dest")"
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else
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info "Kept $(basename "$dest") (--force to overwrite)"
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fi
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}
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# ── prereqs (new install only) ────────────────────────────────────────────────
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if ! $IS_UPDATE; then
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section "Prerequisites"
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if ! command -v docker &>/dev/null; then
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info "Installing Docker..."
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curl -fsSL https://get.docker.com | sh
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sudo usermod -aG docker "$USER"
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warn "Run: newgrp docker (or log out/in)"
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else
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ok "Docker: $(docker --version | cut -d' ' -f3)"
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fi
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if command -v nvidia-smi &>/dev/null && ! dpkg -l 2>/dev/null | grep -q nvidia-container-toolkit; then
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info "Installing NVIDIA Container Toolkit..."
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curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
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| sudo gpg --dearmor --yes -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
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curl -fsSL https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
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| sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \
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| sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
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sudo apt-get update -qq && sudo apt-get install -y nvidia-container-toolkit
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sudo nvidia-ctk runtime configure --runtime=docker && sudo systemctl restart docker
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ok "NVIDIA Container Toolkit installed"
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fi
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command -v rg &>/dev/null || sudo apt-get install -y ripgrep
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fi
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# ── directories ───────────────────────────────────────────────────────────────
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section "Directories"
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for d in papers repos workspace index invokeai-data invokeai-outputs comfyui-data comfyui-output kiwix gitea portainer-data logs; do
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mkdir -p "$BASE/$d"
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done
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ok "Ready under $BASE"
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# =============================================================================
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section "Writing server.py (RAG)"
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# =============================================================================
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write_if_new "$BASE/server.py" << 'PY'
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import ast, fnmatch, hashlib, json, logging, os, re, subprocess, threading, time
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from pathlib import Path
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from typing import Any
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import chromadb, httpx
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from chromadb.utils.embedding_functions import OllamaEmbeddingFunction
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from fastapi import FastAPI, HTTPException, Request, BackgroundTasks
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
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log = logging.getLogger("rag")
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OLLAMA_URL = os.getenv("OLLAMA_URL", "http://ollama:11434")
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CHROMA_URL = os.getenv("CHROMA_URL", "http://chromadb:8000")
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EMBED_MODEL = os.getenv("EMBED_MODEL", "nomic-embed-text")
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CHAT_MODEL = os.getenv("CHAT_MODEL", "qwen2.5:14b")
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PAPERS_DIR = Path(os.getenv("PAPERS_DIR", "/papers"))
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REPOS_DIR = Path(os.getenv("REPOS_DIR", "/repos"))
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TOP_K = int(os.getenv("TOP_K", "6"))
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CODE_EXTS = {".py",".js",".ts",".tsx",".jsx",".go",".rs",".java",".c",".cpp",
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".h",".cs",".rb",".sh",".yaml",".yml",".toml",".sql",".md"}
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SKIP_DIRS = {"node_modules",".git","__pycache__","dist","build",".venv","venv","target"}
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MAX_BYTES = 400_000
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app = FastAPI(title="RAG Server")
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app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
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def _embed_fn():
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return OllamaEmbeddingFunction(url=f"{OLLAMA_URL}/api/embeddings", model_name=EMBED_MODEL)
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def _chroma():
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host, port = CHROMA_URL.replace("http://","").split(":")
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return chromadb.HttpClient(host=host, port=int(port))
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def get_col(name):
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return _chroma().get_or_create_collection(name, embedding_function=_embed_fn())
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def _doc_id(text, key):
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return hashlib.md5(f"{key}|{text[:200]}".encode()).hexdigest()
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def _sliding(text, size=1000, overlap=150):
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chunks, i = [], 0
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while i < len(text):
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chunks.append(text[i:i+size]); i += size - overlap
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return [c for c in chunks if c.strip()]
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def _chunk_python(src):
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try: tree = ast.parse(src)
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except SyntaxError: return []
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lines = src.splitlines(); out = []
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for node in ast.iter_child_nodes(tree):
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if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)):
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out.append((node.name, "\n".join(lines[node.lineno-1:node.end_lineno])[:4000]))
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return out
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def _chunk_file(path, src):
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if path.suffix == ".py":
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pairs = _chunk_python(src)
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if pairs: return pairs
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pat = re.compile(r'(?:^|\n)(?=(?:export\s+)?(?:async\s+)?(?:function|class)|^func |^type |^impl |^pub fn |^fn )', re.M)
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parts = [p.strip() for p in pat.split(src) if p.strip()]
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if len(parts) > 1: return [(f"s{i}", p[:4000]) for i,p in enumerate(parts)]
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return [(f"c{i}", c) for i,c in enumerate(_sliding(src, 1200, 200))]
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def ingest_file(col, fpath, repo=""):
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if fpath.stat().st_size > MAX_BYTES: return
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if any(fnmatch.fnmatch(fpath.name, p) for p in ("*.min.js","*.map","package-lock.json","yarn.lock")): return
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try: src = fpath.read_text(encoding="utf-8", errors="ignore")
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except: return
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if not src.strip(): return
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rel = str(fpath)
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pairs = _chunk_file(fpath, src) if fpath.suffix in CODE_EXTS else \
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[(f"c{i}",c) for i,c in enumerate(_sliding(src))]
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ids,docs,metas = [],[],[]
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for label,chunk in pairs:
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if not chunk.strip(): continue
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ids.append(_doc_id(chunk, rel+label)); docs.append(chunk)
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metas.append({"source":rel,"label":label,"repo":repo,"lang":fpath.suffix.lstrip(".")})
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if ids: col.upsert(ids=ids, documents=docs, metadatas=metas)
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def ingest_dir(col, directory, repo=""):
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count = 0
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for f in directory.rglob("*"):
