Claude
cccf3afc4f
- WEBUI_AUTH=true: require login (auth was disabled by default) - ENABLE_TOOL_SERVERS=true: unlocks the MCP/OpenAPI tool server UI https://claude.ai/code/session_012gDnantBmFTWZGCiKyjazx
838 lines
32 KiB
Bash
Executable File
838 lines
32 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; [[ "$a" == "--no-pull" ]] && NO_PULL=true; done
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BASE="$HOME/docker/ai-stack"
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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", $1/1024}' 2>/dev/null || echo "0")
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if [[ "$VRAM_GB" -ge 14 ]]; then
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CHAT_MODEL="qwen2.5:14b"; CODE_MODEL="qwen2.5-coder:14b"
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CTX=32768; TIER="16GB — 14B models + 32k context"
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elif [[ "$VRAM_GB" -ge 8 ]]; then
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CHAT_MODEL="qwen2.5:14b"; CODE_MODEL="qwen2.5-coder:7b"
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CTX=16384; TIER="8-16GB — 14B chat, 7B code, 16k context"
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elif [[ "$VRAM_GB" -ge 4 ]]; then
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CHAT_MODEL="qwen2.5:7b"; CODE_MODEL="qwen2.5-coder:7b"
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CTX=8192; TIER="6GB — 7B models, 8k context"
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else
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CHAT_MODEL="qwen2.5:7b"; CODE_MODEL="qwen2.5-coder:7b"
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CTX=4096; TIER="CPU-only — 7B models, 4k context"
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fi
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EMBED_MODEL="nomic-embed-text"
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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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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 searxng invokeai-data invokeai-outputs 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"
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# =============================================================================
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write_if_new "$BASE/mcp_server.py" << 'PY'
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import json, os, re, subprocess
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from pathlib import Path
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import httpx
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from mcp.server.fastmcp import FastMCP
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WORKSPACE = Path(os.getenv("WORKSPACE_DIR", "/workspace"))
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REPOS_DIR = Path(os.getenv("REPOS_DIR", "/repos"))
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GITEA_URL = os.getenv("GITEA_URL", "http://gitea:3000")
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GITEA_TOKEN = os.getenv("GITEA_TOKEN", "")
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GITHUB_TOKEN = os.getenv("GITHUB_TOKEN","")
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RAG_URL = os.getenv("RAG_URL", "http://rag-server:8001")
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mcp = FastMCP("local-dev-tools")
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@mcp.tool()
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def bash(command: str, cwd: str = "") -> str:
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"""Run a shell command. Default cwd is /workspace."""
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work = Path(cwd) if cwd else WORKSPACE
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work.mkdir(parents=True, exist_ok=True)
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try:
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r = subprocess.run(command, shell=True, cwd=work, timeout=120, capture_output=True, text=True)
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out = r.stdout + (f"\n[stderr]\n{r.stderr}" if r.stderr else "")
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if r.returncode != 0: out += f"\n[exit {r.returncode}]"
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return out or "(no output)"
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except subprocess.TimeoutExpired: return "[timeout]"
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except Exception as e: return f"[error] {e}"
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@mcp.tool()
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def read_file(path: str) -> str:
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"""Read a file. Absolute or relative to /workspace."""
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p = Path(path) if Path(path).is_absolute() else WORKSPACE / path
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if not p.exists(): return f"[not found] {p}"
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if p.stat().st_size > 500_000: return f"[too large: {p.stat().st_size//1024}KB]"
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return p.read_text(encoding="utf-8", errors="replace")
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@mcp.tool()
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def write_file(path: str, content: str) -> str:
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"""Write content to a file. Relative to /workspace."""
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p = Path(path) if Path(path).is_absolute() else WORKSPACE / path
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p.parent.mkdir(parents=True, exist_ok=True)
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p.write_text(content, encoding="utf-8")
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return f"Wrote {len(content)} chars to {p}"
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@mcp.tool()
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def list_files(path: str = "", pattern: str = "**/*") -> str:
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"""List files matching a glob pattern."""