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if not f.is_file() or any(p in f.parts for p in SKIP_DIRS): continue
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ingest_file(col, f, repo); count += 1
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log.info("Indexed %d files from %s", count, directory); return count
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def ingest_pdfs(col):
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try: import pypdf
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except ImportError: return 0
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n = 0
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for pdf in PAPERS_DIR.glob("*.pdf"):
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try:
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text = "\n".join(p.extract_text() or "" for p in pypdf.PdfReader(str(pdf)).pages)
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for i,chunk in enumerate(_sliding(text)):
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col.upsert(ids=[_doc_id(chunk,str(pdf)+str(i))], documents=[chunk],
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metadatas=[{"source":str(pdf),"label":f"p{i}","repo":"","lang":"pdf"}])
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n += 1
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except Exception as e: log.warning("PDF %s: %s", pdf.name, e)
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return n
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def _startup():
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for _ in range(40):
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try:
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r = httpx.get(f"{OLLAMA_URL}/api/tags", timeout=5)
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if any(EMBED_MODEL in m["name"] for m in r.json().get("models",[])): break
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except: pass
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log.info("Waiting for embed model..."); time.sleep(5)
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code_col = get_col("code"); papers_col = get_col("papers")
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for d in REPOS_DIR.iterdir():
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if d.is_dir(): ingest_dir(code_col, d, d.name)
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ingest_pdfs(papers_col)
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for f in PAPERS_DIR.glob("*.txt"): ingest_file(papers_col, f)
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log.info("Startup index done")
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@app.on_event("startup")
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async def on_startup(): threading.Thread(target=_startup, daemon=True).start()
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@app.get("/health")
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async def health():
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try:
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cc = _chroma()
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return {"status":"ok",
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"code": cc.get_collection("code", embedding_function=_embed_fn()).count(),
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"papers": cc.get_collection("papers", embedding_function=_embed_fn()).count()}
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except Exception as e: return {"status":"error","detail":str(e)}
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class RepoReq(BaseModel):
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url: str; name: str = ""; branch: str = "main"
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@app.post("/ingest/repo")
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async def ingest_repo(req: RepoReq):
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name = req.name or req.url.rstrip("/").split("/")[-1].removesuffix(".git")
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dest = REPOS_DIR / name
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try:
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if dest.exists(): subprocess.run(["git","pull"], cwd=dest, check=True, timeout=120)
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else: subprocess.run(["git","clone","--depth=1","-b",req.branch,req.url,str(dest)], check=True, timeout=300)
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except subprocess.CalledProcessError as e: raise HTTPException(400, str(e))
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return {"status":"ok","repo":name,"files":ingest_dir(get_col("code"),dest,name)}
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@app.post("/ingest/papers")
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async def trigger_papers(bg: BackgroundTasks):
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bg.add_task(ingest_pdfs, get_col("papers")); return {"status":"queued"}
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async def _webhook(payload):
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repo = payload.get("repository") or {}
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url = repo.get("clone_url") or repo.get("html_url",""); name = repo.get("name","unknown")
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if not url: return {"status":"ignored"}
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dest = REPOS_DIR / name
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if dest.exists(): subprocess.run(["git","pull"], cwd=dest, timeout=120)
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else: subprocess.run(["git","clone","--depth=1",url,str(dest)], timeout=300)
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return {"status":"ok","repo":name,"files":ingest_dir(get_col("code"),dest,name)}
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@app.post("/webhook/gitea")
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async def wh_gitea(r: Request): return await _webhook(await r.json())
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@app.post("/webhook/github")
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async def wh_github(r: Request): return await _webhook(await r.json())
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def _ctx(query, cols):
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parts = []
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for cn in cols:
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try:
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col = get_col(cn)
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if col.count() == 0: continue
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res = col.query(query_texts=[query], n_results=min(TOP_K, col.count()))
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for doc,meta in zip(res["documents"][0], res["metadatas"][0]):
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parts.append(f"### {meta.get('source','')}:{meta.get('label','')}\n```{meta.get('lang','')}\n{doc}\n```")
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except Exception as e: log.warning("col %s: %s", cn, e)
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return "\n\n".join(parts)
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class ChatReq(BaseModel):
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model: str = CHAT_MODEL; messages: list[dict[str,Any]]
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stream: bool = False; collections: list[str] = ["code","papers"]
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@app.post("/v1/chat/completions")
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async def chat(req: ChatReq):
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query = next((m["content"] for m in reversed(req.messages) if m.get("role")=="user"), "")
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ctx = _ctx(query, req.collections)
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msgs = [{"role":"system","content":f"You are a coding assistant. Use context below.\n\n## Context\n{ctx}"}] + req.messages
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payload = {"model":req.model,"messages":msgs,"stream":req.stream}
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if req.stream:
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async def gen():
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async with httpx.AsyncClient(timeout=300) as c:
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async with c.stream("POST",f"{OLLAMA_URL}/v1/chat/completions",json=payload) as r:
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async for chunk in r.aiter_bytes(): yield chunk
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return StreamingResponse(gen(), media_type="text/event-stream")
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async with httpx.AsyncClient(timeout=300) as c:
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r = await c.post(f"{OLLAMA_URL}/v1/chat/completions", json=payload)
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return r.json()
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if __name__ == "__main__":
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import uvicorn; uvicorn.run("server:app", host="0.0.0.0", port=8001, reload=False)
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PY
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# =============================================================================
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||
section "Writing mcp_server.py"
|
||
# =============================================================================
|
||
write_if_new "$BASE/mcp_server.py" << 'PY'
|
||
import json, os, re, subprocess
|
||
from pathlib import Path
|
||
import httpx
|
||
from mcp.server.fastmcp import FastMCP
|
||
|
||
WORKSPACE = Path(os.getenv("WORKSPACE_DIR", "/workspace"))
|
||
REPOS_DIR = Path(os.getenv("REPOS_DIR", "/repos"))
|
||
GITEA_URL = os.getenv("GITEA_URL", "http://gitea:3000")
|
||
GITEA_TOKEN = os.getenv("GITEA_TOKEN", "")
|
||
GITHUB_TOKEN = os.getenv("GITHUB_TOKEN","")
|
||
RAG_URL = os.getenv("RAG_URL", "http://rag-server:8001")
|
||
|
||
mcp = FastMCP("local-dev-tools")
|
||
|
||
@mcp.tool()
|
||
def bash(command: str, cwd: str = "") -> str:
|
||
"""Run a shell command. Default cwd is /workspace."""
|
||
work = Path(cwd) if cwd else WORKSPACE
|
||
work.mkdir(parents=True, exist_ok=True)
|
||
try:
|
||
r = subprocess.run(command, shell=True, cwd=work, timeout=120, capture_output=True, text=True)
|
||
out = r.stdout + (f"\n[stderr]\n{r.stderr}" if r.stderr else "")
|
||
if r.returncode != 0: out += f"\n[exit {r.returncode}]"
|
||
return out or "(no output)"
|
||
except subprocess.TimeoutExpired: return "[timeout]"
|
||
except Exception as e: return f"[error] {e}"
|
||
|
||
@mcp.tool()
|
||
def read_file(path: str) -> str:
|
||
"""Read a file. Absolute or relative to /workspace."""
|
||
p = Path(path) if Path(path).is_absolute() else WORKSPACE / path
|
||
if not p.exists(): return f"[not found] {p}"
|
||
if p.stat().st_size > 500_000: return f"[too large: {p.stat().st_size//1024}KB]"
|
||
return p.read_text(encoding="utf-8", errors="replace")
|
||
|
||
@mcp.tool()
|
||
def write_file(path: str, content: str) -> str:
|
||
"""Write content to a file. Relative to /workspace."""
|
||
p = Path(path) if Path(path).is_absolute() else WORKSPACE / path
|
||
p.parent.mkdir(parents=True, exist_ok=True)
|
||
p.write_text(content, encoding="utf-8")
|
||
return f"Wrote {len(content)} chars to {p}"
|
||
|
||
@mcp.tool()
|
||
def list_files(path: str = "", pattern: str = "**/*") -> str:
|
||
"""List files matching a glob pattern."""
|
||
base = Path(path) if path else WORKSPACE
|
||
if not base.exists(): return f"[not found] {base}"
|
||
files = sorted(str(f.relative_to(base)) for f in base.glob(pattern) if f.is_file())
|
||
return "\n".join(files[:500]) or "(empty)"
|
||
|
||
@mcp.tool()
|
||
def search_code(query: str, path: str = "", glob: str = "", case_sensitive: bool = False) -> str:
|
||
"""Search file contents with ripgrep."""
|
||
base = path or str(WORKSPACE)
|
||
cmd = ["rg", "--line-number", "--no-heading"]
|
||
if not case_sensitive: cmd.append("-i")
|
||
if glob: cmd += ["-g", glob]
|
||
cmd += [query, base]
|
||
try:
|
||
r = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
|
||
lines = r.stdout.strip().splitlines()
|
||
if len(lines) > 200: lines = lines[:200] + [f"...({len(r.stdout.splitlines())-200} more)"]
|
||
return "\n".join(lines) or "(no matches)"
|
||
except FileNotFoundError:
|
||
r = subprocess.run(["grep","-rn",query,base], capture_output=True, text=True, timeout=30)
|
||
return r.stdout[:8000] or "(no matches)"
|
||
|
||
@mcp.tool()
|
||
def fetch_url(url: str, extract_text: bool = True) -> str:
|
||
"""Fetch the content of a URL."""
|
||
try:
|
||
r = httpx.get(url, timeout=30, follow_redirects=True, headers={"User-Agent":"Mozilla/5.0"})
|
||
content = r.text
|
||
if extract_text:
|
||
content = re.sub(r'<script[^>]*>.*?</script>', '', content, flags=re.DOTALL)
|
||
content = re.sub(r'<style[^>]*>.*?</style>', '', content, flags=re.DOTALL)
|
||
content = re.sub(r'<[^>]+>', '', content)
|
||
content = re.sub(r'\n{3,}', '\n\n', content).strip()
|
||
return content[:20000]
|
||
except Exception as e: return f"[error] {e}"
|
||
|
||
def _git(args, repo=""):
|
||
cwd = Path(repo) if repo else WORKSPACE
|
||
r = subprocess.run(["git"]+args, cwd=cwd, capture_output=True, text=True, timeout=60)
|
||
return (r.stdout + r.stderr).strip() or "(no output)"
|
||
|
||
@mcp.tool()
|
||
def git_status(repo: str = "") -> str:
|
||
"""Show git status."""
|
||
return _git(["status","--short"], repo)
|
||
|
||
@mcp.tool()
|
||
def git_diff(repo: str = "", cached: bool = False) -> str:
|
||
"""Show git diff."""
|
||
flag = ["--cached"] if cached else []
|
||
return _git(["diff","--stat"]+flag, repo) + "\n\n" + _git(["diff"]+flag, repo)
|
||
|
||
@mcp.tool()
|
||
def git_log(repo: str = "", n: int = 10) -> str:
|
||
"""Show last n commits."""
|
||
return _git(["log",f"-{n}","--oneline","--decorate"], repo)
|
||
|
||
@mcp.tool()
|
||
def git_commit(message: str, repo: str = "", add_all: bool = True) -> str:
|
||
"""Stage all and commit."""