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base = Path(path) if path else WORKSPACE
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if not base.exists(): return f"[not found] {base}"
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files = sorted(str(f.relative_to(base)) for f in base.glob(pattern) if f.is_file())
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return "\n".join(files[:500]) or "(empty)"
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@mcp.tool()
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def search_code(query: str, path: str = "", glob: str = "", case_sensitive: bool = False) -> str:
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"""Search file contents with ripgrep."""
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base = path or str(WORKSPACE)
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cmd = ["rg", "--line-number", "--no-heading"]
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if not case_sensitive: cmd.append("-i")
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if glob: cmd += ["-g", glob]
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cmd += [query, base]
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try:
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r = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
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lines = r.stdout.strip().splitlines()
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if len(lines) > 200: lines = lines[:200] + [f"...({len(r.stdout.splitlines())-200} more)"]
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return "\n".join(lines) or "(no matches)"
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except FileNotFoundError:
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r = subprocess.run(["grep","-rn",query,base], capture_output=True, text=True, timeout=30)
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return r.stdout[:8000] or "(no matches)"
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@mcp.tool()
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def fetch_url(url: str, extract_text: bool = True) -> str:
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"""Fetch the content of a URL."""
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try:
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r = httpx.get(url, timeout=30, follow_redirects=True, headers={"User-Agent":"Mozilla/5.0"})
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content = r.text
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if extract_text:
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content = re.sub(r'<script[^>]*>.*?</script>', '', content, flags=re.DOTALL)
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content = re.sub(r'<style[^>]*>.*?</style>', '', content, flags=re.DOTALL)
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content = re.sub(r'<[^>]+>', '', content)
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content = re.sub(r'\n{3,}', '\n\n', content).strip()
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return content[:20000]
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except Exception as e: return f"[error] {e}"
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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.get_asgi_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 > "$BASE/mcp_requirements.txt" << 'REQ'
|
|
mcp[cli]
|
|
fastapi
|
|
uvicorn[standard]
|
|
httpx
|
|
REQ
|
|
ok "requirements.txt + mcp_requirements.txt"
|
|
|
|
# =============================================================================
|
|
section "SearXNG Config"
|
|
# =============================================================================
|
|
write_if_new "$BASE/searxng/settings.yml" << SEARXNG
|
|
use_default_settings: true
|
|
general:
|
|
instance_name: "Local Search"
|
|
server:
|
|
secret_key: "$(openssl rand -hex 32)"
|
|
limiter: false
|
|
search:
|
|
safe_search: 0
|
|
default_lang: "en"
|
|
formats: [html, json]
|
|
SEARXNG
|
|
|
|
# =============================================================================
|
|
section ".env (tokens — never overwritten)"
|
|
# =============================================================================
|
|
if [[ ! -f "$BASE/.env" ]]; then