|
||
if add_all: _git(["add","-A"], repo)
|
||
return _git(["commit","-m",message], repo)
|
||
|
||
@mcp.tool()
|
||
def git_checkout(branch: str, repo: str = "", create: bool = False) -> str:
|
||
"""Checkout or create a branch."""
|
||
return _git(["checkout","-b",branch] if create else ["checkout",branch], repo)
|
||
|
||
def _gitea(method, path, body=None):
|
||
if not GITEA_TOKEN: return {"error":"GITEA_TOKEN not set in .env"}
|
||
r = httpx.request(method, f"{GITEA_URL}/api/v1{path}",
|
||
json=body, headers={"Authorization":f"token {GITEA_TOKEN}"}, timeout=30)
|
||
try: return r.json()
|
||
except: return {"status":r.status_code,"text":r.text}
|
||
|
||
@mcp.tool()
|
||
def gitea_list_repos() -> str:
|
||
"""List your Gitea repos."""
|
||
d = _gitea("GET", "/repos/search?limit=50")
|
||
if "error" in d: return d["error"]
|
||
return "\n".join(f"{r['full_name']} — {r.get('description','')}" for r in d.get("data",[]))
|
||
|
||
@mcp.tool()
|
||
def gitea_create_repo(name: str, private: bool = True, description: str = "") -> str:
|
||
"""Create a Gitea repo."""
|
||
r = _gitea("POST","/user/repos",{"name":name,"private":private,"description":description,"auto_init":True,"default_branch":"main"})
|
||
return r.get("html_url") or str(r)
|
||
|
||
@mcp.tool()
|
||
def gitea_create_issue(repo: str, title: str, body: str = "") -> str:
|
||
"""Create a Gitea issue (owner/repo)."""
|
||
r = _gitea("POST",f"/repos/{repo}/issues",{"title":title,"body":body})
|
||
return r.get("html_url") or str(r)
|
||
|
||
@mcp.tool()
|
||
def github_api(method: str, endpoint: str, body: str = "") -> str:
|
||
"""Call GitHub REST API. endpoint e.g. /repos/owner/repo/issues"""
|
||
if not GITHUB_TOKEN: return "GITHUB_TOKEN not set in .env"
|
||
r = httpx.request(method.upper(), f"https://api.github.com{endpoint}",
|
||
json=json.loads(body) if body else None,
|
||
headers={"Authorization":f"Bearer {GITHUB_TOKEN}","Accept":"application/vnd.github+json"},
|
||
timeout=30)
|
||
try: return json.dumps(r.json(), indent=2)
|
||
except: return r.text
|
||
|
||
@mcp.tool()
|
||
def ingest_repo(url: str, name: str = "", branch: str = "main") -> str:
|
||
"""Clone a repo and index it in RAG."""
|
||
r = httpx.post(f"{RAG_URL}/ingest/repo", json={"url":url,"name":name,"branch":branch}, timeout=300)
|
||
return r.text
|
||
|
||
@mcp.tool()
|
||
def rag_health() -> str:
|
||
"""Check RAG server status."""
|
||
try: return httpx.get(f"{RAG_URL}/health", timeout=10).text
|
||
except Exception as e: return f"RAG unreachable: {e}"
|
||
|
||
if __name__ == "__main__":
|
||
import uvicorn
|
||
uvicorn.run(mcp.sse_app(), host="0.0.0.0", port=8002)
|
||
PY
|
||
|
||
# =============================================================================
|
||
section "Requirements"
|
||
# =============================================================================
|
||
cat > "$BASE/requirements.txt" << 'REQ'
|
||
fastapi
|
||
uvicorn[standard]
|
||
httpx
|
||
pydantic
|
||
chromadb
|
||
pypdf
|
||
python-multipart
|
||
REQ
|
||
|
||
cat > "$BASE/mcp_requirements.txt" << 'REQ'
|
||
mcp[cli]
|
||
fastapi
|
||
uvicorn[standard]
|
||
httpx
|
||
duckduckgo-search
|
||
REQ
|
||
ok "requirements.txt + mcp_requirements.txt"
|
||
|
||
# =============================================================================
|
||
section ".env (tokens — never overwritten)"
|
||
# =============================================================================
|
||
if [[ ! -f "$BASE/.env" ]]; then
|
||
cat > "$BASE/.env" << ENV
|
||
# Local AI Stack — edit to add your API tokens
|
||
GITEA_TOKEN=your-gitea-token-here
|
||
GITHUB_TOKEN=your-github-token-here
|
||
GITEA_URL=http://$LOCAL_IP:3001
|
||
ENV
|
||
ok "Created .env"
|
||
else
|
||
info "Kept .env"
|
||
fi
|
||
|
||
# =============================================================================
|
||
section "Docker Compose"
|
||
# =============================================================================
|
||
cat > "$BASE/docker-compose.yml" << COMPOSE
|
||
# Local AI Stack — generated $(date '+%Y-%m-%d')
|
||
# GPU: OLLAMA_NUM_GPU=999 uses all available VRAM automatically (V100/RTX/any)
|
||
# Context: OLLAMA_NUM_CTX= set by detected VRAM (GB)
|
||
#
|
||
# ── Common commands (run from this folder) ─────────────────────────────────────
|
||
# Start everything: docker compose up -d
|
||
# Stop everything: docker compose down
|
||
# Restart one service: docker compose restart <service>
|
||
# Stop one service: docker compose stop <service>
|
||
# Start one service: docker compose up -d <service>
|
||
# Follow all logs: docker compose logs -f
|
||
# Follow one service logs: docker compose logs -f <service>
|
||
# Pull latest images: docker compose pull && docker compose up -d
|
||
# Show status: docker compose ps
|
||
#
|
||
# Services: ollama open-webui chromadb rag-server mcp-server aider
|
||
# kiwix gitea invokeai portainer
|
||
# ───────────────────────────────────────────────────────────────────────────────
|
||
|
||
services:
|
||
|
||
ollama:
|
||
image: ollama/ollama:latest
|
||
container_name: ollama
|
||
restart: unless-stopped
|
||
ports: ["0.0.0.0:11434:11434"]
|
||
volumes: [ollama-models:/root/.ollama]
|
||
environment:
|
||
- OLLAMA_NUM_GPU=999
|
||
- OLLAMA_NUM_CTX=$CTX
|
||
- OLLAMA_KEEP_ALIVE=24h
|
||
- OLLAMA_MAX_LOADED_MODELS=1
|
||
- OLLAMA_KV_CACHE_TYPE=$OLLAMA_KV_CACHE
|
||
- OLLAMA_FLASH_ATTENTION=$OLLAMA_FLASH
|
||
deploy:
|
||
resources:
|
||
reservations:
|
||
devices:
|
||
- driver: nvidia
|
||
count: all
|
||
capabilities: [gpu]
|
||
healthcheck:
|
||
test: ["CMD","ollama","list"]
|
||
interval: 30s; timeout: 10s; retries: 5
|
||
|
||
open-webui:
|
||
image: ghcr.io/open-webui/open-webui:main
|
||
container_name: open-webui
|
||
restart: unless-stopped
|
||
ports: ["0.0.0.0:3000:8080"]
|
||
volumes: [open-webui-data:/app/backend/data]
|
||
environment:
|
||
- OLLAMA_BASE_URL=http://ollama:11434
|
||
- OPENAI_API_BASE_URL=http://rag-server:8001/v1
|
||
- OPENAI_API_KEY=local-rag
|
||
- ENABLE_OPENAI_API=true
|
||
- ENABLE_TOOL_SERVERS=true
|
||
- WEBUI_AUTH=true
|
||
- ENABLE_RAG_WEB_SEARCH=true
|
||
- RAG_WEB_SEARCH_ENGINE=duckduckgo
|
||
- ENABLE_IMAGE_GENERATION=true
|
||
- IMAGE_GENERATION_ENGINE=comfyui
|
||
- COMFYUI_BASE_URL=http://comfyui:8188
|
||
depends_on:
|
||
ollama: {condition: service_healthy}
|
||
|
||
chromadb:
|
||
image: chromadb/chroma:latest
|
||
container_name: chromadb
|
||
restart: unless-stopped
|
||
ports: ["0.0.0.0:8000:8000"]
|
||
volumes: [$BASE/index:/chroma/chroma]
|
||
environment:
|
||
- IS_PERSISTENT=TRUE
|
||
- ANONYMIZED_TELEMETRY=FALSE
|
||
healthcheck:
|
||
test: ["CMD-SHELL","wget -qO- http://localhost:8000/api/v2/heartbeat || exit 1"]
|
||
interval: 15s; timeout: 5s; retries: 5
|
||
|
||
rag-server:
|
||
image: python:3.11-slim
|
||
container_name: rag-server
|
||
restart: unless-stopped
|
||
ports: ["0.0.0.0:8001:8001"]
|
||
volumes:
|
||
- $BASE/papers:/papers
|
||
- $BASE/repos:/repos
|
||
- $BASE/index:/index
|
||
- $BASE/server.py:/app/server.py
|
||
- $BASE/requirements.txt:/app/requirements.txt
|
||
working_dir: /app
|
||
environment:
|
||
- OLLAMA_URL=http://ollama:11434
|
||
- CHROMA_URL=http://chromadb:8000
|
||
- EMBED_MODEL=nomic-embed-text
|
||
- CHAT_MODEL=$CHAT_MODEL
|
||
command: >
|
||
bash -c "apt-get update -qq && apt-get install -y --no-install-recommends git &&
|
||
pip install --no-cache-dir -r requirements.txt &&
|
||
uvicorn server:app --host 0.0.0.0 --port 8001"
|
||
depends_on:
|
||
chromadb: {condition: service_healthy}
|
||
ollama: {condition: service_healthy}
|
||
|
||
mcp-server:
|
||
image: python:3.11-slim
|
||
container_name: mcp-server
|
||
restart: unless-stopped
|
||
ports: ["0.0.0.0:8002:8002"]
|
||
volumes:
|
||
- $BASE/workspace:/workspace
|
||
- $BASE/repos:/repos
|
||
- $BASE/mcp_server.py:/app/mcp_server.py
|
||
- $BASE/mcp_requirements.txt:/app/mcp_requirements.txt
|
||
- $SCRIPT_DIR/gitea-github-sync.sh:/app/gitea-github-sync.sh:ro
|
||
working_dir: /app