|
|
cat > "$BASE/.env" << ENV
|
|
# Local AI Stack — edit to add your API tokens
|
|
GITEA_TOKEN=your-gitea-token-here
|
|
GITHUB_TOKEN=your-github-token-here
|
|
ENV
|
|
ok "Created .env"
|
|
else
|
|
info "Kept .env"
|
|
fi
|
|
|
|
# =============================================================================
|
|
section "Docker Compose"
|
|
# =============================================================================
|
|
cat > "$BASE/docker-compose.yml" << COMPOSE
|
|
# Local AI Stack — 2026-03-20
|
|
# GPU: OLLAMA_NUM_GPU=999 uses all available VRAM automatically (V100/RTX/any)
|
|
# Context: OLLAMA_NUM_CTX= set by detected VRAM (GB)
|
|
|
|
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=
|
|
- OLLAMA_KEEP_ALIVE=24h
|
|
- OLLAMA_MAX_LOADED_MODELS=1
|
|
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_RAG_WEB_SEARCH=true
|
|
- RAG_WEB_SEARCH_ENGINE=searxng
|
|
- SEARXNG_QUERY_URL=http://searxng:8080/search?q=<query>&format=json
|
|
- WEBUI_AUTH=true
|
|
- ENABLE_TOOL_SERVERS=true
|
|
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=
|
|
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
|
|
working_dir: /app
|
|
env_file: $BASE/.env
|
|
environment:
|
|
- WORKSPACE_DIR=/workspace
|
|
- REPOS_DIR=/repos
|
|
- GITEA_URL=http://gitea:3000
|
|
- RAG_URL=http://rag-server:8001
|
|
command: >
|
|
bash -c "apt-get update -qq && apt-get install -y --no-install-recommends git ripgrep &&
|
|
pip install --no-cache-dir -r mcp_requirements.txt &&
|
|
python mcp_server.py"
|
|
depends_on: [rag-server]
|
|
|
|
searxng:
|
|
image: searxng/searxng:latest
|
|
container_name: searxng
|
|
restart: unless-stopped
|
|
ports: ["0.0.0.0:8888:8080"]
|
|
volumes: [$BASE/searxng:/etc/searxng]
|
|
cap_drop: [ALL]
|
|
cap_add: [CHOWN, SETGID, SETUID]
|
|
|
|
kiwix:
|
|
image: ghcr.io/kiwix/kiwix-serve:latest
|
|
container_name: kiwix
|
|
restart: unless-stopped
|
|
ports: ["0.0.0.0:8181:8080"]
|
|
volumes: [$BASE/kiwix:/data]
|
|
command: "*.zim"
|
|
|
|
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=float16
|
|
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
|
|
- $BASE/portainer-data:/data
|
|
|
|
volumes:
|
|
ollama-models:
|
|
open-webui-data:
|
|
invokeai-models:
|
|
COMPOSE
|
|
ok "docker-compose.yml"
|
|
|
|
# =============================================================================
|
|
section "Firewall"
|
|
# =============================================================================
|
|
if command -v ufw &>/dev/null && [[ ! -f "$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="${LAN}/24"
|
|
for pc in "3000:Open WebUI" "11434:Ollama" "8001:RAG" "8002:MCP" \
|
|
"8000:ChromaDB" "8888:SearXNG" "8181:Kiwix" \
|
|
"3001:Gitea" "2222:Gitea SSH" "9090:InvokeAI" \
|
|
"9000:Portainer" "9443:Portainer S"; do
|
|
sudo ufw allow from "$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://$LOCAL_IP:3000"
|
|
echo " InvokeAI → http://$LOCAL_IP:9090"
|
|
echo " SearXNG → http://$LOCAL_IP:8888"
|
|
echo " Kiwix → http://$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 $CODE_MODEL
|
|
|
|
echo "Reasoning model (DeepSeek-R1 14B — optional)..."
|
|
read -rp "Pull DeepSeek-R1:14b for planning/reasoning? [y/N]: " DR
|
|
[[ "\${DR,,}" == "y" ]] && docker exec ollama ollama pull deepseek-r1:14b
|
|
|
|
echo "" && docker exec ollama ollama list
|
|
PULLSH
|
|
chmod +x "$BASE/pull-models.sh"
|
|
ok "start.sh stop.sh status.sh pull-models.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 "$BASE/pull-models.sh"
|
|
fi
|
|
|
|
# =============================================================================
|
|
echo ""
|
|
echo -e "${GREEN}${BOLD}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
|
|
echo -e "${GREEN}${BOLD} Done! GPU: ${VRAM_GB}GB → $TIER${NC}"
|
|
echo -e "${GREEN}${BOLD}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
|
|
echo ""
|
|
echo -e " ${CYAN}Open WebUI${NC} → http://$LOCAL_IP:3000"
|
|
echo -e " ${CYAN}InvokeAI${NC} → http://$LOCAL_IP:9090"
|
|
echo -e " ${CYAN}SearXNG${NC} → http://$LOCAL_IP:8888"
|
|
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 " ${YELLOW}Save Claude usage:${NC} use local 14B for boilerplate, docs,"
|
|
echo " simple fixes. Use Claude Sonnet 4.6 for hard bugs,"
|
|
echo " multi-file refactoring, architecture decisions."
|
|
echo ""
|