|
||
env_file: $BASE/.env
|
||
environment:
|
||
- WORKSPACE_DIR=/workspace
|
||
- REPOS_DIR=/repos
|
||
- GITEA_URL=http://gitea:3000
|
||
- RAG_URL=http://rag-server:8001
|
||
- KIWIX_URL=http://kiwix:80
|
||
command: >
|
||
bash -c "apt-get update -qq && apt-get install -y --no-install-recommends git ripgrep curl &&
|
||
pip install --no-cache-dir -r mcp_requirements.txt &&
|
||
python mcp_server.py"
|
||
depends_on: [rag-server, kiwix]
|
||
|
||
aider:
|
||
image: paulgauthier/aider:latest
|
||
container_name: aider
|
||
restart: unless-stopped
|
||
ports: ["0.0.0.0:8080:8501"]
|
||
volumes:
|
||
- $BASE/workspace:/workspace
|
||
- $BASE/repos:/repos
|
||
working_dir: /workspace
|
||
environment:
|
||
- OLLAMA_API_BASE=http://ollama:11434
|
||
- GIT_AUTHOR_NAME=aider
|
||
- GIT_AUTHOR_EMAIL=aider@local
|
||
- GIT_COMMITTER_NAME=aider
|
||
- GIT_COMMITTER_EMAIL=aider@local
|
||
command: >
|
||
--gui --no-auto-commits --no-check-update
|
||
--model ollama/$CODE_MODEL
|
||
depends_on:
|
||
ollama: {condition: service_healthy}
|
||
|
||
kiwix:
|
||
image: ghcr.io/kiwix/kiwix-serve:latest
|
||
container_name: kiwix
|
||
restart: unless-stopped
|
||
ports: ["0.0.0.0:8181:80"]
|
||
volumes: [$BASE/kiwix:/data]
|
||
entrypoint: ["sh", "-c"]
|
||
command: ["ls /data/*.zim >/dev/null 2>&1 && exec kiwix-serve /data/*.zim || { echo 'No ZIM files in /data yet - sleeping. Add .zim files and restart kiwix.'; exec sleep infinity; }"]
|
||
|
||
gitea:
|
||
image: gitea/gitea:latest
|
||
container_name: gitea
|
||
restart: unless-stopped
|
||
ports: ["0.0.0.0:3001:3000","0.0.0.0:2222:22"]
|
||
volumes:
|
||
- $BASE/gitea:/data
|
||
- /etc/timezone:/etc/timezone:ro
|
||
- /etc/localtime:/etc/localtime:ro
|
||
environment:
|
||
- USER_UID=1000
|
||
- USER_GID=1000
|
||
- GITEA__database__DB_TYPE=sqlite3
|
||
- GITEA__database__PATH=/data/gitea/gitea.db
|
||
- GITEA__webhook__ALLOWED_HOST_LIST=rag-server,mcp-server
|
||
|
||
invokeai:
|
||
image: ghcr.io/invoke-ai/invokeai:latest
|
||
container_name: invokeai
|
||
restart: unless-stopped
|
||
ports: ["0.0.0.0:9090:9090"]
|
||
volumes:
|
||
- invokeai-models:/invokeai/models
|
||
- $BASE/invokeai-outputs:/invokeai/outputs
|
||
- $BASE/invokeai-data:/invokeai/databases
|
||
environment:
|
||
- INVOKEAI_HOST=0.0.0.0
|
||
- INVOKEAI_PORT=9090
|
||
- INVOKEAI_PRECISION=$INVOKEAI_PRECISION
|
||
deploy:
|
||
resources:
|
||
reservations:
|
||
devices:
|
||
- driver: nvidia
|
||
count: all
|
||
capabilities: [gpu]
|
||
|
||
comfyui:
|
||
image: ghcr.io/ai-dock/comfyui:latest
|
||
container_name: comfyui
|
||
restart: unless-stopped
|
||
ports: ["0.0.0.0:8188:8188"]
|
||
volumes:
|
||
- comfyui-models:/opt/ComfyUI/models
|
||
- $BASE/comfyui-output:/opt/ComfyUI/output
|
||
- $BASE/comfyui-data:/opt/ComfyUI/custom_nodes
|
||
environment:
|
||
- CLI_ARGS=--listen 0.0.0.0
|
||
deploy:
|
||
resources:
|
||
reservations:
|
||
devices:
|
||
- driver: nvidia
|
||
count: all
|
||
capabilities: [gpu]
|
||
|
||
portainer:
|
||
image: portainer/portainer-ce:latest
|
||
container_name: portainer
|
||
restart: unless-stopped
|
||
ports: ["0.0.0.0:9000:9000","0.0.0.0:9443:9443"]
|
||
volumes:
|
||
- /var/run/docker.sock:/var/run/docker.sock
|
||
- $BASE/portainer-data:/data
|
||
|
||
volumes:
|
||
ollama-models:
|
||
open-webui-data:
|
||
invokeai-models:
|
||
comfyui-models:
|
||
COMPOSE
|
||
ok "docker-compose.yml"
|
||
|
||
# =============================================================================
|
||
section "Firewall"
|
||
# =============================================================================
|
||
if command -v ufw &>/dev/null && [[ ! -f "$BASE/.ufw-done" ]] || $FORCE; then
|
||
read -rp " LAN subnet [192.168.1.0/24]: " LAN; LAN="${LAN:-192.168.1.0/24}"
|
||
[[ "$LAN" =~ /[0-9]+$ ]] || LAN="${LAN}/24"
|
||
for pc in "3000:Open WebUI" "11434:Ollama" "8001:RAG" "8002:MCP" \
|
||
"8000:ChromaDB" "8080:Aider" "8181:Kiwix" \
|
||
"3001:Gitea" "2222:Gitea SSH" "9090:InvokeAI" "8188:ComfyUI" \
|
||
"9000:Portainer" "9443:Portainer S"; do
|
||
sudo ufw allow from "$LAN" to any port "${pc%%:*}" proto tcp comment "${pc##*:}" >/dev/null
|
||
done
|
||
sudo ufw reload >/dev/null; ok "UFW rules set for $LAN"; touch "$BASE/.ufw-done"
|
||
fi
|
||
|
||
# =============================================================================
|
||
section "Helper Scripts"
|
||
# =============================================================================
|
||
cat > "$BASE/start.sh" << STARTSH
|
||
#!/bin/bash
|
||
cd "$BASE"
|
||
docker compose pull --quiet 2>/dev/null
|
||
docker compose up -d
|
||
echo ""
|
||
echo " Open WebUI → http://$LOCAL_IP:3000"
|
||
echo " Aider UI → http://$LOCAL_IP:8080 (Claude Code-like editor)"
|
||
echo " InvokeAI → http://$LOCAL_IP:9090 (inpainting, img2img)"
|
||
echo " ComfyUI → http://$LOCAL_IP:8188 (chat-integrated image gen)"
|
||
echo " Kiwix → http://$LOCAL_IP:8181 (run kiwix_download.sh first)"
|
||
echo " Gitea → http://$LOCAL_IP:3001"
|
||
echo " RAG → http://$LOCAL_IP:8001/health"
|
||
echo " MCP SSE → http://$LOCAL_IP:8002/sse"
|
||
echo " Portainer → https://$LOCAL_IP:9443"
|
||
echo ""
|
||
echo " claude mcp add local http://$LOCAL_IP:8002/sse"
|
||
STARTSH
|
||
chmod +x "$BASE/start.sh"
|
||
|
||
cat > "$BASE/stop.sh" << STOPSH
|
||
#!/bin/bash
|
||
cd "$BASE" && docker compose down
|
||
STOPSH
|
||
chmod +x "$BASE/stop.sh"
|
||
|
||
cat > "$BASE/status.sh" << 'STATUSSH'
|
||
#!/bin/bash
|
||
echo "=== GPU ===" && nvidia-smi --query-gpu=name,memory.used,memory.total \
|
||
--format=csv,noheader 2>/dev/null || echo "(no GPU)"
|
||
echo "" && echo "=== Containers ===" && docker ps --format "table {{.Names}}\t{{.Status}}\t{{.Ports}}"
|
||
echo "" && echo "=== Ollama ===" && docker exec ollama ollama ps 2>/dev/null || echo "(not running)"
|
||
echo "" && echo "=== RAG ===" && curl -s http://localhost:8001/health | python3 -m json.tool 2>/dev/null
|
||
STATUSSH
|
||
chmod +x "$BASE/status.sh"
|
||
|
||
cat > "$BASE/pull-models.sh" << PULLSH
|
||
#!/bin/bash
|
||
echo "Waiting for Ollama..."
|
||
until docker exec ollama ollama list &>/dev/null; do sleep 3; done
|
||
|
||
echo "Embed model (RAG — required)..."
|
||
docker exec ollama ollama pull $EMBED_MODEL
|
||
|
||
echo "Fast chat model..."
|
||
docker exec ollama ollama pull qwen2.5:7b
|
||
|
||
echo "Smart chat model..."
|
||
docker exec ollama ollama pull $CHAT_MODEL
|
||
|
||
echo "Code model..."
|
||
docker exec ollama ollama pull $CODE_MODEL
|
||
|
||
echo "Reasoning model (DeepSeek-R1 14B — optional)..."
|
||
read -rp "Pull DeepSeek-R1:14b for planning/reasoning? [y/N]: " DR
|
||
[[ "\${DR,,}" == "y" ]] && docker exec ollama ollama pull deepseek-r1:14b
|
||
|
||
echo ""
|
||
echo "Vision model (optional — image understanding: Mealie's \"import recipe from"
|
||
echo "photo\", attaching images in Open WebUI chat, etc.). None of the models"
|
||
echo "above can read an image; pick one of these if you need that:"
|
||
echo " 1) moondream ~1.7 GB Moondream AI — tiny, built for CPU-only/"
|
||
echo " weak or old GPU hardware. Recommended"
|
||
echo " default if you have no GPU or a low-VRAM one."
|
||
echo " 2) llava:7b ~4.7 GB General-purpose vision, moderate resources."
|
||
echo " 3) qwen2.5vl:7b ~6 GB Stronger accuracy, needs more RAM/VRAM."
|
||
echo " 4) llama3.2-vision:11b ~7.9 GB Meta's vision model — heaviest of these four."
|
||
read -rp "Pull a vision model? [1-4, blank to skip]: " VM
|
||
case "\$VM" in
|
||
1) docker exec ollama ollama pull moondream ;;
|
||
2) docker exec ollama ollama pull llava:7b ;;
|
||
3) docker exec ollama ollama pull qwen2.5vl:7b ;;
|
||
4) docker exec ollama ollama pull llama3.2-vision:11b ;;
|
||
"") : ;;
|
||
*) echo "Unrecognized choice '\$VM' — skipping. Pull manually later with:"
|
||
echo " docker exec ollama ollama pull <model>" ;;
|
||
esac
|
||
|
||
echo "" && docker exec ollama ollama list
|
||
PULLSH
|
||
chmod +x "$BASE/pull-models.sh"
|
||
ok "start.sh stop.sh status.sh pull-models.sh"
|
||
|
||
cat > "$BASE/aider.sh" << AIDSH
|
||
#!/bin/bash
|
||
# Aider CLI — Claude Code-like terminal experience against any local git repo.
|
||
# Usage:
|
||
# ./aider.sh # interactive, files from stdin
|
||
# ./aider.sh src/main.py # open specific files
|
||
# ./aider.sh --model ollama/qwen2.5-coder:7b src/foo.py # override model
|
||
#
|
||
# Tip: clone Gitea repos into $BASE/repos/, then:
|
||
# cd $BASE/repos/my-project && $BASE/aider.sh <files>
|
||
|
||
MODEL="\${AIDER_MODEL:-ollama/$CODE_MODEL}"
|
||
REPO_ROOT="\$(git rev-parse --show-toplevel 2>/dev/null || pwd)"
|
||
|
||
docker run --rm -it \\
|
||
--network local-ai_default \\
|
||
-v "\$REPO_ROOT:\$REPO_ROOT" \\
|
||
-v "$BASE/repos:/repos" \\
|
||
-w "\$REPO_ROOT" \\
|
||
-e OLLAMA_API_BASE=http://ollama:11434 \\
|
||
-e GIT_AUTHOR_NAME=aider \\
|
||
-e GIT_AUTHOR_EMAIL=aider@local \\
|
||
-e GIT_COMMITTER_NAME=aider \\
|
||
-e GIT_COMMITTER_EMAIL=aider@local \\
|
||
paulgauthier/aider:latest \\
|
||
--no-auto-commits \\
|
||
--no-check-update \\
|
||
--model "\$MODEL" \\
|
||
"\$@"
|
||
AIDSH
|
||
chmod +x "$BASE/aider.sh"
|
||
ok "aider.sh"
|
||
|
||
# =============================================================================
|
||
section "Systemd"
|
||
# =============================================================================
|
||
sudo tee /etc/systemd/system/local-ai.service >/dev/null << SYSD
|
||
[Unit]
|
||
Description=Local AI Stack
|
||
After=docker.service network-online.target
|
||
Requires=docker.service
|
||
|
||
[Service]
|
||
Type=oneshot
|
||
RemainAfterExit=yes
|
||
User=$USER
|
||
WorkingDirectory=$BASE
|
||
ExecStart=/bin/bash $BASE/start.sh
|
||
ExecStop=/bin/bash $BASE/stop.sh
|
||
TimeoutStartSec=300
|
||
|
||
[Install]
|
||
WantedBy=multi-user.target
|
||
SYSD
|
||
sudo systemctl daemon-reload && sudo systemctl enable local-ai.service
|
||
ok "Systemd: local-ai.service enabled"
|
||
|
||
# =============================================================================
|
||
section "Starting Stack"
|
||
# =============================================================================
|
||
cd "$BASE"
|
||
info "Pulling images..."
|
||
docker compose pull --quiet
|
||
docker compose up -d
|
||
ok "Stack running"
|
||
|
||
if ! $IS_UPDATE && ! $NO_PULL; then
|
||
echo ""
|
||
read -rp "Pull Ollama models now? (~15-30 min) [Y/n]: " DO_PULL
|
||
[[ "${DO_PULL,,}" != "n" ]] && bash "$BASE/pull-models.sh"
|
||
|
||
# Install image generation base model (GPU-aware)
|
||
if [[ -n "$IMG_DEFAULT" ]] && [[ -x "$SCRIPT_DIR/setup-image-models.sh" ]]; then
|
||
echo ""
|
||
section "Image Generation Models"
|
||
info "GPU: ${TOTAL_VRAM}GB VRAM → $IMG_TIER"
|
||
info "Auto-installing recommended model: $IMG_DEFAULT"
|
||
bash "$SCRIPT_DIR/setup-image-models.sh" --auto
|
||
fi
|
||
fi
|
||
|
||
# =============================================================================
|
||
echo ""
|
||
echo -e "${GREEN}${BOLD}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
|
||
echo -e "${GREEN}${BOLD} Done! GPU: ${VRAM_GB}GB → $TIER${NC}"
|
||
echo -e "${GREEN}${BOLD} Image gen: $IMG_TIER${NC}"
|
||
echo -e "${GREEN}${BOLD}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
|
||
echo ""
|
||
echo -e " ${CYAN}Open WebUI${NC} → http://$LOCAL_IP:3000"
|
||
echo -e " ${CYAN}Aider UI${NC} → http://$LOCAL_IP:8080 (browser coding assistant)"
|
||
echo -e " ${CYAN}InvokeAI${NC} → http://$LOCAL_IP:9090 (inpainting, img2img)"
|
||
echo -e " ${CYAN}ComfyUI${NC} → http://$LOCAL_IP:8188 (chat-integrated image gen)"
|
||
echo -e " ${CYAN}Kiwix${NC} → http://$LOCAL_IP:8181"
|
||
echo -e " ${CYAN}Gitea${NC} → http://$LOCAL_IP:3001"
|
||
echo -e " ${CYAN}RAG${NC} → http://$LOCAL_IP:8001/health"
|
||
echo -e " ${CYAN}MCP SSE${NC} → http://$LOCAL_IP:8002/sse"
|
||
echo -e " ${CYAN}Portainer${NC} → https://$LOCAL_IP:9443"
|
||
echo ""
|
||
echo -e " ${YELLOW}Add MCP to Claude Code:${NC}"
|
||
echo " claude mcp add local http://$LOCAL_IP:8002/sse"
|
||
echo ""
|
||
echo -e " ${YELLOW}Tokens:${NC} $BASE/.env"
|
||
echo -e " ${YELLOW}PDFs:${NC} $BASE/papers/"
|
||
echo -e " ${YELLOW}Workspace:${NC} $BASE/workspace/"
|
||
echo -e " ${YELLOW}ZIMs:${NC} ./kiwix_download.sh"
|
||
echo ""
|
||
echo -e " ${YELLOW}Aider CLI:${NC} cd your-repo && $BASE/aider.sh <files>"
|
||
echo " (or open browser UI above — both use your local code model)"
|
||
echo ""
|
||
echo -e " ${YELLOW}Recommended Open WebUI Functions${NC} (install from Admin → Functions → ＋):"
|
||
echo " Context tracker: https://openwebui.com/f/centrisic/context_tracker"
|
||
echo " → Shows tokens used vs available, progress bar, context % remaining"
|
||
echo " Context compaction: https://openwebui.com/f/projectmoon/checkpoint_summarization_filter"
|
||
echo " → Auto-summarizes old messages when context fills up (like Claude)"
|
||
echo ""
|
||
echo -e " ${YELLOW}Save Claude usage:${NC} use local models for boilerplate, docs,"
|
||
echo " simple fixes. Use Claude for hard bugs,"
|
||
echo " multi-file refactoring, architecture decisions."
|
||
echo ""
|